Unknown noise removal via sparse representation model

Junchao Zhang; Haibo Luo; Bin Hui; Zheng Chang; Xiangyue Zhang

doi:10.1016/j.isatra.2019.03.028

Unknown noise removal via sparse representation model

ISA Trans. 2019 Nov:94:135-143. doi: 10.1016/j.isatra.2019.03.028. Epub 2019 Mar 26.

Authors

Junchao Zhang¹, Haibo Luo², Bin Hui², Zheng Chang², Xiangyue Zhang³

Affiliations

¹ Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China; The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China. Electronic address: zhangjunchao@sia.cn.
² Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China; The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China.
³ Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China; The Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China.

PMID: 30929808
DOI: 10.1016/j.isatra.2019.03.028

Abstract

To remove more complex or unknown noise, we propose a new dictionary learning model by assuming noise as Mixture of Gaussian (MoG) distributions. Since MoG is able to approximate any continuous distributions universally, the proposed method can effectively recover the original image from the corrupted one with various forms of noise. Meanwhile, to solve weighted ℓ₂-ℓ₀ minimization problems, we further propose modified orthogonal matching pursuit method in sparse coding and extend alternating proximal algorithm to update dictionaries. Experimental results demonstrate that our proposed method is superior to several previous denoising methods in terms of quantitative measures and visual quality.

Keywords: Dictionary learning; Image denoising; Mixed noise; Sparse representation.