text:
C. A. L. Totten
|This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. (June 2009)
Charles Adelle Lewis Totten (February 3, 1851 – April 12, 1908) was an American military officer, a professor of military tactics, a prolific writer, and an influential early advocate of British Israelism.
Charles Totten was born in New London, Connecticut into a military family. His father, James Totten was a 1st lieutenant in the Army and would become a Brigadier-General in the Missouri Militia during the American Civil War. He was not directly related to Brevet Major General Joseph Gilbert Totten, who was Chief of the United States Army Corps of Engineers from 1838 to 1864.
Totten was appointed to the United States Military Academy at West Point on September 1, 1869. A year later, his father was dismissed from the Army for misconduct.
Military career[edit]
Totten graduated from West Point (where he had been an honor student) in June 1873. He was commissioned as a second lieutenant in the 4th Artillery Regiment. He was promoted to first lieutenant the next year. He would not be promoted again, however, due to slow promotions in the post-Civil War Army.
He taught military science and tactics at Massachusetts Agricultural College, (now known as the University of Massachusetts Amherst) from 1875 to 1878. In this assignment he introduced fencing as a collegiate sport.
Charles Totten and W. R. Livermore are variously credited with being the first to bring the practice of war gaming from Germany to the United States. Totten's book Stragegos, one of the first modern wargaming systems in the United States, was published in 1880.
In 1881 Totten participated in the Chiricahua Campaign against the Apaches in Arizona. For this service, Totten was entitled to receive the Indian Campaign Medal when it was established in 1907.
He was stationed at Fort Adams in Newport, Rhode Island from November 9, 1882 to August 1, 1883. He then served as Professor of Military Science and Tactics, Cathedral School of St. Paul in New York from August 4, 1883 until April 21, 1886. A strong opponent of the Metric System, he patented a system of weights and measures in 1884.
He was again assigned to Fort Adams, as well as serving as an advisor at Rhode Island Militia encampments, from May 30, 1886 until October 1, 1889. His last assignment in the Army was as Professor of Military Science and Tactics at Yale University from August 1, 1889 until 1892.
Writing and Publishing[edit]
After a leave of absence, Totten resigned his commission in August 1893 and settled in Milford, Connecticut with his office in New Haven. He devoted most of his remaining life to writing, chiefly on biblical chronology, biblical prophecy, the Great Pyramid, British Israelism, the symbolism of the Great Seal of the United States and other esoteric subjects.
He was a prolific author, writing over 180 books and articles, including a massive 26 volume series entitled "Our Race" defending British Israelism, and his writings continue to exert influence in some Christian Zionist circles. Totten's works were read and embraced by Mary Baker Eddy, the founder of the Christian Science Church. British Israelism was later embraced by Herbert W. Armstrong, founder of the Worldwide Church of God and the white supremacist Christian Identity movement.
See also[edit]
Cullum's Register of Graduates of the United States Military Academy Vols. III, IV and V.
Some works[edit]
- Laws of Athletics and General Rules
- The gospel of history;: An interwoven harmony of Matthew, Mark, Luke, and John, with their collaterals, jointly and severally re-translated and consolidated word-by-word into one composite truth
- Joshua's Long Day and the Dial of Ahaz, a Scientific Vindication and "a Midnight Cry"(1890)
- The seal of history : our inheritance in the great seal of "Manasseh," the United States of America : its history and heraldry; and its signification unto "the great people" thus sealed (1897)
- An Important Question in Metrology: Based Upon Recent and Original Discoveries: A Challenge to "The Metric System." and an Earnest Word with the English-Speaking Peoples on Their Ancient Weights and Measures (1884)
- The Romance of History: Lost Israel Found; Or, Jeshurun's Pilgrimage Towards Ammi, from Lo-Ammi
- The Riddle of History, a Chronological Itinerary Through the Period of the Judges: Together with Other Biblico-Literary Excursus (1892)
External links[edit]
- Totten's Legacy: UMass Fencing from 1875 to the Present
- Cullum's Register of Graduates of the USMA
- Joshua's Long Day and the NASA Computers (traces a popular urban legend based in part on one of Totten's books)


text:
The Science Behind Choosing a Saddle Please join us Dec. 3rd at 7PM for a class on saddles for road and triathlon bikes. This free class is for those wanting to learn how to choose the correct saddle for their road or triathlon bike. You will learn: the interaction between body and saddle […]
Innersport Wins THREE Best of Competitor Running Awards in the Pacific West Coast Region!
Best Sports Medicine Doctor (Dr. Jessica Greaux)
Best Sports Rehab Clinic
Best Sports Massage
Innersport Chiropractic's vision is to create a holistic approach to healthy, active living whether you are an athlete or a weekend warrior. Our objective is to help patients remove as many physical barriers in life. The care and treatment of every patient is designed to improve overall physical and mental well-being.
Dr. Jessica Greaux was chosen Best Chiropractor 2005 and Best Doctor 2012 by the East Bay Express and Best Sports Medicine Doctor Competitor Running Pacific West Coast 2013.

subset: commoncrawl


text:
Sign Up ForFREE Pattern Info!
Tell A Friend!
Out of Stock
With Our "Find This For Me" Service we notify you via email once we have stock!
Piece Code: K1B
Piece Name: Blunt Hollow Knife
Size: 8 1/2 in
© 2015 Replacements, Ltd. All rights reserved
Piece Code: F2PP
Piece Name: Pearl Handle Fork
Size: 7 1/2 in


text:
David Toledo recently admitted in court that he tried to damage another parked car in the Northeast.
The Mayfair tire slasher was screwing around with some parked cars in his new neighborhood, so authorities nailed him with more criminal charges, according to Philadelphia police.
At the end of the day, Mayfair residents will have spent more time waiting for justice in the "tire slasher" case than it will take the convicted tire slasher to serve his sentence.
A Northeast-based nonprofit can provide financial help to crime victims, the organization's executive director last week told 15th Police District Advisory Council members.
A Common Pleas Court jury decided that David Toledo did, indeed, slash or puncture a bunch of tires on his Mayfair neighbors' cars.
National Night Out to relaunch on Aug. 6.
As 2012 comes to a close, a look back shows that the Northeast had a lot to celebrate and just as much to mourn.
— The man charged with dozens of tire slashings is no stranger to the area.
David Toledo admitted in court that he tried to damage another parked car in the Northeast neighborhood where he moved to flee his former neighbors' ire.
David Toledo on Friday, two days after a couple of SEPTA bus drivers reported that they saw him plant nails and screws under the tire of a car that was parked on the 1600 block of Creston St. in Oxford Circle, said Lt. Dennis Rosenbaum of Northeast Detectives.


text:
Join Date: Mar 2012
Posts: 2
Mentioned: 0 Post(s)
Tagged: 0 Thread(s)
Quoted: 0 Post(s)
|Thread Tools
Hi, I know this is an old thread but wondering if you can tell me where to buy mag07/ I am in australia.
I'm so glad this post got bumped! We're having the same issue with 19mo DS - he had a traumatic poo (was given a brownie and then didn't poop for 7 days despite our efforts to get him moving (we are GF and both times he's had gluten it has stopped him up horribly), then when he did go it was painful) and has been withholding since. It's so stressful and maddening to see him walk around on his toes, clench, do anything in his power to keep it in. Flax oil does help, but he still mostly nurses and doesn't eat a ton of solid food, so it's almost impossible to get him to take 2 tsp a day.
Off to research!
|Thread Tools
Show Printable Version Show Printable Version
Email this Page Email this Page
|Posting Rules
You may not post new threads
You may not post replies
You may not post attachments
You may not edit your posts
BB code is On
Smilies are On
[IMG] code is On
HTML code is Off
Trackbacks are On
Pingbacks are On
Refbacks are Off


text:
Yellowstone Grand Tetons National Park Photo Tours Workshops Fall
Join us at Yellowstone and Grand Tetons National Parks for an Instructional Photo Tours and Photo Workshops. This is an adventure of a life with world famous landscape and wildlife photography.
Best of Yellowstone and Grand Tetons National Park
Photo Tours and Workshops Fall
September 12-21, 2015 (3 Spaces Left)
World famous Yellowstone and Grand Teton National Park are some of the most exciting places in the World to photograph landscapes and wildlife. This a must see on any nature photographer and wildlife lovers list. Join us as we explore this park and the surrounding areas.
The Greater Yellowstone ecosystem is a mountainous region bisected by the continental divide. Several peaks like Grand Teton are over 13000 feet. Powerful geological forces created this landscape.
Fall is a great time to visit as the huge summer crowds and traffic jams are gone. Wildlife is more abundant. It is the time of year when many other professional photographers and videographers visit the park annually.
Yellowstone has one the great concentration of thermal activity. It is home to the famous Old Faithful. It also has beautiful rivers, geysers, springs, mudpots, waterfalls, canyons, ponds, and mountains. Vibrant leaves of aspen and cottonwood show off their fall color. We will visit a gorge carved by time and water that some people think rivals the Grand Canyon.
Yellowstone is one of the premiere wildlife viewing areas in the US. Creatures great and small await us at almost every corner. We will be visiting the parks during the rut for elk, moose, and pronghorn. This is a great time to photograph great wildlife behavior like courtship, territorial displays, sparing. The elk will bugle, spar, and herd cows this time of year. Other species that are commonly photographed as moose, bison, big horned sheep, mule deer, coyotes, wolves, and bears. The mammals will have their prime winter fur since they are getting ready for the harsh winter ahead. Bird sighting can be very exciting as we will look for trumpeter swans and great grey owls or whatever else we discover.
Our itinerary will be planned around recent wildlife activity, lighting, and weather conditions. We need to take advantage of the unique opportunities. This tour is pretty intense. Our goal is to see the beauty of the parks. The days are long, typically starting before dawn and ending after sunset. Come and enjoy the autumn tranquility of this great ecosystem.
Sept 12--21, 2015
Yellowstone and Grand Teton National Park
Tour begins and ends in Jackson Hole, Wyoming. We will have an optional pickup and dropoff people at airport.
Per person includes hotel (double occupancy), Park admission and transportation.
For single occupancy it is an additional $300.
Deposit: $500
Balance Due: July 1st
Gallery: Check on our Yellowstone Gallery
Click on the Icon on the top right hand side to see Thumbnails
Payment can be made by check to Slonina Photography. We also accept Visa, Mastercard, Discover and American Express. If you prefer to use credit card you can call our office at (508) 736-1167 or use the paypal shopping carts on our website.
Each day is paced to maximize the best opportunities and time of day.
Day 1: We will meet pick people up at the Jackson, WY (JAC) Airport at 1 pm. We will drive to hotel and check in. We will have a introductory meeting go over safety issues, recent wildlife sightings and behavior. We also will discuss plans, goals and objectives for the upcoming week. We will start our first photo shoot and stay out till sunset at Grand Teton National Park.
Hotel: Painted Buffalo Inn (Jackson Hole Wyoming). This hotel is located in downtown Jackson. It is walking distance to many famous shops, restaurants, and galleries.
Day 2 : Sunrise Grand Teton then drive to Yellowstone with many stops along the way. We will check in to the hotel and have a afternoon until sunset shoot.
Hotel: West Yellowstone Area
Day 3 : We will meet and be on location before the sun comes up to see the beautiful morning light and the peak wildlife activity. We will photograph the geysers, wildlife, waterfalls, the Grand Canyon of Yellowstone etc.
Day 4: Sunrise Shoot check out of hotel, Drive to next hotel with many photo stops along the way.
Check in to hotel and afternoon Shoot until Sunset
Hotel: Absaroka Lodge Gardiner MT
Day 5 and 6: Sunrise to Sunset Yellowstone National Park. Explore the Lamar Valley and many other areas (look for landscapes, elk, pronghorn, bears and wolves etc)
Day 7: Morning shoot. Check out of the hotel and go back to the Grand Tetons. Check into to hotel. Afternoon photo shoot till sunset.
Day 8: Photograph Grand Tetons
Day 9: Sunrise Grand Tetons return to hotel before checkout time. I will be able to bring people to airport for afternoon flights or you could take a taxi from hotel.
Click Here to see Testimonials
Group Size:
Our groups are small in size this one is limited to 6 people. We will work with each participant to provide complete, informative, helpful information. This also ensures that no question is left unanswered.
Fitness Level:
This trip will involve some short flat walks. None of the hikes are long most of them within site of my tour van. So you do not have to be in great shape for this tour.
What's Included:
The hotels are included in this tour (6 nights stay). Double Occupancy
For Single Occupancy it is an additional $300.
Also included are gates fees and permits.
We also will be providing transportation including optional pickup and dropoff at Jackson Airport.
Not Included:
Food and drinks will not be included. We will visit various restaurants in and out of the park. Each participant will be responsible for their own food tab. We will provide snacks and drinks.
Sorry air travel is NOT included.
Getting There:
Jackson, Wyoming Airport (JAC)
Pickup and Dropoff at Airport
Cancellation Policy:
If you cancel before 120 days of departure you will receive a full refund.
Within 120 days you will lose deposit.
Within 60 days you will lose the full amount. If we are able to fill the spot you will receive a full refund.
About Me:
John Slonina is a professional nature photographer who has been photographing for over 25 years. He estimates he has taken over 500,000 photos. John has a love of natural history as well. You will receive information on photo technique and the natural world. Understanding the natural world not only increases your appreciation of nature but also helps you become a better nature photographer.


