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Complexity-regularized image denoising

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2 Author(s)
Juan Liu ; Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA ; Moulin, P.

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

Published in:

Image Processing, IEEE Transactions on  (Volume:10 ,  Issue: 6 )