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

@article{Ding2015ArtifactFreeWD,
  title={Artifact-Free Wavelet Denoising: Non-convex Sparse Regularization, Convex Optimization},
  author={Yin Ding and Ivan W. Selesnick},
  journal={IEEE Signal Processing Letters},
  year={2015},
  volume={22},
  pages={1364-1368}
}
Algorithms for signal denoising that combine wavelet-domain sparsity and total variation (TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations, normally introduced by pure wavelet thresholding. This paper formulates wavelet-TV (WATV) denoising as a unified problem. To strongly induce wavelet sparsity, the proposed approach uses non-convex penalty functions. At the same time, in order to draw on the advantages of convex optimization (unique minimum, reliable… CONTINUE READING
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