General nonconvex total variation and low-rank regularizations: Model, algorithm and applications

作者：

Highlights：

• We propose a new algorithm for the general nonconvex TV+LR model and prove the convergence.

• The algorithm enjoys the advantage of just minimizing convex subproblems in each iteration. No inner loops are needed andthen it is easy to program the algorithm.

• A warm-up technique is used to accelerate the algorithm.

摘要

•We propose a new algorithm for the general nonconvex TV+LR model and prove the convergence.•The algorithm enjoys the advantage of just minimizing convex subproblems in each iteration. No inner loops are needed andthen it is easy to program the algorithm.•A warm-up technique is used to accelerate the algorithm.

论文关键词：Low-Rank,Total Variation,Nonconvex and nonsmooth minimization,Regularization,image restoration

论文评审过程：Received 1 July 2021, Revised 22 February 2022, Accepted 3 April 2022, Available online 5 April 2022, Version of Record 21 May 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108692