Complexity-regularized image denoising
We study a new approach to image denoising based on complexity regularization, This technique presents a flexible alternative to the more conventional l(2), l(1), 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.
regularization rate-distortion optimization image compression image restoration minimum description length principle wavelets