text:
Vibrating glove could direct users to items within stores
It looks nothing like the NES Power Glove, but we can dream, right? (shelf: Shutterstock)
Warmer, warmer ... (hand: Shutterstock, Snickers: sudeep1106 | Flickr)
Article Summary
These days, we do our hunting and gathering in supermarkets and department stores. While this is much easier than the challenge faced by our ancestors, it is not without its difficulties. Tracking down that one specific item on our list can prove frustrating when faced with aisle after aisle filled with shelf upon shelf of products. Researchers are eying the use of gloves to make the task a lot simpler but, unlike high tech wearable computing devices like Google's Project Glass, these conceptual gloves use a simpler formula: they emit vibrations to tell you where to go.
« Back to Vibrating glove could direct users to items within stores
Other Images from this Gallery
Related Articles


text:
The /5 phoenix project
View Single Post
Captain Yesterday
Gnarly Adventurer
Joined: Aug 2007
Location: Paso Robles
Oddometer: 152
Left side rod bearings
Right side rod bearings
Rear main
That engine sprocket did not want to come off.
What the hell is up with this myth that BMW's easy to work on yeah right
Taking this engine apart was a total pain in the ass. You need a "special" tool for just about everything, and nothing came apart willingly.
Only question is on the right side rod bearing. It's not smooth enough to where I feel comfortable reinstalling it. So do I replace the the rod bearing and leave the crank alone or do I take the crank to a chop and have them grind it down to match the new road bearing? The crank feels fine to the touch.
The front main looks ok but the back is boarder line as well as the thrust washers bearings.
decision decisions do I replace all the bearings or just the rod bearing decision decisions I guess it's up to how much more money I want to dump into this thing.
Captain Yesterday
View Public Profile
Find More Posts by Captain Yesterday


text:
Change Cursor To Panning Hand?Apr 25, 2012
I tried "PictureBox1.Cursor = Cursors.Hand" but that isn't what I'm wanting... I want the hand that "grabs" when you click. You can find it in picture editors and such. The "Cursors.Hand" just points.I'm sorry if I misspelled something or my grammar is wrong, I've never done well in those subjects.View 5 Replies


text:
Pronunciation: sentetik
Added on: 2011-11-09 Listened: 8 timesin: Add categories to sentetik
Pronunciation in Turkish [tr]
- Pronunciation by muhammetmustafa (Male from Turkey)
- Pronunciation by Alcarinque (Female from Turkey)
0 votes
Could you do it better? different accent? Pronounce sentetik in Turkish
Accents & languages on maps
- Random word: Türkiye vizesi
Not satisfied?
Modify word:
· Add languages
· Add categories
Report word:
Is there anything wrong with this word?
Please report it here.


text:
The Special Additional Sessions Court (Marad cases) at Eranhipalam here trying the murder of Revolutionary Marxist Party (RMP) leader T.P. Chandrasekharan will pronounce its verdict on Wednesday.
R. Narayana Pisharadi, judge, is expected to pronounce the verdict at 11 a.m. The case relates to a section of Communist Party of India (Marxist) [CPI(M)] leaders in Kozhikode and Kannur districts allegedly hatching a conspiracy and hiring a seven-member gang to kill Chandrasekharan, who had walked out of the party and floated a dissident outfit at Onchiyam. The growing clout of the parallel party in rural areas had reportedly perturbed the CPI (M) leadership. Chandrasekharan was killed at Vallikad at 10.15 p.m. on May 4, 2012.
Unprecedented security has been put in place in the city and in the rural areas of the district. Prohibitory orders have been issued under Section 78 (regulation to prevent violence) and Section 79 (regulation of public assemblies) of the Kerala Police Act for five days.


text:
About Schools in Austin, AR
Contact a Coldwell Banker® real estate agent to learn more about the schools and school districts in Austin, AR.FIND AN AGENT
Austin, Arkansas is located in Lonoke County. Nearby cities and towns include Ward, Cabot, Beebe, McRae, Jacksonville, Vilonia, Garner, Lonoke, Gibson, Sherwood, Mount Vernon, Enola, McAlmont, Holland, Higginson, Carlisle, Griffithville, North Little Rock, Searcy, Scott, Rose Bud, Kensett, Mayflower, and College Station. Austin is a rural community with a population of 2,141. The median household income is $56,893. In Austin, 64% of residents are married, and families with children reside in 42% of the households. Half the population of Austin commutes 28 minutes or less to work, with 51% of residents holding white collar jobs and 49% residents holding blue collar jobs. The median age of homes in Austin, AR is 17 years, with 79% of those homes owned, 16% rented, and 5% not occupied. In the previous year, 88 Austin properties were sold. The median sale price of a home in Austin in the previous year was $141,003.
Contact a Coldwell Banker® real estate agent to learn more about the schools and school districts in Austin, AR.FIND AN AGENT
See if your personality and goals are right for real estate.SEE ALL REAL ESTATE CAREERS
Community information and market data Powered by Onboard Informatics. Copyright ©2015 Onboard Informatics. Information is deemed reliable but not guaranteed.
This information is provided for general informational purposes only and should not be relied on in making any home-buying decisions. School information does not guarantee enrollment. Contact a local real estate professional or the school district(s) for current information on schools. This information is not intended for use in determining a person's eligibility to attend a school or to use or benefit from other city, town or local services.


text:
Traditional country music singer Joe Nichols, of Nashville, will co-headline with Sara Evans Saturday evening at the Missouri State Fair and he said he's at the top of his game with his new release "Yeah."
Nichols' song "Yeah," written by Ashley Gorley and Bryan Simpson, from his album, "Crickets," has held the No. 1 spot for three consecutive weeks on the Billboard Country Airplay chart — this makes him only the second country music artist to have a three-week No. 1 run after Luke Bryan's "Play it Again."
While on a recent drive across Nebraska he talked to the Democrat about his newest hit and his career by phone.
"It's a big song, three weeks at No. 1," he said. "That's only happened one other time in my career. That makes me feel pretty good about it. It connects with the fans, they love it — they love it at the shows and they are buying records, and buying singles. So that's how you can tell if they're a hit. I'm proud of the song because it's been with me the longest."
He said he started the album "Crickets" with his own money, beginning with four singles in the studio.
"It was my own bank account, so we set out to make another two songs to get a label and kind of jump in," Nichols said. "And that's what we did."
Nichols is represented by RED Bow Records, a newly created entity of BBR Music Group and Sony RED.
He said he feels with the the single "Yeah," and the "Crickets" album he's at his highest career point.
"Think we're at it right now," Nichols said. "I think this is probably the biggest moment we've ever had. We've had two No. 1's since 2002 … 'The Impossible,' and 'Brokenheartsville.' And along the way we've never had the sustained successes that we are having right now, the momentum that we have right now. I think with the catalog that we have and the existing album, I think we have a great future over the next few years. But certainly this album is one of my best. My producers on this album are great, the songs on this album are outstanding, and I think we've got eight or nine singles on this album."
A native of Rogers, Ark., Nichols has always had singing in his blood — he laughed and noted that he was a disc jockey while in high school and had at one time wanted to be a rock singer.
"In high school I did the midnight to 6 a.m. shift," he said. "From the radio station I'd go to school all tired-eyed. Mother didn't like that much."
It soon became evident that his rich voice had a natural, pure county vibe and the rock n' roll genre wouldn't be a career choice.
"I tried to be in a rock band for about five minutes when I was in high school," he said. "I was probably maybe 15 years old and there was a rock band that was a pretty hot deal in high school and they lost their singer. They wanted to audition some singers and they heard that I sang. So I went and auditioned for them — it was one of the deals where we all looked at each other and said, 'Yeah this ain't for me.' I sang just way too country to be a rock singer. And they were like, 'Yeah you're a country dude.' That's how I knew I couldn't do anything else other than country."
During his career Nichols, who began singing professionally in March 2002, said he has written some of his own music, "What's a Guy Gotta Do," and "Cool to be a Fool."
"And several album cuts along the way," he added.
Nichols said he looks to traditional country music singers Merle Haggard, George Strait, George Jones and Hank Williams Jr. as his inspiration. With the style of these country greats in mind Nichols' music, by his own right, has garnered him four Grammy nominations.
"I'm very proud to have been nominated for Grammys," he said. "That's a big deal. And I'm proud to say the people I lost to were big deals. I think I lost to Johnny Cash, Willie Nelson, Vince Gill and I think the Dixie Chicks.
This summer he been busy touring with Lady Antebellum with the "Take Me Down" tour.
"Yeah, we started with them back in May, I believe," he said. "And I have been enjoying a few weekends here and there with them throughout the summer. I think we'll wrap up with them in late September. They're a great group, they're such a family vibe and such great people. It's a very cool tour to be a part of, it's one of my favorites."
At the end of day, at the end of the tour, when the entertainment is complete, Nichols said it all boils down to his wife and children.
"More than any music I think my purpose is to be a good dad, which I'm trying to be," he said. "I have three (children) a 15-year-old, a 2-year-old and a 2-month-old. Yeah I think that's the most important thing."
For more information on Joe Nichols visit his website at www.joenichols.com.
Joe Nichols and Sara Evans will perform at 7:30 p.m. Saturday at the Pepsi Grandstand at the Missouri State Fair. Tickets cost $35, $30, $25, and $20; for more information visit www.mostatefair.com or call 1-800-422-FAIR ext. 3247.


