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Complexity-regularized image denoising
Liu, J.; Moulin, P.;
Image Processing, IEEE Transactions on
Volume 10,
Issue 6,
June 2001
Page(s):841
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851
Abstract:
We study 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
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