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Alternative design of DeepPDNet in the context of image restoration

2022-02-20 13:15:39
Mingyuan Jiu, Nelly Pustelnik
arXiv_CV
       
Abstract

This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations when specified for minimizing a sum of a $\ell_2$-norm data-term and an analysis sparse prior. The parameters of our network are the step-sizes of the Chambolle-Pock scheme and the linear operator involved in sparsity-based penalization, including implicitly the regularization parameter. A backpropagation procedure is fully described. Preliminary experiments illustrate the good behavior of such a deep primal-dual network in the context of image restoration on BSD68 database.

Abstract (translated)

URL

https://arxiv.org/abs/2202.09810

PDF

https://arxiv.org/pdf/2202.09810.pdf

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