[1]徐久成,王楠,王煜尧,等.基于非凸加权Lp范数稀疏误差约束的图像去噪算法[J].智能系统学报,2019,14(03):500-507.[doi:10.11992/tis.201804057]
 XU Jiucheng,WANG Nan,WANG Yuyao,et al.Non-convex weighted-Lp-norm sparse-error constraint for image denoising[J].CAAI Transactions on Intelligent Systems,2019,14(03):500-507.[doi:10.11992/tis.201804057]
点击复制

基于非凸加权Lp范数稀疏误差约束的图像去噪算法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
500-507
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
Non-convex weighted-Lp-norm sparse-error constraint for image denoising
作者:
徐久成12 王楠12 王煜尧12 徐战威12
1. 河南师范大学 计算机与信息工程学院, 河南 新乡 453007;
2. 河南省高校计算智能与数据挖掘工程技术研究中心, 河南 新乡 453007
Author(s):
XU Jiucheng12 WANG Nan12 WANG Yuyao12 XU Zhanwei12
1. College of Computer and Information Engineering, He’nan Normal University, Xinxiang 453007, China;
2. Engineering Technology Research Center for Computing Intelligence and Data Mining in Colleges and University of He’nan Province, Xinxiang 453007, C
关键词:
图像去噪稀疏表示稀疏系数先验知识l1范数非凸加权lp范数稀疏误差约束峰值信噪比
Keywords:
image denoisingsparse representationsparse coefficientprior knowledgel1 normnon-convex weighted lp normsparse error constraintpeak signal-to-noise ratio
分类号:
TP391
DOI:
10.11992/tis.201804057
摘要:
图像去噪过程中由于噪声的影响,无法学习到准确的先验知识,因此难以获取较优的稀疏系数。针对该问题,本文提出一种基于非凸加权lp范数稀疏误差约束的图像去噪算法。该算法将系数求解过程分解为两个子问题,采用广义软阈值算法求解lp范数中的稀疏系数,再利用代理算法求解稀疏误差约束中的稀疏系数,根据二者的均值来获取更具鲁棒性的稀疏系数。与当前几种典型的算法进行对比分析,实验结果表明:本文算法不仅具有更高的峰值信噪比(PSNR),而且在运行时间上具有更高的效率,同时在视觉角度上产生了更好的视觉感受。
Abstract:
Due to noise during image denoising, it is difficult to learn accurate prior knowledge. Therefore, obtaining a desirable sparse coefficient proves to be difficult. To solve this problem, this paper proposes an image denoising method based on the non-convex weighted-lp-norm sparse-error constraint. This algorithm decomposes the coefficient-solving process into two sub-problems. First, the algorithm solves the sparse coefficient in the lp norm by the generalized soft threshold value algorithm and then uses the surrogate algorithm to solve the sparse coefficient in the sparse-error constraint. Finally, the algorithm obtains a robust sparse coefficient according to its average value. The experimental results show that the proposed algorithm features a high peak signal-to-noise ratio and high efficiency in terms of the running time. Simultaneously, a desirable visual perception is obtained.

参考文献/References:

