Visual data denoising with a unified Schatten-p norm and ℓq norm regularized principal component pursuit

作者：

Highlights：

• We propose a (p; q-PCP) model for low-rank and sparse matrix recovery.

• A new Proximal Iteratively Reweighted Algorithm is presented to solve the problem.

• We show that our solutions can approximate the original problem.

• Experiments on a variety of datasets demonstrate the superiority of our approach.

摘要

Highlights•We propose a (p; q-PCP) model for low-rank and sparse matrix recovery.•A new Proximal Iteratively Reweighted Algorithm is presented to solve the problem.•We show that our solutions can approximate the original problem.•Experiments on a variety of datasets demonstrate the superiority of our approach.

论文关键词：Image processing,Denoising,Robust principal component analysis,Schatten-p norm,ℓq norm

论文评审过程：Received 22 August 2014, Revised 8 December 2014, Accepted 24 January 2015, Available online 12 February 2015, Version of Record 17 June 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.01.024