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|a eng
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| 100 |
1 |
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|a Chierchia, Giovanni
|e verfasserin
|4 aut
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| 245 |
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|a A nonlocal structure tensor-based approach for multicomponent image recovery problems
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| 264 |
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|c 2014
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 30.03.2015
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| 500 |
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|a Date Revised 30.01.2015
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Nonlocal total variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the structure tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the nonlocal variations, jointly for the different components, through various l(1, p)-matrix-norms with p ≥ 1. To facilitate the choice of the hyperparameters, we adopt a constrained convex optimization approach in which we minimize the data fidelity term subject to a constraint involving the ST-NLTV regularization. The resulting convex optimization problem is solved with a novel epigraphical projection method. This formulation can be efficiently implemented because of the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for color, multispectral, and hyperspectral images. The results demonstrate the interest of introducing a nonlocal ST regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods, such as the alternating direction method of multipliers
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Pustelnik, Nelly
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pesquet-Popescu, Béatrice
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pesquet, Jean-Christophe
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 23(2014), 12 vom: 16. Dez., Seite 5531-44
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|x 1941-0042
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|g volume:23
|g year:2014
|g number:12
|g day:16
|g month:12
|g pages:5531-44
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|d 23
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|h 5531-44
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