Computer Science ›› 2020, Vol. 47 ›› Issue (1): 176-185.doi: 10.11896/jsjkx.181202280

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Image Denoising Algorithm Based on Adaptive Matching Pursuit

LI Gui-hui,LI Jin-jiang,FAN Hui   

  1. (School of Computer Science and Technology,Shandong Technology and Business University,Yantai,Shandong 264000,China);
    (Co-innovation Center of Shandong Colleges and Universities:Future Intelligent Computing,Yantai,Shandong 264000,China)
  • Received:2018-12-09 Published:2020-01-19
  • About author:LI Gui-hui,born in 1991,postgraduate,Ph.D supervisor.Her main research interests include graphic image processing and machine learning;LI Jin-jiang,born in 1978,Ph.D,professor,postgraduate supervisor, is Member of China Computer Federation (CCF).His main research interests include graphic image processing, computer vision and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61472227,61772319,61602277).

Abstract: Aiming at the problem that the current sparse denoising algorithm has low decomposition efficiency and unsatisfactory denoising effect,an image denoising algorithm based on adaptive matching pursuit was proposed.Firstly,the algorithm uses the adaptive matching pursuit algorithm to solve the sparse coefficients,and then uses the K-means singular value decomposition algorithm to train the dictionary into an adaptive dictionary that can effectively reflect the image structure features.Finally,theima-ge is reconstructed by combining the sparse coefficient with the adaptive dictionary.During the reconstruction process,the coefficients corresponding to the noise are removed,and finally the denoising effect is achieved.Spike-Slab priori is introduced to guide the sparsity of sparse coefficient matrix,and two weight matrices are used to make the denoising model more realistic.In view of the importance of dictionary in sparse algorithm,this paper compared adaptive dictionary with DCT redundant dictionary and Global dictionary.The experimental results show that the denoising result of adaptive dictionary is about 4.5 dB higher than that of traditional dictionary in terms of peak signal-to-noise ratio (PSNR).The proposed method improves three evaluation indicators in varying degrees compared with the current six main methods of sparse denoising.The PSNR is increased by about 0.76dB to 6.24 dB,the feature similarity (FSIM) is increased by about 0.012 to 0.082,and the structure similarity (SSIM) is increased by about 0.015 to 0.108 on average.The qualitative evaluation of the image denoising algorithm shows that the proposed algorithm retains more useful information and has the best visual effect.Therefore,the experiment fully proves its effectiveness and robustness.

Key words: Image denoising, Sparse representation, Adaptive matching pursuit, K-means singular value decomposition, Spike-Slab priori

CLC Number: 

