Abstract
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 algorithms, simplified regularization parameter selection), the non-convex penalties are chosen so as to ensure the convexity of the total objective function. A computationally efficient, fast converging algorithm is derived.
Original language | English (US) |
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Article number | 7047778 |
Pages (from-to) | 1364-1368 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 22 |
Issue number | 9 |
DOIs | |
State | Published - Sep 1 2015 |
Keywords
- Convex optimization
- non-convex regularization
- total variation denoising
- wavelet denoising
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics