CIF:SMALL: Image Restoration via Bayesian Structured Sparse Coding

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Image quality degradation is inevitable in various scientific and engineering applications such as biomedical/astronomical imaging and error-prone image communications. Physics-based solutions to fight against those adversary factors (e.g., sensor noise, optical blur and channel errors) are often too expensive; therefore it is highly desirable to develop computational alternatives to salvage those degraded images. Computationally efficient and high-quality image restoration algorithms could find a wide range of applications from energy-efficient sensing to more-affordable healthcare.This research contains two components. First is to characterize both nonlocal invariance and local variation of images in a principled fashion. A new framework called Bayesian structured sparse coding will be developed to unify and extend several existing classes of models (e.g., wavelet-based and patch-based) for natural images. Second, the new theoretical framework will facilitate the invention of computationally efficient inference algorithms for image restoration applications. The ultimate objective along this line of research is to make the developed Bayesian image restoration algorithms not only theoretically optimal but also practically feasible.