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Blind deconvolution using bilateral total variation regularization: a theoretical study and application
Abstract : Blind image deconvolution recovers a deblurred image and the blur kernel from a blurred image. From a mathematical point of view, this is a strongly ill-posed problem and several works have been proposed to address it. One successful approach proposed by Chan and Wong, consists in using the total variation (TV) as a regularization for both the image and the kernel. These authors also introduced an Alternating Minimization (AM) algorithm in order to compute a physical solution. Unfortunately, Chanâs approach suffers in particular from the ringing and staircasing effects produced by the TV regularization. To address these problems, we propose a new model based on Bilateral Total Variation (BTV) regularization of the sharp image keeping the same regularization for the kernel. We prove the existence of a minimizer of a proposed variational problem in a suitable space using a relaxation process. We also propose an AM algorithm based on our model. The efficiency and robustness of our model are illustrated and compared with the TV method through numerical simulations.
https://hal-enpc.archives-ouvertes.fr/hal-03177198
Contributeur : Mohammed El Rhabi <>
Soumis le : mardi 23 mars 2021 - 07:22:16
Dernière modification le : mardi 1 juin 2021 - 11:56:16
Archivage à long terme le : : jeudi 24 juin 2021 - 18:08:11
Contributeur : Mohammed El Rhabi <>
Soumis le : mardi 23 mars 2021 - 07:22:16
Dernière modification le : mardi 1 juin 2021 - 11:56:16
Archivage à long terme le : : jeudi 24 juin 2021 - 18:08:11
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