[1] CHAMBOLLE A. An algorithm for total variation minimization and applications[J]. Journal of mathematical imaging and vision, 2004, 20:89-97.
[2] 邓承志. 图像稀疏表示理论及其应用研究[D]. 武汉:华中科技大学, 2008. DENG Chengzhi. Research on image sparse representation theory and its applications[D]. Wuhan:Huazhong University of Science and Technology, 2008.
[3] 练秋生, 张伟. 基于图像块分类稀疏表示的超分辨率重构算法[J]. 电子学报, 2012, 40(5):920-925 LIAN Qiusheng, ZHANG Wei. Image super-resolution algorithms based on sparse representation of classified image patches[J]. Acta electronica sinica, 2012, 40(5):920-925
[4] DAI Tao, XU Zhiya, LIANG Haoyi, et al. A generic denoising framework via guided principal component analysis[J]. Journal of visual communication and image representation, 2017, 48:340-352.
[5] 郝红侠, 刘芳, 焦李成, 等. 采用结构自适应窗的非局部均值图像去噪算法[J]. 西安交通大学学报, 2013, 47(12):71-76 HAO Hongxia, LIU Fang, JIAO Licheng, et al. A non-local means algorithm for image denoising using structure adaptive window[J]. Journal of Xi’an jiaotong university, 2013, 47(12):71-76
[6] ZUO Chenglin, JOVANOV L, GOOSSENS B, et al. Image denoising using quadtree-based nonlocal means with locally adaptive principal component analysis[J]. IEEE signal processing letters, 2016, 23(4):434-438.
[7] ZHANG Kai, ZUO Wangmeng, CHEN Yunjin, et al. Beyond a gaussian denoiser:residual learning of deep cnn for image denoising[J]. IEEE transactions on image processing, 2017, 26(7):3142-3155.
[8] 陈轶鸣, 夏景明, 陈轶才, 等. 结合稀疏表示与神经网络的医学图像融合[J]. 河南科技大学学报(自然科学版), 2018, 39(2):40-47 CHEN Yiming, XIA Jingming, CHEN Yicai, et al. Medical image fusion combining sparse representation and neural network[J]. Journal of Henan University of Science and Technology (natural science), 2018, 39(2):40-47
[9] 解凯, 张芬. 基于过完备表示的图像去噪算法[J]. 电子学报, 2013, 41(10):1911-1916 XIE Kai, ZHANG Fen. Overcomplete representation base image denoising algorithm[J]. Acta electronica sinica, 2013, 41(10):1911-1916
[10] BUDIANTO, LUN D P. Robust fringe projection profilometry via sparse representation[J]. IEEE transactions on image processing, 2016, 25(4):1726-1739.
[11] ZHANG Jian, ZHAO Debin, GAO Wen. Group-based sparse representation for image restoration[J]. IEEE transactions on image processing, 2014, 23(8):3336-3351.
[12] 占美全, 邓志良. 基于L1范数的总变分正则化超分辨率图像重建[J]. 科学技术与工程, 2010, 10(28):6903-6906 ZHAN Meiquan, DENG Zhiliang. L1 norm of total variation regularization based super resolution reconstruction for images[J]. Science technology and engineering, 2010, 10(28):6903-6906
[13] 杨平先, 陈明举. 一种基于L1范数的非局部变分图像复原模型[J]. 液晶与显示, 2017, 32(8):635-641 YANG Pingxian, CHEN Mingju. A nonlocal total variation based on L1 norm for image recovery[J]. Chinese journal of liquid crystals and displays, 2017, 32(8):635-641
[14] 张艳艳, 陈苏婷, 葛俊祥, 等. 自适应非凸稀疏正则化下自适应光学系统加性噪声的去除[J]. 物理学报, 2017, 66(12):368-375 ZHANG Yanyan, CHEN Suting, GE Junxiang, et al. Removal of additive noise in adaptive optics system based on adaptive nonconvex sparse regularization[J]. Acta physica sinica, 2017, 66(12):368-375
[15] ZHA Zhiyuan, LIU Xin, HUANG Xiaohua, et al. Analyzing the group sparsity based on the rank minimization methods[C]//Proceedings of IEEE International Conference on Multimedia and Expo. Hong Kong, China, 2017:883-888.
[16] ZUO Wangmeng, MENG Deyu, ZHANG Lei, et al. A generalized iterated shrinkage algorithm for non-convex sparse coding[C]//Proceedings of IEEE International Conference on Computer Vision. Sydney, NSW, Australia, 2013:217-224.
[17] ZHANG Xiaoqun, BURGER M, BRESSON X, et al. Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J]. SIAM journal on imaging sciences, 2010, 3(3):253-276.
[18] DONG Weisheng, ZHANG Lei, SHI Guangming, et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE transactions on image processing, 2013, 22(4):1620-1630.
[19] GU Shuhang, XIE Qi, MENG Deyu, et al. Weighted nuclear norm minimization and its applications to low level vision[J]. International journal of computer vision, 2017, 121(2):183-208.
[20] LUO Enming, CHAN S H, NGUYEN T Q. Adaptive image denoising by mixture adaptation[J]. IEEE transactions on image processing, 2016, 25(10):4489-4503.
[21] LIU Hangfan, XIONG Ruiqin, ZHANG Jian, et al. Image denoising via adaptive soft-thresholding based on non-local samples[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA, 2015:484-492.
[22] PAPYAN V, ELAD M. Multi-scale patch-based image restoration[J]. IEEE transactions on image processing, 2016, 25(1):249-261.

