Removing mixed noise in low rank textures by convex optimization

作     者：Xiao Liang 

作者机构：Institute for Advanced Study Tsinghua University 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2016年第2卷第3期

页      面：267-276页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：This article is published with open access at Springerlink.com Xiao Liang is currently a Ph.D. student in computer science and technology at the Institute for Advanced Study in Tsinghua University  Beijing  China. Her adviser is Prof. Harry Shum. She received her B.E. degree in electronic engineering from Tsinghua University. During her study  she interned at Microsoft Research Asia for over four years. Her research interests include texture processing  3D computer vision and sparsity  and low rank matrix recovery. Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)  which permits unrestricted use  distribution  and reproduction in any medium  provided you give appropriate credit to the original author(s) and the source  provide a link to the Creative Commons license  and indicate if changes were made. Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript  please go to https://www. editorialmanager.com/cvmj 

主　　题：image denoising low rank texture total variation convex optimization augmented Lagrangian method 

摘      要：This paper introduces a new low rank texture image denoising algorithm, which can restore low rank texture contaminated by both Gaussian and salt-and-pepper noise. The algorithm formulates texture image denoising in terms of solving a low rank matrix optimization problem. Simply assuming low rank is insufficient to describe the properties of natural images, causing high noise amplitudes which lead to unsatisfactory denoising results or serious loss of image details. Thus, in addition to the low rank assumption,the continuity of natural images is also assumed by the algorithm, by adding a total variation regularizer to the optimization objective function. We further give an effective algorithm to solve this optimization problem. By combining the low rank and continuity assumptions, the proposed algorithm overcomes the deficiencies of using either the low rank assumption or total variation regularization alone. Experiments show that our algorithm can effectively remove mixed noise in low rank texture images, and is better than existing algorithms in both its subjective visual effects and in terms of quantitative objective measures.