[1]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(3):500-507.[doi:10.11992/tis.201804057]
Copy

Non-convex weighted-Lp-norm sparse-error constraint for image denoising

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 3 Page number: 500-507 Column: 学术论文—机器感知与模式识别 Public date: 2019-05-05
Title:
Non-convex weighted-Lp-norm sparse-error constraint for image denoising
Author(s):
XU Jiucheng1; 2;  WANG Nan1; 2;  WANG Yuyao1; 2;  XU Zhanwei1; 2
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
Keywords:
image denoising; sparse representation; sparse coefficient; prior knowledge; l1 norm; non-convex weighted lp norm; sparse error constraint; peak signal-to-noise ratio
CLC:
TP391
DOI:
10.11992/tis.201804057
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.
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.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems