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A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
Abstract : Non-local 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 non-local variations, jointly for the different components, through various mixed matrix-norms. To facilitate the choice of the hyper-parameters, 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 thanks to 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 non-local structure tensor 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.
Cited literature [109 references]
https://hal.archives-ouvertes.fr/hal-01372564
Contributor : Giovanni Chierchia Connect in order to contact the contributor
Submitted on : Tuesday, September 27, 2016 - 4:46:34 PM
Last modification on : Saturday, January 15, 2022 - 3:57:39 AM
Contributor : Giovanni Chierchia Connect in order to contact the contributor
Submitted on : Tuesday, September 27, 2016 - 4:46:34 PM
Last modification on : Saturday, January 15, 2022 - 3:57:39 AM
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