text:
Memphis Grizzlies v Houston Rockets
James Harden of the Houston Rockets defends against Tayshaun Prince... News PhotoBasketball - NBA Pro,Basketball - Sport,Defending,Gulf Coast States,Houston - Texas,Houston Rockets,James Harden,Memphis Grizzlies,NBA,Sport,Tayshaun Prince,Texas,Toyota Center - Houston,USA,VerticalPhotographer Scott HalleranCollection: Getty Images Sport 2013 Getty ImagesHOUSTON, TX - APRIL 12: James Harden #13 of the Houston Rockets defends against Tayshaun Prince #21 of the Memphis Grizzlies at the Toyota Center on April 12, 2013 in Houston, Texas. NOTE TO USER: User expressly acknowledges and agrees that, by downloading and or using this photograph, User is consenting to the terms and conditions of the Getty Images License Agreement. (Photo by Scott Halleran/Getty Images)


text:
Parse (geometry Data Type)
Returns a geometry instance from an Open Geospatial Consortium (OGC) Well-Known Text (WKT) representation. Parse() is equivalent to STGeomFromText(), with the exception that it assumes a spatial reference ID (SRID) of 0 as a parameter. The input may carry optional Z (elevation) and M (measure) values.
Applies to: SQL Server (SQL Server 2008 through current version), Azure SQL Database.
The following example uses Parse() to create a geometry instance.
DECLARE @g geometry; SET @g = geometry::Parse('LINESTRING (100 100, 20 180, 180 180)'); SELECT @g.ToString();
Other Resources


text:
In the Wild West, we follow the comings and goings of a cocky cowboy who falls in love with a Diva.
Paul Jaulmes
Boris Croisé
Guillaume Cunis
Mael Oudin
contact us : email@example.com
For any informations about festivals submissions or other kind of diffusions, please contact our distribution label "Premium-films" at firstname.lastname@example.com
Loading more stuff…
Hmm…it looks like things are taking a while to load. Try again?


text:
May 24, 2015
A few years can make a life-saving difference for girls who give birth in their teens.
That's according to a United Nations survey showing that girls who give birth before the age of 15 are five times more likely to die in childbirth, compared to women who gave birth in their 20s.
The director of UNICEF says 70,000 women between the ages of 15 and 19 die in childbirth or from pregnancy complications every year. The U.N.'s annual children's survey paints a bleak picture of the many health and lifestyle risks of teenage pregnancy. Teen pregnancy is especially common in developing countries.
The study also finds that teen births risk more than the mother's life. If a teen give birth under the age of 18, her baby's risk of dying in its first year is 60 percent greater.
Click Here to access the online Public Inspection File
Viewers with disabilities can get assistance accessing this station's FCC Public Inspection File by contacting the station with the information listed below. Questions or concerns relating to the accessibility of the FCC's online public file system should be directed to the FCC at 888-225-5322, 888-835-5322 (TTY), or firstname.lastname@example.com.


text:
Loading stream...
Now Playing
Podcasts & RSS Feeds
|All Content
|View all podcasts & RSS feeds
Connect with Us
Most Active Stories
SDPB Voices
SDPB Radio News Director
SDPB Morning Edition Host
SDPB News Producer
SDPB Freelance Reporter/Producer
SDPB All Things Considered Host
SDPB Freelance Reporter/Producer
SDPB Dakota Midday Host
SDPB News Reporter
2:48 pm
Tue June 10, 2014
Pilot Study Seeks Ways To Predict Best Cancer Treatments
By Jackie Hendry
Sanford Health has partnered with Chronix Biomedical in a new cancer study. The basis of the study is the ability to map out whole genomes of cancer cells. The big question: can this ability be used to predict how cancer patients will respond to different therapies? Dr. John Lee of Sanford Health joined the program to discuss the study, along with Chronix Biomedical CEO Dr. Howard Urnovitz and Professor Ekkehard Schutz, Chief Techonology Officer of Chronix.


text:
From the Hearts & Hands of Artisans
[PRONTO!]℠ sales are collections of handcrafted treasures at least 50% off original prices for a limited time.
Pam's Mixed Metals Cross
Availability: In Stock and Ready To Ship
e Exclusive
Our Texas artisan handcrafts this ornately rustic cross of weathered metal brushed with shimmering metallic paints and embellished with sparkling accents, exclusively for Uno Alla Volta. Approx. 11.5 in. x 7.5 in. Limited edition of 240.
Certificate of Authenticity Included


text:
Devious Maids Reviews
WHAT?! I can't believe Marc Cherry, Eva Longoria, and the writers of Devious Maids are leaving us hanging like that. I loved every bit of the Devious Season 2 finale. There were flashbacks, an unforg More »
Tonight's penultimate installment of Devious Maids Season 2 set Marisol on a quest to find out the truth about Nick's past. All season long this has been the core mystery. Aside from Nick's complicated past, More »
I was shocked when Opal pulled a gun out on Marisol. Luckily, Ethan walked in just in time to see his mom as crazy as ever. Ethan had revealed to Marisol that Opal pushed Dahlia off the bridge and that she was also More »
Just as promised in my exclusive interview with Mark Deklin , Nick revealed a very shocking secret to Marisol while in a drug-induced haze on Devious Maids Season 2 Episode 10 . He said, You have to believe More »
With Opal gone, Marisol dealt with a new hire at home on Devious Maids Season 2 Episode 9 . From the start, I had a feeling something was off about Molly. Unfortunately for Marisol, she shared a little too mu More »
On Devious Maids Season 2 Episode 8 , Nick asked Marisol to marry him before his risky surgery. That's right, Nick was lucky to still be alive after crazy Opal tried to run him over just last week. I was More »
Finally! Devious Maids Season 2 Episode 7 featured some shocking moments that revealed more about the Deerings and their secrets. Marisol was still convinced that Dahlia and Opal carried on an affair. S More »
There was so much scheming on Devious Maids Season 2 Episode 4 - and I absolutely loved it. I watched tonight's installment with my sister. It was her very first dose of the hilarious maids and their crazy s More »
We found out right away on Devious Maids Season 2 Episode 3 that Alejandro is really dead. RIP, man! Concerned about their future once they'd be kicked out of Alejandro's home, Carmen and Odessa were left scr More »
Some some classic Spence and Peri drama kicked off Devious Maids Season 2 Episode 2 . Spence came clean to Peri about about his affair with Rosie. In fact, he even told her he wanted a divorce and that he was More »


text:
NEW YORK ( TheStreet) -- Extra Space Storage (EXR - Get Report) is the second-largest publicly listed self storage REITs in the U.S. With more than 900 facilities (owned and managed) in a portfolio that spans 34 states, Extra Space has an exceptional, well-balanced revenue model.
In addition to its diverse operating platform, Extra Space is best known for its innovative marketing initiatives that have allowed the Salt Lake City-based REIT to leverage technology to drive consumers to rent storage space.
With a strategic focus on quality and innovation, Extra Space has produced exceptional operating results, as evidenced by an extraordinary year-over-year total return of 73.73%.
A few days ago I had an opportunity to sit down with Spencer Kirk, the CEO and president, at The Street's New York headquarters. During the interview, Kirk talked about the company's leading results and innovatively successful operating platform.
Thomas: Spencer, first off I want to congratulate you and your team on some exceptional and remarkable results. Starting with your most recent results, Extra Space led the sector in both revenue gains (up 6.7%) and net operating income (up 10.2%). What is driving the performance?Kirk: One of the strongest factors driving the performance of Extra Space Storage has been our use of technology. Technology is the enabler that is driving the results of well-performing operators and is the bane of other operators who have yet to harness its benefits. The Internet was often hyped up as "the great equalizer," but we've found that it's proven to be "the great divider." We've realized that we have to identify, embrace and embed technology into every aspect of our business. Extra Space has a team of more than 40 people who focus strictly on e-marketing. We're in a need-driven business, so when our customers are looking for convenient, clean, secure and well-priced storage, they turn to the Internet. And when they turn to the Internet, we'll be their top search result. Thomas: Remarkably, Extra Space has racked up 26 quarters of average same-store outperformance. What is the secret to the highly sustainable income performance?


