From Image Restoration to Compressive Sampling in Computational Photography. A Bayesian Perspective.

the quality of the imaging system or reproducing the scene conditions in order to acquire another image is not an option, computational approaches provide a powerful means for the recovery of lost information. Image recovery is the process of estimating the information lost due to the acquisition or processing system and obtaining images with high quality, additional information, and/or resolution from a set of degraded images. Three specific areas of image recovery are today of high interest. The first one is image restoration, blind deconvolution, and super-resolution, with application, for instance, on surveillance, remote sensing, medical and nano-imaging applications, and improving the quality of photographs taken by hand-held cameras. The second area is compressive sensing (CS). CS reformulates the traditional sensing processes as a combination of acquisition and compression, and traditional decoding is replaced by recovery algorithms that exploit the underlying structure of the data. Finally, the emerging area of computational photography has provided effective solutions to a number of photographic problems, and also resulted in novel methods for acquiring and processing images. Image recovery is related to many problems in computational photography and, consequently, its algorithms are efficiently utilized in computational photography tasks. In addition, image recovery research is currently being utilized for designing new imaging hardware. In this talk, we will provide a brief overview of Bayesian modelling and inference methods for image recovery and the very related of compressive sensing, and computational photography.