Gaussian Blur In MATLAB and Connection to Image Resolution I am calling fspecial() in MATLAB to create a Guassian blur of an image.

For example,

%Create Gaussian Filter
G = fspecial('gaussian', [5 5], 2);

% Blur Image
blurredImage = imfilter(nonBlurredImage,G,'same')


The parameter [5 5] is hsize which defines the extent to which the Gaussian filter is applied ... which according to user "Try Hard" ... "In the case of a gaussian filter, the intensity at each pixel around the central one is weighted according to a gaussian function prior to performing a box average over the region."

The parameter 2 is sigma ... with units of pixels.

Say the size of nonBlurredImage is 200x200. And that the side of each pixel represents a length of 0.25 mm. Then the image could resolve objects with a "resolution" of 1 line pair/mm without aliasing. 

If I pass G = fspecial('gaussian', [5 5], 1) to the filter function, and then produce the blurred image. What would be the "resolution" of this image? How does it relate to hsize? If I instead used [10 10] or [1 1], how would that affect things?

Thanks!
When applying Gaussian Blur on an image you should care for the STD only.
The rest should be set as following:

STD_TO_RADIUS_FACTOR = 5;
kernelRadius = ceil(STD_TO_RADIUS_FACTOR * kernelStd);
kernelLength = (2 * kernelRadius ) + 1;
mGaussianKernel = fspecial('gaussian', [kernelLength, kernelLength], kernelRadius);


Now, regarding the resolution of the image, this is tricky since it has many parameters in it.
If you want to measure it in Cycles in Spatial (As DFT), you can see the spectrum properties (Cut Off Frequency) of a Gaussian Kernel.

Update

The STD_TO_RADIUS_FACTOR should be determined by the balance between accuracy and performance.
The above is basically a truncated version of the Gaussian Kernel (Which is infinite). The balance sets how good is the approximation vs. how long the Convolution Kernel is (The longer the higher computational resources it requires).