  • TP391.41
[1]JUBAIR I,RAHMAN M,ASHFAQUEUDDIN S,et al.An enhanced decision based adaptive median filtering technique to remove Salt and Pepper noise in digital images[C]∥International Conference on Computer & Information Technology.Dhaka:IEEE press,2011:428-433.
[2]NAVEED K,SHAUKAT B,REHMAN N U.Signal denoising based on dual tree complex wavelet transform and goodness of fit test[C]∥International Conference on Digital Signal Proces-sing.London:IEEE,2017:1-5.
[3]YAGAN A C,OZGEN M T.A spectral graph wiener filter in graph fourier domain for improved image denoising[C]∥2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).Washington:IEEE,2016:450-454.
[4]DAS S L,NACHIAPPAN A.Role of hybrid switching filter in image denoising - a comparative study[C]∥2012 Annual IEEE India Conference (INDICON).Kochi:IEEE,2012:1180-1183.
[5]CAO Y,LUO Y P,YANG S Y.Hybrid Linear Model Based Ima- ge Denoising[J].Chinese Journal of Computers,2009,32(11):2260-2264.
[6]RAJA H,BAJWA W U.Cloud K-SVD:A Collaborative Dictionary Learning Algorithm for Big,Distributed Data[J].IEEE Transactions on Signal Processing,2016,64(1):173-188.
[7]JIA L N,SONG S T,YAO L H,et al.Image Denoising via Sparse Representation over Grouped Dictionaries with Adaptive Atom Size[J].IEEE Access,2017,5:22514-22529.
[8]LU C,SHI J,JIA J.Scale Adaptive Dictionary Learning[J]. IEEE Transactions on Image Processing,2014,23(2):837-847.
[9]ROMANO Y,ELAD M.Patch-Disagreement as a Way to Improve K-SVD Denoising[C]∥IEEE International Conference on Acoustics.Brisbane:IEEE,2015:1280-1284.
[10]ENGAN K,AASE S O ,HUSOY J H.Method of optimal directions for frame design[C]∥IEEE International Conference on Acoustics,Speech,and Signal Processing.USA:IEEE,1999:2443-2446.
[11]AHARON M,ELAD M,BRUCKSTEIN A.MYMrm KMYM-SVD:An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[12]MALLAT S G,ZHANG Z.Matching pursuits with time-frequency dictionaries[J].IEEE Transactions on Signal Processing,1993,41(12):3397-3415.
[13]TROPP J A,GILBERT A C.Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit[J].IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[14]DONOHO D L,TSAIG Y,DRORI I,et al.Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit[J].IEEE Transactions on Information Theory,2012,58(2):1094-1121.
[15]TANG L,CHEN M J.Image Denoising Method Using the Gradient Matching Pursuit[J].Mathematical Modelling of Engineering Problems,2016,3(2):53-56.
[16]DENG X,LIU Z.Image denoising based on steepest descent OMP and K-SVD[C]∥IEEE International Conference on Signal Processing.Ningbo:IEEE,2015:1-5.
[17]LI S,FANG L.Signal Denoising With Random Refined Orthog- onal Matching Pursuit[J].IEEE Transactions on Instrumentation and Measurement,2012,61(1):26-34.
[18]DO T T,GAN L,NGUYEN N,et al.Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]∥Conference on Signals,Systems & Computers.Pacific Grove:IEEE,2008:581-587.
[19]YUAN S,WANG S,MA M,et al.Sparse Bayesian Learning-Based Time-Variant Deconvolution[J].IEEE Transactions on Geoscience & Remote Sensing,2017,55(11):6182-6194.
[20]JIN M,ROTH S,FAVARO P.Noise-Blind Image Deblurring
[C]∥IEEE Conference on Computer Vision & Pattern Recognition.Honolulu:IEEE,2017:3834-3842.
[21]VU T H,MOUSAVI H S,MONGA V.Adaptive matching pursuit for sparse signal recovery[C]∥IEEE International Conference on Acoustics.New Orleans:IEEE,2017:4331-4335.
[22]OLIVA G,SETOLA R,HADJICOSTIS C N.Distributed asynchronous Cholesky decomposition[C]∥Decision & Control.Las Vegas:IEEE,2016:4414-4419.
[23]MITCHELL T J,BEAUCHAMP J J.Bayesian Variable Selection in Linear Regression [J].Publications of the American Statistical Association,1988,83(404):1023-1032.
[24]GEORGE E,MCCULLOCH R.Variable Selection via Gibbs Sampling[J].Publications of the American Statistical Association,1993,88(423):881-889.
[25]CHEN B,PAISLEY J W,CARIN L.Sparse linear regression with beta process priors[C]∥IEEE International Conference on Acoustics Speech & Signal Processing.Dallas:IEEE,2010:1234-1237.
[26]ZHUANG P X,HUANG Y,ZENG D L,et al.Mixed noise removal based on a novel non-parametric Bayesian sparse outlier model[J].Neurocomputing,2016,174(PB):858-865.
[27]DING X H,MI Z Y,HUANG Y,et al.Robust rvm based on spike-slab prior[J].Journal of Electronics (China),2012,29(6):593-597.
[28]WANG Z,BOVIK A C,SHEIKH H R,et al.Image Quality Assessment:From Error Visibility to Structural Similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.
[29]ZHANG L,ZHANG L,MOU X Q,et al.FSIM:A Feature Similarity Index for Image Quality Assessment[J].IEEE Transactions on Image Processing,2011,20(8):2378-2386.
[1] TIAN Xu, CHANG Kan, HUANG Sheng, QIN Tuan-fa. Single Image Super-resolution Algorithm Using Residual Dictionary and Collaborative Representation [J]. Computer Science, 2020, 47(9): 135-141.
[2] CHENG Zhong-Jian, ZHOU Shuang-e and LI Kang. Sparse Representation Target Tracking Algorithm Based on Multi-scale Adaptive Weight [J]. Computer Science, 2020, 47(6A): 181-186.
[3] WU Qing-hong, GAO Xiao-dong. Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine [J]. Computer Science, 2020, 47(6): 121-125.
[4] CAO Yi-qin, XIE Shu-hui. Category-specific Image Denoising Algorithm Based on Grid Search [J]. Computer Science, 2020, 47(11): 168-173.
[5] LI Xiao-yu,GAO Qing-wei,LU Yi-xiang,SUN Dong. Image Fusion Method Based on Image Energy Adjustment [J]. Computer Science, 2020, 47(1): 153-158.
[6] ZHANG Bing, XIE Cong-hua, LIU Zhe. Multi-focus Image Fusion Based on Latent Sparse Representation and Neighborhood Information [J]. Computer Science, 2019, 46(9): 254-258.
[7] SONG Xiao-xiang,GUO Yan,LI Ning,YU Dong-ping. Missing Data Prediction Algorithm Based on Sparse Bayesian Learning in Coevolving Time Series [J]. Computer Science, 2019, 46(7): 217-223.
[8] ZHANG Fu-wang, YUAN Hui-juan. Image Super-resolution Reconstruction Algorithm with Adaptive Sparse Representationand Non-local Self-similarity [J]. Computer Science, 2019, 46(6A): 188-191.
[9] XIAO Jia, ZHANG Jun-hua, MEI Li-ye. Improved Block-matching 3D Denoising Algorithm [J]. Computer Science, 2019, 46(6): 288-294.
[10] DU Xiu-li, ZUO Si-ming, QIU Shao-ming. Adaptive Dictionary Learning Algorithm Based on Image Gray Entropy [J]. Computer Science, 2019, 46(5): 266-271.
[11] RU Feng, XU Jin, CHANG Qi, KAN Dan-hui. High Order Statistics Structured Sparse Algorithm for Image Genetic Association Analysis [J]. Computer Science, 2019, 46(4): 66-72.
[12] MAO Xia, WANG Lan, LI Jian-jun. Human Action Recognition Framework with RGB-D Features Fusion [J]. Computer Science, 2018, 45(8): 22-27.
[13] GAN Ling, ZHAO Fu-chao, YANG Meng. Self-adaptive Group Sparse Representation Method for Image Inpainting [J]. Computer Science, 2018, 45(8): 272-276.
[14] JIA Xu, SUN Fu-ming, LI Hao-jie, CAO Yu-dong. Vein Recognition Algorithm Based on Supervised NMF with Two Regularization Terms [J]. Computer Science, 2018, 45(8): 283-287.
[15] XU Shao-ping, ZENG Xiao-xia ,JIANG Yin-nan ,LIN Guan-xi ,TANG Yi-ling. Fast Noise Level Estimation Algorithm Based on Nonlinear Rectification of Smallest Eigenvalue [J]. Computer Science, 2018, 45(7): 219-225.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .