The DUDE framework for continuous tone image denoising

Abstract

This paper discusses the challenges of applying the DUDE framework to continuous tone images and the tools used to address these challenges. As in lossless image compression, a key component of the DUDE framework is the determination of a probability distribution for samples of the input (noisy) image, conditioned on their contexts. Thus, we can leverage from tools developed and tested in the context of lossless compression for determining such distributions, together with tools that are specific to the assumptions of the denoising application. These tools combine with the DUDE principles into a framework that yields powerful and practical denoisers for continuous tone images corrupted by a variety of noise processes.