text:
Page 1
Efficient Fourier-Wavelet Super-Resolution
M. Dirk Robinson, Cynthia A. Toth, Joseph Y. Lo, and Sina Farsiu
Abstract—Super-resolution (SR) is the process of combining
multiple aliased low-quality images to produce a high-resolu-
tion high-quality image. Aside from registration and fusion of
low-resolution images, a key process in SR is the restoration and
denoising of the fused images. We present a novel extension of the
combined Fourier-wavelet deconvolution and denoising algorithm
ForWarD to the multiframe SR application. Our method first uses
a fast Fourier-base multiframe image restoration to produce a
sharp, yetnoisy estimateof thehigh-resolution image.Our method
then applies a space-variant nonlinear wavelet thresholding that
addresses the nonstationarity inherent in resolution-enhanced
fused images. We describe a computationally efficient method
for implementing this space-variant processing that leverages the
efficiency of the fast Fourier transform (FFT) to minimize com-
plexity. Finally, we demonstrate the effectiveness of this algorithm
for regular imagery as well as in digital mammography.1
Index Terms—Digital X-ray imaging, multiframe deblurring,
super-resolution (SR), wavelets, denoising.
high-resolution high-quality image at a resolution greater than
the sampling rate of the detector. SR has received much atten-
tion in recent years in the image processing community. We
refer the interested reader to [2]–[4] for a broad review of re-
cent algorithmic development in this area.
Aside from registration and fusion of low-resolution images,
a key process in SR is the restoration and denoising of fused
images. In this paper, we propose an efficient restoration and
denoising method that is a novel multiframe extension of the
Fourier wavelet regularized deconvolution (ForWarD) algo-
rithm [5], which considers the nonstationarity of the multiframe
reconstruction process. The algorithm's efficiency stems from
separating the multiframe deconvolution or restoration step
from the wavelet-based denoising step allowing us to achieve
UPER-RESOLUTION (SR) is the process of combining
multiple aliased low-resolution (LR) images to produce a
Manuscript received January 19, 2009; revised August 31, 2009; accepted
February 03, 2010. Date of publication May 10, 2010; date of current version
September 17, 2010. This work was supported in part by Siemens Healthcare.
The associate editor coordinating the review of this manuscript and approving
it for publication was Dr. Arun Ross.
M. D. Robinson is with Ricoh Innovations, Menlo Park, CA 94025 USA
(e-mail: email@example.com).
C. A. Toth and S. Farsiu are with the Departments of Ophthalmology and
Biomedical Engineering, Duke University, Durham, NC 27710 USA (e-mail:
email@example.com; firstname.lastname@example.com).
J. Y. Lo is with the Departments of Radiology and Biomedical Engineering,
Duke University, Durham, NC 27710 USA (e-mail: firstname.lastname@example.com).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIP.2010.2050107
1Preliminary results of this work were presented at ICIP, October 2008 [1].
nonlinear denoising in a noniterative fashion. Furthermore, we
describe how to efficiently implement the algorithm to address
thecomputational complexity associated with thenonstationary
noise processes inherent to multiframe reconstruction.
In this paper, we also explore an application of this algo-
rithm to digital mammography. While relatively new, digital
mammography is rapidly replacing film-based mammography
for the screening and diagnosis of early carcinomas in women.
Solid-state detectors have demonstrated improved performance
in terms of specificity and sensitivity over film-based imaging
for certain groups of women such as those with dense breast
Unlike film-based mammography, digital mammography
provides the opportunity to directly apply sophisticated digital
processing techniques without the need for a secondary film
scanning process. An ideal digital mammography system ex-
poses the patient to the minimum amount of radiation required
to accomplish the screening task. Digital mammography sys-
tems face the same design tradeoff between image resolution,
signal-to-noise ratio (SNR), and illumination or radiation
exposure level as those found in any digital imaging system.
Shrinking the pixel dimension at the detector increases sam-
pling resolution at the expense of dynamic range and SNR.
While improved SNR and dynamic range may be obtained by
combining multiple images, increasing total radiation beyond
the standard dosage is undesirable for the safety of the patient.
Alternatively, using large detector pixels improves both the dy-
namic range and the SNR of the system at the obvious expense
of resolution. Digital mammography imaging systems typically
choose the highest resolution which supports a minimum
required SNR.
To overcome the said quality tradeoffs, we propose digitally
combining multiple low-dosage images, each containing spa-
tial shifts. This motion may be the result of patient movement,
intentional dithering of the detector, vibration in the imaging
system, or small movement of the imaging gantry. In practice,
the motion contained in the captured images is a combination of
low-resolution (LR) images.
ApplyingSR algorithmsto digital mammographyhas two in-
herent challenges. The captured low-resolution images are typ-
ically of size 10 megapixels and larger. Thus, algorithmic ef-
ficiency is very important due to the sheer size of the recon-
structed images, which could range from 40 to 160 megapixels
depending on the resolution enhancement factor. Also, to min-
imize total radiation exposure, we must use lower than normal
dosages of X-ray exposure for capturing each frame. Therefore,
the captured data has extremely low peak SNR (PSNR). For ex-
ample, Fig. 1 compares a high dosage X-ray image (computed
Page 2
Fig. 1. Mammogram X-ray images from a phantom breast containing a pentagram-shaped set of micro-calcification cluster. (a) High dosage at 226mAs, faintly
showing the nodules (PSNR??? dB). (b) Extremely low-dosage at 11.3 mAS used in the proposed multiframe reconstruction scheme (PSNR?? dB), where the
nodules are almost undetectable. The total dosage of using 15 of these frames (??????? ? ??? mAs) is still less than the high dosage. (c) Restoration combining
the 15 low-dosage frames, clearly demonstrating the pentagram-shaped set of micro-calcification cluster.
that the effectiveness of the two-stage restoration and denoising
algorithm allows us to provide high-resolution, high contrast,
and low noise images at very low radiation dosages. Further-
more, our results suggest improved detection rates of texture re-
sembling small calcification in breast tissue. These results sug-
gest new tradeoffs in designing digital mammogram systems.
In Section II, we describe the forward imaging model and
the problem of SR. In Section III, we describe the multiframe
variant of the ForWarD algorithm we employ to restore and
denoise the reconstructed images. Section IV presents experi-
mental results using this new approach and Section V outlines
some future directions of this work.
dB) with the very low exposure images (computed
dB) used in our multiframe scheme. We demonstrate
In this section, we establish the background required for
the development of our efficient multiframe SR algorithm in
Section III. We introduce our imaging model and since our
method is a combination of the multiframe SR and ForWarD
deblurring algorithms, we also review these two concepts.
Moreover, we review a novel multiframe motion estimation
algorithm [7] that is used to produce the results in Section IV.
We believe that due to some algorithmic similarities, this brief
review of the motion estimation technique facilitates the study
of the method described in Section III.
A. Imaging Model
The captured LR X-ray images are often very large and
may contain complicated relative motions due to patient mo-
tion. However, following several other popular SR methods
[8]–[10], we consider the translational (or pure rotational)
motion models. To better justify and extend the application
of this model, in [11], we introduced a novel joint motion
estimation and SR approach in a tile-based fashion. That is,
instead of considering a global translational motion model, we
assume that each LR image is made of a set of small sized
2In this work, the PSNR was computed numerically as ????
?????. In experiments on real images, ? is the grayscale difference
between the minimum and maximum signal regions and ? is the noise standard
deviation estimated from flat regions. In simulated experiments, ? is the RMSE
error between the estimated and ground truth image.
tiles (blocks). These tiles move independently in a translational
(or pure rotational) model. When using tile-based processing,
the relative shifts between data sets is better approximated by
the translational (or rotational) motion models. Furthermore,
applying the algorithm to small tiles reduces the memory
requirements of the multiframe reconstruction algorithm. The
motion estimation process involves identifying image tiles
from different LR images corresponding to a particular region
of interest. In [11], we introduced a joint estimation technique,
in which matching blocks of different LR frames are optimally
detectedandregisteredina multiframejointestimationprocess.
To simplify the notations, without the loss of generality, all
formulas used in this paper correspond to the reconstruction of
a single HR tile (a full image is reconstructed by stitching a set
of such HR tiles).
image tiles by the vector
. These noisy LR input image tiles
high-resolution image tile denoted by . The forward model re-
tile is given by
in which the vector
without loss of generality) samples of the captured image
, where
vector. The captured image is undersampled with re-
spect to an unknown high-resolution image
, by a factor of
The vector
represents samples of the unknown
high-resolution image tile
represents the blurring associated with the imaging
system. In X-ray imaging, this blurring is due to the geometry
of the radiating illumination as well as the scattering of light
in the object material. In each set of tiles, we approximate
this effect by a spatially-invariant point spread function (PSF)
. Although, the PSF can be different in different tile
sets. The warping operator
represents the subpixel spatial shifts between similar tiles in
the captured images. The spatial shifting is described by the
for the
represents (assumed square
, are ordered as a
, where
in each dimension.
similarly ordered. The
of size
th frame. In our model, we
Page 3
ROBINSON et al.: EFFICIENT FOURIER-WAVELET SUPER-RESOLUTION2671
assume that these spatial shifts are continuous values in the
range of
. This corresponds to the range of subpixel
motions in the captured images. Without loss of generality, we
assume that the tile
defines the coordinate system of the
high-resolution image and, hence, we only have to estimate
the unknown motion parameters for the remaining
The downsampling operator
the undersampling of the detector. Finally,
represents the noise inherent in the analog-to-digital conver-
sion. For our purposes, we assume this noise to be uncorrelated
zero-mean noise with standard deviation .
capturesof size
of size
B. Classic Maximum A Posteriori SR Reconstruction
The general problem of SR is to combine
images and estimate the high-resolution image . An important
category of solutions to the SR problem is the maximum a pos-
teriori (MAP) and closely related methodologies, which is dis-
cussed in this section. The MAP methods are based on the con-
struction of a cost function
, which is the summation of two
distinct terms. One is the data penalty term
the closeness of data to the estimates. The other is the regular-
ization term
, which represents the prior information about
the unknown high-resolution (HR) image
Early MAP-based SR methods assumed that the motion vec-
tors were accurately estimated in a separate process and the
noise model was Gaussian [12], [13], which justifies the ap-
plication of quadratic data penalty terms. As for the regular-
ization term, these techniques most frequently employed the
quadratic Tikhonov style regularization despite its tendency to
reduce edge contrast. The resulting cost function is in the form
captured LR
, which measures
weighting scalar. When
known HR image, then this cost function produces the ideal
Wiener filter estimate of the unknown image. This MAP func-
tional has the advantage of being quadratic, which means that
the estimate image is a linear function of the input measure-
ments and is, thus, easy to compute.
Through the years, application of more advanced prior func-
such as Adaptive Kernel regression [14] which gener-
alizes popular priors such as Tikhonov and Bilateral Total-Vari-
ation (B-TV) [9], have produced higher quality estimates. For
example, the B-TV cost function is defined as
is often a spatial high-pass operator and
is the exact covariance of the un-
is the
is a constant [9]. The parameter
responding Bilateral filter kernel. The Bilateral filter and its pa-
rameters are extensively discussed in [15], [9].
is a set of integer pixel shifts and
defines the size of the cor-
These advanced regularization functions are not quadratic
(nonlinear estimators) and, hence, require more compu-
tationally-complex iterative minimization strategies. Such
nonquadratic functionals can, however, preserve many impor-
tant features of images such as edges. Also, MAP-based robust
SR techniques (e.g., [9], [16], and [17]) are able to reduce the
effect of outliers such as motion estimation error.
Practical tests show that using a separate motion estimation
process, specially in low-SNR cases, is suboptimal. Therefore,
the critical issue of joint SR and motion estimation problem has
been the topic of several papers (e.g., [18]–[22]). A simplified
MAP formulation of this problem has the form
tion vector distribution may also be added to the previously
mentioned cost function [18]. The previously mentioned ap-
proaches are commonly solved in an iterative fashion and are
techniques are generally computationally more complex than
LR images is small or when the motion of most LR frames are
mating the unknown motion vectors and the aliasing free image
ages containing aliasing artifacts.
. Note that, additional priors on mo-
C. Problem of Joint Motion and Image Estimation
In this subsection, we briefly review an alternative approach
variable-projection principal [23], which we described in detail
inourrecent publication[7].Whilemotion-estimationis notthe
simplifies the material described in later sections of this paper.
[8] and rewrite the imaging model as
tion process will then be formulated as
is the unknown HR blurry image. The optimiza-
which is typically assumed to be stationary. A typical solution
to the previously mentioned problem is the cyclic coordinate-
descent method [18], in which in each iteration one unknown
variable is updated based on the estimate of the other unknown
variable in the previous iteration.
is the covariance matrix of the unknown signal,
D. Efficient Joint Estimation Using Variable Projections
Separable Nonlinear Least Squares problem [23], in our Vari-
Page 4
linear parameters (motion-vectors) are known. Consequently,
the estimate of the set of linear parameters
is computed as
Note that, unlike the cyclic coordinate-descent method, we re-
quire no iterations between the sets of parameters since we do
not explicitly calculate (7). Indeed, a direct approach to max-
imize (10) involves inverting a large matrix of size
which is computationally challenging for even small
image tiles. In [7], we described a series of numerical tricks
to speed up the process. One trick is solving the problem in
the Fourier domain and taking advantage of the spectral folding
phenomenon in aliased images.
To simplify the derivation, we reformulate the image cap-
ture model (19) in the Fourier domain. We use the over-script
" " to denote the Fourier domain representation. For example,
the th LR image is given by
tial frequencies are indexed by
. The
discrete Fourier transform of the observed image
Similarly, the high-resolution spatial frequency coordinates are
indexed by
and, where
Because the shift
operator is spatially-invariant, its Fourier
representation is a diagonal matrix defined as
, where the LR spa-
according to
term is the base of the
The downsampling operator
its Fourier representation is not diagonal. The downsampling
operator is, however, periodic and is conveniently represented
is not spatially-invariant and so
sents a
matrix of
corresponds to samples of the spectrum of the captured image
The form of matrix
justifies a decoupling of the observed
spatial frequency components in the Fourier domain. In other
words, we can consider each LR spatial frequency component
represents the Kronecker matrix product,
vector of all ones, and
dimension. In the Fourier domain, the vector
represents the identity
(indexed by
given by
and ) as an independent observation model
where the
matrices are constructed as
and the vectors are constructed according to
A single spatial frequency in the captured image is a function of
the original signal content and summation of the
spectral components. This demonstrates that the reconstruction
can be appliedto each collectionof aliased spectral components
independently. Thus, a singlet set of
frequency components
high-resolution spatial
can be estimated as
in which
density (PSD) function.
Finally, the motion estimation function of (10) simplifies to
are samples of the signal's power spectral
Estimating the motion vectors using (18) and the high-resolu-
tion image using (15) is significantly faster than using the di-
rect matrix form of (10) and (7). The simplified form requires
small matrices of size
verting one very large
similar acceleration techniques was described in [7].
So far, ignoring the PSF effects, we have studied a com-
putationally efficient multiframe joint motion estimation and
as opposed to in-
matrix of (7). A set of
Page 5
ROBINSON et al.: EFFICIENT FOURIER-WAVELET SUPER-RESOLUTION2673
SR technique in the presence of aliasing. In the next subsec-
tion, ignoring the aliasing effects, we study an efficient single
frame restoration (deblurring and denoising) method. We com-
bine these two techniques in Section III.
E. Single Frame Deblurring Using ForWarD Algorithm
In this section, we consider the problem of restoring the
contrast lost due to the blurring (PSF) inherent to the imaging
system. To achieve this, we briefly review the fast two-step
ForWarD deblurring algorithm [5]. The ForWarD algorithm
combines a Fourier-based regularized deconvolution algorithm
with a wavelet-based denoising post processing step. As de-
scribed in the following, in the first step, a Fourier domain
implementation of the Wiener filter reduces blur artifacts
while suboptimally magnifying the noise. In the second step, a
wavelet-based denoising process reduces the noise artifacts.
