Improved wavelet decoding via set theoretic estimation

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. b s t r a c tWe define the new idea of blind image repair as a process of correcting one or more different and unknown types of distortions afflicting an image. These distortions could introduce linear or non-linear degradations, compression artifacts, noise, etc., or combinations of these. Thus the concept encompasses denoising, deblurring, deblocking, deringing, and other post-acquisition image improvement processes that address distortions. The problem is distortion-blind when the natures of the distortion processes are unknown prior to analyzing the image. Towards solving this problem, we describe a new framework for repairing an image that has undergone an unknown set of distortions, based on identifying the distortion(s) present in the image (if any) and applying possibly multiple distortion-specific image repair algorithms. Our philosophy is based on the principle that the task of general purpose image repair is one of agglomeration, i.e., the algorithm should embody multiple high-performing distortion-specific repair modules such that seamless general purpose image repair is achieved. Our proposed frameworkthe GEneral-purpose No-reference Image Improver (GENII) -enables the design of algorithms that are blind to distortion type as well as to distortion parameters, and only requires as input the distorted image to be repaired. The GENII framework is modular and easily extensible to image repair problems beyond those considered here. GENII operates by using natural scene statistic models to identify distortion, to perceptually optimize the distortion parameter(s), to assess the quality of the intermediate repaired images, and to perceptually optimize the repair processes. We explain the general purpose image repair framework and one specific realization, dubbed GENII-1, which assumes that the image has been affected by one or more of four possible distortion types.The performance of GENII-1 is evaluated on 4000 distorted images, and shown to deliver substantial improvements in both quantitative and qualitative visual quality.

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