数据采集与处理2018，Vol.33Issue(1)：1-11,11.DOI:10.16337/j.1004-9037.2018.01.001
基于稀疏表示及正则约束的图像去噪方法综述
Image Denoising Based on Sparse Representation and Regularization Constraint:A Re-view
摘要
Abstract
Data denoising is a classic issue in the field of signal and image processing which has been wide-ly applied in various engineering practices.Due to the diversity of noise sources,denoising is a challeng-ing and active research topic,and a variety of classical denoising methods have been developed.In recent years,with the development of compressed sensing theory,the methods for solving inverse problem based on sparse representation and regularization constraint have become important research directions and technical approaches in the field of image denoising.This paper firstly reviews and summarizes the sources and types of image noise,and then according to the different types of image noise,gives a com-prehensive review focusing on the image denoising techniques based on sparse representation and regulari-zation constraints.In addition,we analyze and describe the principle,advantages and disadvantages of several major denoising methods.Finally,the performance evaluation of denoising algorithm is summa-rized.关键词
图像去噪/稀疏表示/字典学习/全变分/正则化约束Key words
image denoising/sparse representation/dictionary learning/total variation/regularization constraint分类
信息技术与安全科学引用本文复制引用
彭真明,陈颖频,蒲恬,王雨青,何艳敏..基于稀疏表示及正则约束的图像去噪方法综述[J].数据采集与处理,2018,33(1):1-11,11.基金项目
国家自然科学基金(61775030,61571096)资助项目 （61775030,61571096）
福建省自然科学基金(2015J01270)资助项目 （2015J01270）
福建省教育厅中青年教师教育科研基金(JAT170352)资助项目 （JAT170352）
广东省数字信号与图象处理技术重点实验室开放课题(2017GDDSIPL-01)资助项目 （2017GDDSIPL-01）
中国科学院光束控制重点实验室基金(2017LBC003)资助项目. （2017LBC003）