The basic ForWarD algorithm addresses the problem of
restoring an unknown image signal, which has been blurred by
a spatially-invariant operator such as a point spread function
(PSF) and corrupted by a stationary noise distribution as in the
forward model
The noise is assumed to have a stationary distribution. The first
step of the ForWarD algorithm involves inverting the spatially-
invariant blurring operator
using a regularized sharpening
filter such as the Wiener filter
process is formulated as
[5], [24]. Such estimation
ment noise (typically
[25]. For example,in the case of the normal and low-dosage im-
ages shown in Fig. 1, the noise standard deviation corresponds
to about 20% and 65% of the maximum signal intensity, respec-
tively. Indeed, under certain conditions the Wiener filter can be
regarded as a Tikhonov regularization functional [26], which
was described in Section II-B. While in the common Wiener
filter implementation
, in ForWarD algorithm, weighting
factor is chosen such that
choice of the weighting factor often enhances sharpness at the
expense of substantial noise amplification.
The Wiener filter can be implemented efficiently in the
Fourier domain using fast Fourier transforms (FFT). Imple-
mentation of the Wiener filter in the Fourier domain is founded
on the assumption that the blurring operator represented by
is spatially-invariant and that both the noise and signal are
stationary random processes. In this case, the blurring oper-
ator can be represented by its transfer function
computed as the Fourier transform of the system's PSF. Also,
the signal statistics are represented by a power spectral density
function (PSD)
. The application of the Wiener
is the covariance matrix of the stationary measure-
) and is a weighting factor
. Such perceivably suboptimal
filter is performed in the Fourier domain producing an estimate
of the original signal spectrum
according to
This estimate is converted back into the spatial domain using
FFT operators to obtain the estimate
The second step in the ForWarD process applies a nonlinear
denoising filter to the wavelet transform of the sharpened image
for the purpose of eliminating the residual noise amplified
by the restoration filter
step is based on adaptive thresholding in the wavelet domain.
The redundant (no downsampling) wavelet transform [27] is
applied to the sharpened image by convolving the image with
a set of scaling and wavelet functions, represented by the ma-
and , producing a set of scale coefficient images
and wavelet coefficient images
ficients, or wavelet shrinkage, eliminates the noisy artifacts in-
troduced during the deblurring step according to
. This nonlinear denoising
. Reducing the wavelet coef-
represents the noise variance at the th wavelet space for the
pixel, and
After shrinking the wavelet coefficients in this manner, the
inverse wavelet transform produces the final denoised estimate
of the original image . We refer the reader to the original work
of [5] for a more complete explanation of this process including
visualization of the images at the various steps in the algorithm.
Conceptually similar to the Wiener filter, the wavelet
shrinkage reduces the observed wavelet coefficients as a
function of the local SNR of the wavelet coefficients. The
performance of the wavelet shrinkage depends on the ability
to predict the SNR for the wavelet coefficients. Because of
the wavelet's spatial locality property, the wavelet shrinkage
provides efficient local, signal-dependent denoising.
The value of the noise variance for each wavelet function is
given by the covariance of the residual noise
domain. The covariance matrix of the noise in the wavelet do-
main is given by
. Because the Wiener filter is spa-
tially-invariant, the residual noise covariance
otherwords, theresidualnoise isstationarydue tothespatial in-
variance of the Wiener filter. Since the redundant wavelet filter
[27] is also spatially-invariant,theresidual noise power ina par-
ticular wavelet space is uniform over the entire image [5]. The
residual noise power in the wavelet domain is computed effi-
ciently in the Fourier domain by way of
identifies the pixel location,
is the wavelet signal power.
in the wavelet
is circulant. In
Page 6
This must be computed once for each wavelet filter used in the
The accuracy of the wavelet thresholding depends on accu-
In practice, this signal information must be estimated from
the captured image data. In [5], the authors propose a simple
process for estimating the signal power term
The authors estimate the wavelet signal power by first very
coarsely denoising the sharpened image
simple hard thresholding wavelet denoising approach to obtain
the coarsely denoised image using a different set of scaling and
wavelet functions
and wavelet
soft thresholding. We denote the wavelet coefficient images for
this different wavelet transform by
applied to the wavelet and scaling coefficients according to
. They propose a
than those used for the
. A hard thresholding
provides the coarsely denoised image. The
threshold([28]describesthechoicesfor ).Theinversewavelet
transform applied to the hard thresholded wavelet coefficients
reader to [5] for more information).
Next, the wavelet transform using the original wavelet func-
andwavelet isappliedtothecoarselydenoisedimage
to produce the coefficients
term is the input
The spatially-varying standard deviation of the signal's wavelet
coefficients is estimated to be the value of the coefficients of the
coarsely denoised image, or
This estimate of the signal power is used in (23). The ForWarD
approach to deconvolution has been applied to several deconvo-
lution problems with success and has been used as a benchmark
for evaluating the success of other single frame deconvolution
algorithms [14]. In the next section, we propose a multiframe
extension of this algorithm, which considers the aliasing and is-
sues inherent to the SR problem.
The goal of the multiframe SR problem, aside from reducing
ring inherenttothe imagingsystem. Toachievethis, wederivea
novel multiframe variant of the fast two-step ForWarD method
[5]. The authors of the original ForWarD algorithm have re-
cently proposed a new version of this algorithm that addresses
the multiframe deblurring problem [24] for nonaliased images
with stationary noise model. In this section, we introduce an ex-
tension of this multiframe algorithm, which considers the non-
stationarity inherent to the SR problem. Fig. 2 presents a flow
chart representation of this multiframe ForWarD (MForWard)
SR process.
Fig. 2. Flow chart representation of the MForWard algorithm.
A. MForWarD
The efficiency of the original ForWarD algorithm [5], as well
as the more recent multiframe version [24], is founded on the
stationarity of the noise as well as the spatial invariance of the
blurring operators. The stationarity assumption breaks down in
the case of multiframe SR.
and aliased images onto a higher resolution sampling grid. The
multiframe Wiener filter producing a sharp estimate of
variant of (21) given by
is a
the motion vectors
ForWarD algorithm, we typically use values of
tend to sharpen the images at the expense of increased noise
amplification and ringing artifacts in the resolution-enhanced
image .Afterapplyingthemultiframeresolutionenhancement
and are defined in (8) and (9). Here, we assume that
are estimated from (10). Similar to the
, which
Page 7
ROBINSON et al.: EFFICIENT FOURIER-WAVELET SUPER-RESOLUTION2675
Fig. 3. Grid on the left shows the 4?4 grid of HR image sample locations
and the number of LR measurements (assuming integer sampling offsets) per
sample location (no blur case as in the shift-and-add image reconstrution de-
scribed in [9]). In the sharpened image ? ?, the locations with fewer measure-
ments will have higher residual noise variance. For example, the grid on the
right shows the residual noise variance for an image reconstructed using an un-
infinite noise variance.
filter of (28), the covariance matrix of the residual noise error
Effective denoising and artifact removal using the wavelet
shrinkage method requires accurate estimates of the residual
noise power in the different wavelet filter domains. The noise
covariance matrix in the wavelet coefficient domain of the
wavelet function
is given by
stationary model, the residual noise power
the diagonal of this covariance matrix. Estimating this single
value is computed efficiently in (24) using FFT operations.
Unlike the standard ForWarD algorithm, the multiframe es-
timate of the deblurred image
field which is not stationary. In the case of multiframe recon-
struction, ignoring the border pixels, the residual noise field
is periodic with a period of
must compute the residual noise powers
diagonal of
which correspond to the
locations. We use
HR sampling locations (note Fig. 3).
The spatially-varying residual noise power depends on the
collection of motion vectors . That is, even though the noise
fields of the captured LR images
amounts of data. Because of this, the residual noise powers
in the wavelet domain are not uniform and cannot
be computed using (24).
To see this effect more clearly, we present a simple example.
Suppose that we capture twelve LR images with a downsam-
pling factor of
in both the vertical and horizontal di-
mensions. Furthermore, suppose that the captured images are
offset by integer numbers of pixels in the high-resolution grid
with the number of offsets per HR grid location shown in Fig. 3.
To simplify the analysis, we assume that the signal covariance
matrix is given by
free of blur
. In this case, after applying the multiframe
Wiener filter, the covariance matrix of the residual noise field
is a diagonal matrix. The terms along the diagonal corre-
spond to the residual noise variance in the reconstructed image.
. In the case of the
is constant along
has a residual random error
. For each wavelet filter, we
along the
HR grid
to index the
are stationary, different
and that the imaging system is
The HR pixels in the sharpened image
ments will have much higher noise variance as indicated on the
right side of Fig. 3. Indeed, in the more general case, the motion
vectors do not fall perfectly onto grid locations.
Once we obtain the
spatially-varying noise powers, we
employ the same pattern of the traditional ForWarD algorithm
using the coarse denoising by the hard thresholding of (25) to
estimate the wavelet coefficient energies followed by the soft
thresholding of (23). The only difference being that when we
apply the hard or soft thresholding of (23) and (25), we do not
use the same noise variance for every pixel. Instead, we use the
spatially-varying noise powers corresponding to the HR grid lo-
erties of the sharpened image
denoising. The next subsection explains a computationally effi-
cient approach to this issue.
lacking LR measure-
when performing the wavelet
B. Efficient Fourier MForWarD
One key advantage of the original ForWarD algorithm is its
to compute (22) and (24) eliminates the need to explicitly con-
struct the extremely large matrices to deconvolve the image and
to estimate the residual noise variances. The MForWarD algo-
anypractical value.Forexample,computingthecovariancema-
trix in (30) directly is computationally prohibitive due to the
size of the images. We now describe an efficient implementa-
tion of the MForWarD algorithm which leverages FFT opera-
tions, analogous to the method used in the case of the stationary
ForWarD approach.
We operate in the the Fourier domain as we did in
Section II-C, where we originally defined many of the matrices.
The only additional matrix is that of the blur operator
is spatially-invariant and, hence, is diagonal in the Fourier
In the case of multiframe Wiener filtering, we again consider
each spatial frequency component indexed by
independent observation model given by
andas an
A single spatial frequency in the captured image is a function of
the original signal content and summation of the
spectral components. This demonstrates that the reconstruction
can be appliedto each collectionof aliased spectral components
Page 8
Estimates of the
high-resolution image from the multiple measurements of the
observed spatial frequencies
spatial frequency components in the
are obtained via
resolution image in this fashion is significantly faster than using
the direct matrix form of (28). Implementing the multiframe
Wiener filter in the Fourier domain requires inverting
matrices of size
as opposed to inverting one very large
matrix of (28). In a practical sense, any im-
ages larger than 200 by 200 pixels would require inordinate (for
today's machines) amounts of processing to invert the B matrix
if usingthedirectmatrix formulation.This approachenablesef-
ficient implementation of a multiframe resolution enhancement
in the Fourier domain. After estimating the sharpened image ,
the image is converted back into the spatial domain using an in-
verse FFT to obtain the multiframe filtered image in the spatial
We can also apply a similar technique to accelerate the cal-
culation of the
residual noise powers in the wavelet spaces
, which are required for the wavelet denoising step.
Because the residual noise field is not stationary, its statistics
are not completely characterized by a power spectral density
function.Theresidual noisespectralcomponentsarecorrelated.
covariance matrix of the residual noise field spec-
tral components associated with the LR spatial frequency set
indexed by
is defined in (8). As in Section II-C, estimating the high-
To estimate the residual noise power for a given wavelet filter
at a particular grid location, we compute
the diagonal matrix
and "Tr" represents the trace operator and
represents the samples of the wavelet filter spectral response.
Equation (35) provides an efficient method for computing the
residual noise powers in the wavelet domain for use in wavelet
denoising. This step must be performed twice; once for the soft
thresholding wavelet functions
olding wavelet set
and once for the hard thresh-
Fig. 4. Comparison of linear SR techniques for increasing the resolution of 12
simulated LR frames by a factor of ? ? ?: (a) original image; (b) captured
image; (c) linear, strong prior; and (d) linear, weak prior.
Fig. 5. Solid curve shows the PSNR performance versus regularization using
standard linear regularization. The optimal weighting is around ? ? ??? for the
linearregularization.Thedashedcurvescompare thePSNRperformanceversus
regularization for the MForWarD algorithm using different thresholding values
?. The MForWarD algorithm shows superior peak PSRN performance over the
linear regularization.
In this section, we perform two sets of experiments demon-
strating the capability of the MForWarD algorithm. The first
section describes experiments using simulated general image
data. These results demonstrate the broad applicability of the
MForWarD algorithm. The second section describes some ex-
perimental results using real data captured by a digital mammo-
gram system on a phantom breast.
Page 9
ROBINSON et al.: EFFICIENT FOURIER-WAVELET SUPER-RESOLUTION2677
A. Simulation-Based Experiments
For the first set of experiments, we construct a simple sim-
ulation example which highlights the advantages of the MFor-
WarD algorithm for general imaging. We construct a simulated
data sequence by downsampling the 400 by 400 pixel portion
of a high-resolution shown in Fig. 4(a) using the subsample lo-
cation shown in Fig. 3. We show only a cropped portion of the
image to highlight the detail in the experiment. We simulate the
optical blur using a simpleheavy-tailed point spread function of
the form
where the
is the normalizing constant. We use a
also added noise to the LR images to create an effective SNR of
about 26 dB. An example of the simulated LR image is shown
in Fig. 4(b).
We reconstructed the HR image using three different ap-
proaches representing the different classes of SR algorithms.
In all these simulated cases, the motion was assumed to be
perfectly known. The first method is the completely linear
approach embodied by the multiframe Wiener filter of (28),
or the Fourier implementation of (33). This is perhaps the
least computationally complex approach to multiframe SR.
Using (33) produces an estimate of the HR image in a single,
noniterative step and requires inverting several very small
matrices. The drawback to this linear approach, however, is
that the regularization of the multiframe linear filter trades off
sharpness in the final image for noise reduction. For example,
Fig. 4(c) shows an example of the linear reconstruction when
using a power spectral density (PSD) function of the form
term controls the rate of decay of the PSF, and
value of 1.2 pixels. We
The reconstructed image shows poor contrast to maintain
minimal noise amplification. Alternatively, if we apply the
linear reconstruction with a weaker prior
reconstructed image shows improved contrast restoration at the
expense of noise amplification as seen in Fig. 4(d). This is the
classic tradeoff inherent to linear restoration algorithms.
The black curve of Fig. 5 shows the PSNR tradeoff as a func-
tion of regularization weighting strength. For a small
PSNR is quite poor due to weak regularization of the poorly
conditioned system. When
increases beyond 4.5, the PSNR
slowly degrades as the estimate becomes overly smoothed. If
the signal's PSD was perfectly defined by (38), then the PSNR-
maximizing weighting parameter would be near
dashed curves show PSNR versus regularization weighting for
the MForWarD algorithm using different thresholding param-
. While the performance varies considerably for large
valuesof ,thepeak PSNRnear
parameter .Whenwe applyourMForWarDalgorithm,weem-
ploy 2-tap Daubechie filters for the soft thresholding wavelet
functions and 6-tap Daubechie filters for the coarse denoising
by way of hard wavelet coefficient thresholding.
. The regularization weighting of (33) is.
, the
, the
. The
Fig. 6. Comparison of image quality for different regularization techniques
using PSNR-optimal settings: (a) linear regularization (???? ? ????? dB);
(b) B-TV (???? ? ????? dB); and (c) MForWarD (???? ? ???? dB).
Fig. 6 compares the PSNR-optimal parameter settings for
three different algorithms. Fig. 6(a) shows the PSRN-optimal
linear regularization setting, which has a PSNR of 23.90 dB at
. The image shows a reasonable balance between con-
trast and noise gain, but still maintains a considerable amount
of noise in the flat sky region. Fig. 6(b) shows the PSNR-op-
timal B-TV algorithm of (3) having 23.96 dB. In this experi-
, and
a reasonably good job of preserving contrast while eliminating
the noise in the flat regions. Fig. 6(c) shows the MForWarD
algorithm at 24.3 dB using a thresholding value of
The image preserves much of the contrast while eliminating the
is the minimal computational overhead required for the wavelet
denoising. Running on an Intel Core-2-Duo 2.2 GHz processor,
the Fourier restoration requires 4.3 s of computation time. Per-
for a total of about 6.6 s. In contrast, the B-TV algorithm, repre-
