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摘要
In this paper, a simple primal-dual method named PDL is proposed for a convex concave saddle problem and applied to total variational image deblurring. Introduction of linear mapping on proximal term relaxes convergence requirement on pairwise primal-dual stepsize. Simple proof is presented for 0(1/N) convergence rate in ergodic sense. Experiments show that performance of PDL is comparable with proximal PDHG (Zhu et al., 2010; Bonettini and Ruggiero, 2012) and PDCP (Chambolle and Pock, 2011) on Gaussian or Salt-Pepper noisy image deblurring.
- 出版日期2017-1
- 单位华侨大学
