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Wavelet decomposition method for L2/TV-image deblurring

Fornasier, M. ; Kim, Y. ; Langer, A. LU and Schönlieb, C. B. (2012) In SIAM Journal on Imaging Sciences 5(3). p.857-885
Abstract

In this paper, we show additional properties of the limit of a sequence produced by the subspace correction algorithm proposed by Fornasier and Schönlieb [SIAM J. Numer. Anal., 47 (2009), pp. 3397-3428 for L2/TV-minimization problems. An important but missing property of such a limiting sequence in that paper is the convergence to a minimizer of the original minimization problem, which was obtained in [M. Fornasier, A. Langer, and C.-B. Schönlieb, Numer. Math., 116 (2010), pp. 645-685 with an additional condition of overlapping subdomains. We can now determine when the limit is indeed a minimizer of the original problem. Inspired by the work of Vonesch and Unser [IEEE Trans. Image Process., 18 (2009), pp. 509-523], we adapt... (More)

In this paper, we show additional properties of the limit of a sequence produced by the subspace correction algorithm proposed by Fornasier and Schönlieb [SIAM J. Numer. Anal., 47 (2009), pp. 3397-3428 for L2/TV-minimization problems. An important but missing property of such a limiting sequence in that paper is the convergence to a minimizer of the original minimization problem, which was obtained in [M. Fornasier, A. Langer, and C.-B. Schönlieb, Numer. Math., 116 (2010), pp. 645-685 with an additional condition of overlapping subdomains. We can now determine when the limit is indeed a minimizer of the original problem. Inspired by the work of Vonesch and Unser [IEEE Trans. Image Process., 18 (2009), pp. 509-523], we adapt and specify this algorithm to the case of an orthogonal wavelet space decomposition for deblurring problems and provide an equivalence condition to the convergence of such a limiting sequence to a minimizer. We also provide a counterexample of a limiting sequence by the algorithm that does not converge to a minimizer, which shows the necessity of our analysis of the minimizing algorithm.

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author
; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Alternating minimization, Convex optimization, Image deblurring, Oblique thresholding, Total variation minimization, Wavelet decomposition method
in
SIAM Journal on Imaging Sciences
volume
5
issue
3
pages
29 pages
publisher
Society for Industrial and Applied Mathematics
external identifiers
  • scopus:84867163734
ISSN
1936-4954
DOI
10.1137/100819801
language
English
LU publication?
no
additional info
Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
id
6f6ef39c-e4b9-4008-8d1c-c9ea41e60e73
date added to LUP
2021-03-15 22:35:42
date last changed
2022-02-16 20:58:13
@article{6f6ef39c-e4b9-4008-8d1c-c9ea41e60e73,
  abstract     = {{<p>In this paper, we show additional properties of the limit of a sequence produced by the subspace correction algorithm proposed by Fornasier and Schönlieb [SIAM J. Numer. Anal., 47 (2009), pp. 3397-3428 for L<sub>2</sub>/TV-minimization problems. An important but missing property of such a limiting sequence in that paper is the convergence to a minimizer of the original minimization problem, which was obtained in [M. Fornasier, A. Langer, and C.-B. Schönlieb, Numer. Math., 116 (2010), pp. 645-685 with an additional condition of overlapping subdomains. We can now determine when the limit is indeed a minimizer of the original problem. Inspired by the work of Vonesch and Unser [IEEE Trans. Image Process., 18 (2009), pp. 509-523], we adapt and specify this algorithm to the case of an orthogonal wavelet space decomposition for deblurring problems and provide an equivalence condition to the convergence of such a limiting sequence to a minimizer. We also provide a counterexample of a limiting sequence by the algorithm that does not converge to a minimizer, which shows the necessity of our analysis of the minimizing algorithm.</p>}},
  author       = {{Fornasier, M. and Kim, Y. and Langer, A. and Schönlieb, C. B.}},
  issn         = {{1936-4954}},
  keywords     = {{Alternating minimization; Convex optimization; Image deblurring; Oblique thresholding; Total variation minimization; Wavelet decomposition method}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{857--885}},
  publisher    = {{Society for Industrial and Applied Mathematics}},
  series       = {{SIAM Journal on Imaging Sciences}},
  title        = {{Wavelet decomposition method for L<sub>2</sub>/TV-image deblurring}},
  url          = {{http://dx.doi.org/10.1137/100819801}},
  doi          = {{10.1137/100819801}},
  volume       = {{5}},
  year         = {{2012}},
}