sentative of the large class of iterative techniques, requires only
0.5 s per iteration, but requires at least 60 steepest descent iter-
ations (30 s) before approaching a limiting image quality.
. The algorithm does
B. Real X-Ray Data Experiments
In this section, we apply our multiframe reconstruction
and restoration algorithm to real images captured on an ex-
perimental X-ray imaging system. Our experimental imaging
system is based on a Mammomat NovationTOMO digital
mammography prototype system (Siemens Medical Solutions,
Page 10
Fig. 7. Scatter plot shows the set of estimated motions ?? ? ? on the HR image
grid. Some high-resolution pixels have as many as four measurements whereas
others have none underscoring the need for a spatially-varying denoising
Erlangen, Germany),3stationed at the Duke University Medical
Center. The system uses a stationary selenium-based detector
of 85
m pixels. Pixels with this size correspond to a Nyquist
sampling rate of 5.6 line pairs per millimeter (lp/mm). We use
a CIRS model 11A breast phantom (CIRS Inc., Norfolk, VA)
to test our SR algorithms. We introduce shifts in the image
by two methods. First, we allow the x-ray tube to rotate by
1 degree. Second, we manually move the breast phantom
to introduce motion into the system. This manual motion is
completely uncontrolled. Our dataset consists of 15 frames at
the low dosage level of 11.3 mAs at 28 kVp tube voltage. As
a point of reference, we also acquire a single frame at a more
typical dosage of 226 mAs at 28 kVp tube voltage (Fig. 1). The
breast phantom includes several testing features including a
pair of resolution bar charts. We focus on the results of the test
resolution chart to explore the contrast performance of the mul-
tiframe imaging system. We apply our algorithm to 100
pixel tiles in the captured image to estimate 400
high-resolution images (enhancement
We modeled our system PSF as a heavy-tailed exponential
energy distribution with
PSNR, we calculated the standard deviation in a textureless
region of the phantom. We also measured the difference in
grayscale values between for the registration bars in the res-
olution chart to get an approximate PSNR value of 3 dB.
We fit the
terms of (38) to the periodogram obtained by
averaging the spectral energy distribution over the collection of
LR images. We employed 2-tap Daubechie filters for the the
soft thresholding wavelet functions and 6-tap Daubechie filters
for the coarse denoising by way of hard wavelet coefficient
Fig. 7 shows a scatter plot of the set of estimated motions
on the HR image grid. The grid reflects the number of
400 pixel
. To get a measure of the
3Caution: Investigational Device. Limited by U.S. Federal law to investiga-
tional use. The information about this product is preliminary. The product is
under development and is not commercially available in the U.S.; and its future
availability cannot be ensured.
Fig. 8. (a) High dosage LR image (226 mAs); (b) low-dosage LR image
(11.3 mAs); (c) motion compensated average of LR frames (no resolution
enhancement); and (d) Multiframe reconstruction image ? ? of (7).
image samples per pixel in the reconstructed image. The ex-
ample shows some pixel estimates combining as many as four
measurements, whereas other pixels have no measurements un-
derscoring the spatial variability of the residual noise variance.
Fig. 8 gives a visual example of the SNR for an image ob-
SNR after combining multiple low-exposure images. We focus
on the portion of the resolution chart beyond the Nyquist rate
for the imaging system (5.6 lp/mm). The numbers indicate the
ically high radiation dosage of 226 mAs. The bar targets clearly
show aliasing artifacts. The second image Fig. 8(b) shows the
same portion of the resolution chart captured at a much lower
SNR of the captured images at such low illuminating radiation.
The third image Fig. 8(c) shows the result of averaging the mo-
tion compensated LR frames without enhancing the resolution
or sampling rate of the system. Interestingly, the multiframe av-
erage appears to have an approximately equivalent SNR to the
single frame captured at a normal radiation dosage, although it
contains some blur as a result of averagingframes with subpixel
sampling offsets. The fourth image Fig. 8(d) shows the recon-
structed image
after registering the collection of images using
the multiframe algorithm described in Section II. The recon-
structed image shows a restored resolution above the Nyquist
rate inherent to the detector. The image is, however, still noisy
and has low contrast. The effective SNR seems comparable to
that of the single image captured under high dosage Fig. 8(a).
Page 11
ROBINSON et al.: EFFICIENT FOURIER-WAVELET SUPER-RESOLUTION2679
Fig. 9. Different restoration techniques applied on the low-dosage sequence
illustrated in Fig. 8(b): (a) multiframe sharpened image ? ?; (b) basic ForWarD ? ?
applied to multiframe sharpened image; and (c) MForWarD ? ?. The MForWarD
algorithm provides superior noise removal while preserving contrast.
Fig. 9 shows the resulting images after applying the sharp-
shows the result
after applying the multiframe Wiener sharp-
ening filter of(28). Theimageshows improvedcontrast within-
creasedsharpness, butalso amplified noise.Theimage Fig.9(b)
to the Wiener sharpened image Fig. 9(a). The hard threshold
value was chosen to be
. The resulting image preserves
the contrast around the bar chart signal locations while elimi-
The image still contains some residual noise. The final image
Fig. 9(c) shows the result after applying the MForWarD algo-
rithm including the nonstationary noise power computations.
The hard threshold for the MForWarD algorithm was also
. Including the spatially-varying noise powers improves the
noise removal over the basic ForWarD algorithm Fig. 9(b).
To get an another perspective on the effects of the multiframe
restoration and denoising, we plot slices through the resolution
test chart region as indicated in Fig. 10 (top). Fig. 10 shows
slices through several images. The top curve shows the slice
pixel motion. The slice shows some aliasing as well as lost con-
trast for the bars about the Nyquist sampling rate of 5.6 lp/mm.
The second graph shows a slice through the multiframe recon-
structed image . The reconstruction eliminates the aliasing ar-
of the detector. The signal strength above 8 lp/mm, however, is
Fig. 10. Top: input low-dosage image [a zoomed in version of which was il-
lustrated in Fig. 8(b)]. The arrow marks the sampling slice through the resolu-
tion chart. Bottom: five curves show slices through the horizontal resolution bar
charts. The Nyquist rate of the system corresponds to 5.6 lp/mm. The top slice
shows a slice through an interpolated average of the captured images showing
aliasing artifacts and lost contrast. The second slice through ? ? shows enhanced
resolution beyond the Nyquist rate, but poor contrast. The third slice through ? ?
shows restored contrast but with noise amplification. The first three left ellip-
soids in the fourth slice through the basic Forward reconstruction mark the am-
plified noise regions. The rightmost ellipsoid marks the lost resolution region.
The bottom slice through the MForWarD estimate ? ? shows contrast preserva-
tion with significantly less noise.
very weak due to the blurring inherent to the imaging system.
sharping restores contrast out to the 12 lp/mm, more than twice
the Nyquist rate, but at the expense of noise amplification. The
has some noise amplified regions. The bottom slice shows
the bar chart signal regions.
The final goal of digital mammography is the detection and
diagnosis of cancerous lesions in the breast tissue. The breast
phantom contains small grains of calcium for predicting the
diagnostic capability of the imaging system for calcifications
in the breast. The calcium grains range from 400
m down to
Page 12
Fig. 11. Table of images shows the low-dosage image (first column), mul-
tiframe average (second column), standard dosage image (third column) and
MForWarD imageofcalciumcarbonitedepositswithdecreasinggrainsize.The
synthetic calcifications are clearly visible in all of the MForWarD images.
196 m. Fig. 11 shows the performance of the MForWarD al-
gorithm for enhancing the contrast of these small deposits. The
grains of calcium carbonite. In fact, the grains are visible even
when the grains are nearly indistinguishable from noise in the
single images captured at a standard dosage.
In this paper, we have proposed a novel method for restoring
and denoising super-resolved low dosage X-ray images in a fast
multiframe variant of the ForWarD algorithm of [5]. The pro-
posed Fourier multiframe restoration and wavelet denoising al-
proving the extremely poor SNR of low-dosage images. The
experimental results confirm that multiframe imaging can pro-
vide an alternative in the SNR versus resolution tradeoff for
digital mammography. We note that our restoration algorithm
can be easily modified to further enhance the quality of other
Shift-and-Add based SR techniques [2], [29].
The design of future X-ray imaging systems would benefit
from a systematic analysis of the resolution and SNR required
for mammographic screening and diagnosis. Currently, there
are no publicly available databases of multiframe aliased digital
mammography images, only digitized analog mammography
images. Upon approval of an institutional review board (IRB),
we intend to apply this technique to imagery collected from pa-
tients in the clinical setting. At this point, we hope to demon-
strate the ability of this technique to improve image quality on
real digital mammography images.
In the future, one might explore the fundamental tradeoffs
between radiation exposure, number of frames, and reconstruc-
tion performance. Furthermore, extensions to the ForWarD al-
gorithm which include more sophisticated redundant wavelet
techniques such as curvelets [30] or ridgelets [31] might show
even better performance. Recent research has shown that use
of more sophisticated wavelets have been found to improve the
quality in other medical imaging applications [32]. Future re-
searchmight alsoincludeexplorationoftheeffectsofsuchmul-
tiframe imaging on the higher-level segmentation or detection
tasks associated with digital mammography.
Our MATLAB software implementation of the noted algo-
rithms is in part based on the ForWarD software, developed by
Dr. R. D. Neelamani of the Digital Signal Processing group at
Rice University (available at http://www.dsp.rice.edu/software/
ward.shtml). The authors would like to thank Prof. P. Milanfar
us in the original multiframe motion estimation publication.
[1] M. D. Robinson, S. Farsiu, J. Y. Lo, and C. A. Toth, "Efficient restora-
tion and enhancement of super-resolved x-ray images," in Proc. 15th
IEEE Int. Conf. Image Processing, Oct. 2008, pp. 629–632.
[2] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, "Advances and chal-
lenges in super-resolution," Int. J. Imag. Syst. Technol., vol. 14, no. 2,
pp. 47–57, Oct. 2004.
[3] S. Borman and R. L. Stevenson, "Super-resolution from image se-
quences—A review," in Proc. Midwest Symp. Circuits and Systems,
Apr. 1998, vol. 5, pp. 374–378.
[4] S.Park,M.Park,andM.G.Kang,"Super-resolutionimagereconstruc-
tion, a technical overview," IEEE Signal Process. Mag., vol. 20, no. 3,
pp. 21–36, May 2003.
[5] R. Neelamani, H. Choi, and R. Baraniuk, "Forward: Fourier-wavelet
regularized deconvolution for ill-conditioned systems," IEEE Trans.
Signal Process., vol. 52, no. 2, pp. 418–433, Feb. 2004.
[6] E.Pisano,C.Gatsonis,E.Hendrick,M.Yaffe,J.Baum,S.Acharyya,E.
agnostic performance of digital versus film mammography for breast
cancer screening—The results of the american college of radiology
imaging network (ACRIN) digital mammographic imaging screening
trial (DMIST)," New Eng. J. Med., pp. 1773–1783, Oct. 2005.
[7] D.Robinson,S.Farsiu,andP.Milanfar,"Optimalregistationofaliased
Comput. J., vol. 52, no. 1, pp. 31–42, Jan. 2009.
[8] M. Elad and Y. Hel-Or, "A fast super-resolution reconstruction algo-
rithm for pure translational motion and common space invariant blur,"
IEEETrans. Image Process., vol. 10, no. 8, pp. 1186–1193, Aug. 2001.
[9] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, "Fast and robust
multi-frame super-resolution," IEEE Trans. Image Process., vol. 13,
no. 10, pp. 1327–1344, Oct. 2004.
[10] R. Hardie, "A fast image super-resolution algorithm using an adap-
tive wiener filter," IEEE Trans. Image Process., vol. 16, no. 12, pp.
2953–2964, Dec. 2007.
[11] D. Robinson, S. Farsiu, J. Y. Lo, P. Milanfar, and C. A. Toth, "Effi-
cient multiframe registration of aliased X-ray images," in Proc. 41st
Asilomar Conf. Signals, Systems, and Computers, Nov. 2007, pp.
[12] R. R. Schultz and R. L. Stevenson, "Extraction of high-resolution
frames from video sequences," IEEE Trans. Image Process., vol. 5,
no. 6, pp. 996–1011, Jun. 1996.
[13] M. Elad and A. Feuer, "Restoration of single super-resolution image
from several blurred, noisy and down-sampled measured images,"
IEEE Trans. Image Process., vol. 6, no. 12, pp. 1646–1658, Dec. 1997.
[14] H. Takeda, S. Farsiu, and P. Milanfar, "Deblurring using regularized
locally adaptive kernel regression," IEEE Trans. Image Process., vol.
17, no. 4, pp. 550–563, Apr. 2008.
[15] C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color
images," in Proc. IEEE Int. Conf. Computer Vision, Jan. 1998, pp.
[16] V. Patanavijit and S. Jitapunkul, "A Lorentzian stochastic estimation
for a robust iterative multiframe super-resolution reconstruction
with Lorentzian-Tikhonov regularization," EURASIP J. Adv. Signal
Process, vol. 2007, no. 2, 2007.
Page 13
ROBINSON et al.: EFFICIENT FOURIER-WAVELET SUPER-RESOLUTION2681
[17] N. A. El-Yamany and P. E. Papamichalis, "Robust color image su-
perresolution: An adaptive m-estimation framework," J. Image Video
Process., vol. 8, no. 2, pp. 1–12, 2008.
[18] R. Hardie, K. Barnard, and E. Armstrong, "Joint map registration and
high-resolution image estimation using a sequence of undersampled
images," IEEE Trans. Image Process., vol. 6, no. 12, pp. 1621–1633,
Dec. 1997.
[19] L.D.Alvarez,J.Mateos,R.Molina,andA.K.Katsaggelos,"Advances
no. 2, pp. 58–66, Oct. 2004.
[20] D. Robinson and P. Milanfar, "Statistical performance analysis of
superresolution," IEEE Trans. Image Process., vol. 15, no. 6, pp.
1413–1428, Jun. 2006.
[21] P. Vandewalle, S. Susstrunk, and M. Vetterli, "A frequency domain
approach to registeration of aliased images with application to super-
resolution," EURASIP J. Appl. Signal Process., 2006.
[22] N.Woods,N.Galatsanos,andA.Katsaggelos,"Stochasticmethodsfor
joint registration, restoration, and interpolation of multiple undersam-
pled images,"IEEE Trans. Image Process., vol. 15, no. 1, pp. 201–213,
Jan. 2006.
[23] G. Golub and V. Pereyra, "Separable nonlinear least squares: The vari-
able projectionmethod and itsapplications," Inst. Phys.Inv. Prob., vol.
19, pp. R1–R26, 2003.
[24] R. Neelamani, M. Deffenbaugh, and R. Baraniuk, "Texas two-step: A
frameworkforoptimal multi-inputsingle-outputdeconvolution," IEEE
Trans. Image Process., vol. 16, no. 11, pp. 2752–2765, Nov. 2007.
[25] A. K. Jain, Fundamentals of Digital Image Processing, 1st ed.
Saddle River, New Jersey: Prentice-Hall, 1989.
[26] A. Murli, L. D'Amore, and V. De Simone, "The wiener filter and regu-
larization methods for image restoration problems," in Proc. Int. Conf.
Image Analysis and Processing, Sep. 1999, pp. 394–399.
[27] J. Fowler, "The redundant discrete wavelet transform and additive
noise," IEEE Signal Process. Lett., vol. 12, no. 9, pp. 629–632, Sep.
[28] S. Mallat, A Wavelet Tour of Signal Processing.
demic, 1998, p. 442.
[29] H. Takeda, S. Farsiu, and P. Milanfar, "Kernel regression for image
processing and reconstruction," IEEE Trans. Image Process., vol. 16,
no. 2, pp. 349–366, Feb. 2007.
[30] J.-L. Starck, E. Candes, and D. Donoho, "The curvelet transform for
image denoising," IEEE Trans. Image Process., vol. 11, no. 6, pp.
670–684, Jun. 2002.
[31] M. Do and M. Vetterli, "The finite ridgelet transform for image repre-
sentation," IEEE Trans. Image Process., vol. 12, no. 1, pp. 16–28, Jan.
[32] A. Khare and U. S. Tiwary, "A new method for deblurring and
denoising of medical images using complex wavelet transform," in
Proc. IEEE Conf. Engineering in Medicine and Biology, Sep. 2005,
pp. 1897–1900.
New York: Aca-
trical engineering from the University of California,
Santa Cruz, in 2004, where he developed super-reso-
lution algorithms.
He is currently the Manager of the Digital Optics
Research group at Ricoh Innovations, Inc., Menlo
Park, CA, researching practical applications of
computational imaging technology. His work ranges
from optical imaging systems design to image
processing algorithm research.
Cynthia A. Toth received the M.D. degree from the
Medical College of Pennsylvania.
She completed ophthalmology residency training
at Geisinger Medical Center and a Fellowship in Vit-
reoretinal Diseases and Surgery at University of Cal-
ifornia, Davis. She was Chief of the Retina Service
at Wilford Hall USAF Medical Center. Joining Duke
University Medical Center Faculty, Durham, NC, as
a vitreoretinal surgeon in 1993, she is now Professor
of Ophthalmology and of Biomedical Engineering.
She heads the Duke Advanced Research in SDOCT
Imaging (DARSI) Laboratory. She is the author of over 100 peer-reviewed pub-
lications and multiple book chapters, and an inventor on 11 patents.
Dr. Toth is a Diplomate of the American Board of Ophthalmology, Fellow of
the American Academy of Ophthalmology, and a member of the Retina Society
and Club Jules Gonin.
Joseph Y. Lo received the B.S.E. and Ph.D. degrees
in biomedical engineering from Duke University,
Durham, NC.
and biomedical engineering, faculty of the medical
physics graduate program of Duke University/Duke
doctoralfellowin radiologyatDukeUniversityMed-
ical Center. His research interests span many topics
in the advanced imaging and management of cancer,
including breast tomosynthesis imaging, computer-
aided diagnosis and risk prediction, radiation therapy treatment planning, bioin-
formatics, and digital image processing.
Sina Farsiu received the B.Sc. degree in electrical
engineering from Sharif University of Technology,
Tehran, Iran, in 1999, the M.Sc. degree in biomed-
ical engineering from the University of Tehran,
Tehran, in 2001, and the Ph.D. degree in electrical
engineering from the University of California, Santa
Cruz (UCSC), in 2005.
He was a Postdoctoral Scholar at UCSC from
2006 to 2007 and a postdoctoral research associate in
the Department of Ophthalmology, Duke University,
Durham, NC, from 2007 to 2009. He is currently
an Assistant Professor in the Departments of Ophthalmology and Biomedical
Engineering, and Fitzpatrick Institute for Photonics at Duke University, where
he is the director of the Vision and Image Processing (VIP) laboratory. His
technical interests include robust image enhancement and reconstruction,
automatic segmentation, computer aided surgery, ocular imaging and analysis,
optical coherence tomography, and digital X-ray imaging.


