In this paper, we study the problem of reproducing the world lighting from a single image of an object covered with random specular microfacets on the surface. We show that such reflectors can be interpreted as a randomized mapping from the lighting to the image. Such specular objects have very different optical properties from both diffuse surfaces and smooth specular objects like met als, so we design special imaging system to robustly and effectively photograph them. We present simple yet reliable algorithms to calibrate the proposed system and do the inference. We conduct experiments to verify the correctness of our model assumptions and prove the effectiveness of our pipeline.
@cite_18: We call a random lens one for which the function relating the input light ray to the output sensor location is pseudo-random. Imaging systems with random lenses can expand the space of possible camera designs, allowing new trade-offs in optical design and potentially adding new imaging capabilities. Machine learning methods are critical for both camera calibration and image reconstruction from the sensor data. We develop the theory and compare two different methods for calibration and reconstruction: an MAP approach, and basis pursuit from compressive sensing [5]. We show proof-of-concept experimental results from a random lens made from a multi-faceted mirror, showing successful calibration and image reconstruction. We illustrate the potential for super-resolution and 3D imaging.
@cite_17: Compressive Sensingis an emerging field based on the revelation that a small numbe r of linear projections of a compressible signal contain enough information for reconstruction and processing. It has many promising implications and enables the design of new kinds of Compressive Imagingsystems and cameras. In this paper, we develop a new camera architecture that employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns. Its hallmarks include the ability to obtai n an image with a single detection element while sampling the image fewer times than the number of pixels. Other attractive properties include its universality, robustness, scalab ility, progressivity, and computational asymmetry. The most intriguing feature of the system is that, since it relies on a sing le photon detector, it can be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.
Many ideas in this paper are inspired by previous work on Random Camera @cite_18 . However, the key difference between our paper and the previous work is that in @cite_18 no lens is used and hence all the lights from all directions in the lightfield get mixed up which is difficult to invert. In our setup, we place a lens between the world and the camera sensor, which makes the problem significantly easier and more tractable to solve. Also similar ideas appears in the single pixel camera'' @cite_17 where measurements of the light are randomized for compressed sensing.