Artifact-Free Wavelet Denoising: Non-convex Sparse Regularization, Convex Optimization

PROJECT TITLE:

Artifact-Free Wavelet Denoising: Non-convex Sparse Regularization, Convex Optimization

ABSTRACT:

Algorithms for signal denoising that combine wavelet-domain sparsity and total variation (TV) regularization are comparatively free of artifacts, like pseudo-Gibbs oscillations, normally introduced by pure wavelet thresholding. This paper formulates wavelet-TV (WATV) denoising as a unified downside. To strongly induce wavelet sparsity, the proposed approach uses non-convex penalty functions. At the identical time, in order to draw on the benefits of convex optimization (distinctive minimum, reliable algorithms, simplified regularization parameter choice), the non-convex penalties are chosen thus as to make sure the convexity of the total objective operate. A computationally efficient, fast converging algorithm is derived.

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