相似文献/References:

[1]王科俊,杨晓飞.采用改进结构特征度度量分析的噪声标准差估计算法[J].智能系统学报,2015,10(02):255.[doi:10.3969/j.issn.1673-4785.201511015]
 WANG Kejun,YANG Xiaofei.The contourlet denoising algorithm based on modified noise variance estimation[J].CAAI Transactions on Intelligent Systems,2015,10(03):255.[doi:10.3969/j.issn.1673-4785.201511015]
[2]李俊泽,袁小芳,张振军,等.一种基于二维GARCH模型的图像去噪方法[J].智能系统学报,2015,10(01):62.[doi:10.3969/j.issn.1673-4785.201403066]
 LI Junze,YUAN Xiaofang,ZHANG Zhenjun,et al.A method of image denoising based on two-dimensional GARCH model[J].CAAI Transactions on Intelligent Systems,2015,10(03):62.[doi:10.3969/j.issn.1673-4785.201403066]
[3]张佳骕,蒋亦樟,王士同.基于特征选择聚类方法的稀疏TSK模糊系统[J].智能系统学报,2015,10(04):583.[doi:10.3969/j.issn.1673-4785.201412001]
 ZHANG Jiasu,JIANG Yizhang,WANG Shitong.Sparse TSK fuzzy system based on feature selection clustering method[J].CAAI Transactions on Intelligent Systems,2015,10(03):583.[doi:10.3969/j.issn.1673-4785.201412001]
[4]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
 MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11(03):279.[doi:10.11992/tis.201603026]
[5]赵志辉,赵瑞珍,岑翼刚,等.基于稀疏表示与线性回归的图像快速超分辨率重建[J].智能系统学报,2017,12(01):8.[doi:10.11992/tis.201603039]
 ZHAO Zhihui,ZHAO Ruizhen,CEN Yigang,et al.Rapid super-resolution image reconstruction based on sparse representation and linear regression[J].CAAI Transactions on Intelligent Systems,2017,12(03):8.[doi:10.11992/tis.201603039]
[6]马忠丽,刘权勇,武凌羽,等.一种基于联合表示的图像分类方法[J].智能系统学报,2018,13(02):220.[doi:10.11992/tis.201611036]
 MA Zhongli,LIU Quanyong,WU Lingyu,et al.Syncretic representation method for image classification[J].CAAI Transactions on Intelligent Systems,2018,13(03):220.[doi:10.11992/tis.201611036]
[7]赵晓晓,周治平.结合稀疏表示与约束传递的半监督谱聚类算法[J].智能系统学报,2018,13(05):855.[doi:10.11992/tis.201703013]
 ZHAO Xiaoxiao,ZHOU Zhiping.A semi-supervised spectral clustering algorithm combined with sparse representation and constraint propagation[J].CAAI Transactions on Intelligent Systems,2018,13(03):855.[doi:10.11992/tis.201703013]
[8]孙必慎,石武祯,姜峰.计算视觉核心问题:自然图像先验建模研究综述[J].智能系统学报,2019,14(01):71.[doi:10.11992/tis.201804019]
 SUN Bishen,SHI Wuzhen,JIANG Feng.Core problem in computer vision: survey of natural image prior models[J].CAAI Transactions on Intelligent Systems,2019,14(03):71.[doi:10.11992/tis.201804019]

备注/Memo

备注/Memo:
收稿日期:2018-04-26。
基金项目:国家自然科学基金项目(61370169,61402153);河南省科技攻关重点项目(142102210056,162102210261);河南省高等学校重点科研项目(16A520057).
作者简介:徐久成,男,1963年生,教授,中国计算机学会副理事长,主要研究方向为粒计算、粗糙集、数据挖掘和生物信息学。先后主持和参加国家级及省部级项目10余项,其中主持国家自然科学基金项目3项。发表学术论文120余篇;王楠,女,1993年生,硕士研究生,主要研究方向为机器学习、计算机视觉;王煜尧,男,1994年生,硕士研究生,主要研究方向为机器学习、计算机视觉。
通讯作者:王楠.E-mail:190606759@qq.com
更新日期/Last Update: 1900-01-01