text:
Emergency Responder Technology
Attorney General Eric Holder spoke to a conference of emergency responders and praised the advances made in emergency communication since the September 11 attacks.
*The transcript for this program was compiled from uncorrected Closed Captioning.
People in this video
- Rushern Baker County Executive Prince George's County, MD
- Marc Bashoor Fire Chief Prince George's County, MD
- Robert Griffin First Responders Group Director Department of Homeland Security->Science and Technology
- Eric H. Holder Jr. Attorney General Department of Justice
- Mark A. Magaw Police Chief Prince George's County, MD
- Laurie Robinson Assistant Attorney General Department of Justice->Office of Justice Programs
Hosting OrganizationMore Hosting Organizations
More Videos From
DOD, DOJ, DHS: Technologies For Critical Incident Preparedness Expo
Related Video
Lessons Learned from BP Gulf of Mexico Oil Spill
Navy and Coast Guard officials talked about the lessons learned from the BP oil spill in the Gulf of Mexico. Among…
Lessons Learned from BP Gulf of Mexico Oil Spill, Part 2
The Departments of Defense, Homeland Security, and Justice sponsored a technology conference for emergency…
Counterterrorism and Cybercrime
U.S. security and law enforcement officials spoke to reporters and answered questions following a meeting with European…
Public Defender System
The Department of Justice held a symposium on the public defender system. Attorney General Eric Holder gave the…


