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Dual constrained TV-based regularization
Abstract : Algorithms based on the minimization of the Total Variation are prevalent in computer vision. They are used in a variety of applications such as image denoising, compressive sensing and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa-Osher's projection algorithms for TV minimization in a flexible graph data representation by generalizing the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of a fast parallel proximal algorithm, which performs better than the classical TV approach for denoising, and is also applicable to inverse problems such as image deblurring.
Cited literature [11 references]
https://hal-upec-upem.archives-ouvertes.fr/hal-00744071
Contributor : Laurent Najman Connect in order to contact the contributor
Submitted on : Monday, October 22, 2012 - 11:44:19 AM
Last modification on : Saturday, January 15, 2022 - 3:59:29 AM
Long-term archiving on: : Saturday, December 17, 2016 - 3:02:16 AM
Contributor : Laurent Najman Connect in order to contact the contributor
Submitted on : Monday, October 22, 2012 - 11:44:19 AM
Last modification on : Saturday, January 15, 2022 - 3:59:29 AM
Long-term archiving on: : Saturday, December 17, 2016 - 3:02:16 AM
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DCTV-ICASSP.pdf
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