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Cited by 490

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery

Combettes, Patrick L., Pesquet, Jean-Christophe·2007·IEEE Journal of Selected Topics in Signal Processing

Abstract

Under consideration is the large body of signal recovery problems that can be formulated as the problem of minimizing the sum of two (not necessarily smooth) lower semicontinuous convex functions in a real Hilbert space. This generic problem is analyzed and a decomposition method is proposed to solve it. The convergence of the method, which is based on the Douglas-Rachford algorithm for monotone operator-splitting, is obtained under general conditions. Applications to non-Gaussian image denoising in a tight frame are also demonstrated.

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Metadata

Authors
Combettes, P. L., & Pesquet, J.-C.
Venue
IEEE Journal of Selected Topics in Signal Processing
Published
2007-12
Volume
1
Issue
4
Pages
564-574
ISSN
  • 1932-4553 (ISSN-L)
  • 1941-0484
OpenAlex
W2142280715
Cited by
490
References
36
FWCI
26.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery. https://app.cochat.ai/papers/10.1109/jstsp.2007.910264
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Combettes, P. L., & Pesquet, J.-C. (2007). A Douglas–Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery. IEEE Journal of Selected Topics in Signal Processing. https://doi.org/10.1109/jstsp.2007.910264

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.