text:
School copes with aftermath of football player's injury
SAN MARCOS - Mission Hills High School brought in psychologists and extra counselors Monday to assist grieving students and a nearby church held a vigil, as the school coped with news that one of its football players was severely injured. View A Video
Linebacker Scott Eveland, 17, fell unconscious Friday during a football game against Santee's West Hills High School, on Mission Hills High's field. He has been in a coma in the critical care unit at Palomar Medical Center ever since.
Doctors say the teen suffered a severe blow to the head. Exactly how and when the injury occurred is unclear, with witnesses reporting that Eveland stumbled and appeared dazed before coaches told him to lay down on the sidelines. An ambulance rushed the linebacker to the hospital shortly thereafter.
On Monday, friends of the teen and his family set up a charitable fund at Discovery Bank, with donations earmarked for the football player's rehabilitation. And Mission Hills Church, which located near the high school, held a vigil for students, parents and others who wanted a chance to come together and pray on behalf of Eveland.
"All we can do is be as positive as we can and keep going," Teammate Torey Williams said.
Several hundred teenagers, clad in jeans and sneakers, filled the large sanctuary of Mission Hills Church on Monday evening to show support for Eveland at a special 7 p.m. prayer service. Many wore large white or black T-shirts that resembled jerseys with Eveland's last name spelled out on the back.
Mission Hills pastor Chico Goff told those gathered that the service was "not about a school or a particular community or a particular sport. It's about people, all students, and coaches and teachers and administrators caring for each other and supporting each other in a very difficult time."
Mission Hills High Principal Brad Lichtman held a news conference at the school, telling reporters the teen's condition remained unchanged.
Standing in front of a giant banner that proclaimed students' support for the football player, the principal said school officials -- including the football team's coaches -- had yet to watch videotapes of Friday's game, even though doing so might reveal how linebacker was hurt.
"There will be a time when we will review the tapes," said Lichtman. "Right now, we are going to focus on supporting the family."
High school football coaches typically review game footage within days after a game, to verify their teams' performances and statistics. Lichtman said that was not done this time because the Mission Hills football team has no game scheduled this week, and school officials have been too busy "being there" for the injured player and his family to get to the tapes of Friday's game.
Community members were quick to rally around Eveland and his family, with about 200 people attending a prayer vigil at the school Saturday night. Hospital spokesman Andy Hoang said Monday that about 300 of the teen's teammates, fellow students and friends have come by the hospital, where they have been allowed to visit Eveland in small groups.