Complexity-Regularized Image Denoising

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Complexity-Regularized Image Denoising
We develop a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional l2 , l1 , and Besov regularization methods. Different complexity measures are considered, in particular those induced by state? of?the?art image coders. We focus on a Gaussian denoising problem and derive a connection between complexity?regularized denoising and operational rate?distortion optimization. This connection suggests the use of efficient algorithms for computing complexity-regularized estimates. Bounds on denoising performance are derived in terms of an index of resolvability that characterizes the compressibility of the true image. Comparisons with state-of-the-art denoising algorithms are given. Work supported by the National Science Foundation under award MIP-9732995 (CAREER). This work was presented in part at ICIP'97. 1
Juan Liu, Pierre Moulin
Added 26 Oct 2009
Updated 26 Oct 2009
Type Conference
Year 1997
Where ICIP
Authors Juan Liu, Pierre Moulin
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