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An epigraphical convex optimization approach for multicomponent image restoration using non-local structure tensor

Abstract : TV-like constraints/regularizations are useful tools in variational methods for multicomponent image restoration. In this paper, we design more sophisticated non-local TV constraints which are derived from the structure tensor. The proposed approach allows us to measure the non-local variations, jointly for the different components, through various ℓ_1,p matrix norms with p >= 1. The related convex constrained optimization problems are solved through a novel epigraphical projection method. This formulation can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments carried out for color images demonstrate the interest of considering a Non-Local Structure Tensor TV and show that the proposed epigraphical projection method leads to significant improvements in terms of convergence speed over existing numerical solutions.
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https://hal.archives-ouvertes.fr/hal-00826003
Contributor : Giovanni Chierchia <>
Submitted on : Tuesday, May 28, 2013 - 4:34:36 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:06 PM
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  • HAL Id : hal-00826003, version 1

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Giovanni Chierchia, Nelly Pustelnik, Jean-Christophe Pesquet, Béatrice Pesquet-Popescu. An epigraphical convex optimization approach for multicomponent image restoration using non-local structure tensor. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, May 2013, Canada. ⟨hal-00826003⟩

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