Computer Science ›› 2018, Vol. 45 ›› Issue (2): 94-97, 134.doi: 10.11896/j.issn.1002-137X.2018.02.016

Previous Articles     Next Articles

Sparsity-adaptive Image Denoising Algorithm Based on Difference Coefficient

JIAO Li-juan and WANG Wen-jian   

  • Online:2018-02-15 Published:2018-11-13

Abstract: With the remarkable adaptability and the details recovery capability,K-SVD is a highly effective method based on sparse representation theory in image denoising.But the sparsity K should be given in advance,and different images have different K values in fact.On the other hand,pursuit algorithms which are used in evaluating the relevance between vectors of an image by calculating vector inner product,are brought into K-SVD to train sparse coefficients.Denoising effect is reduced because a few noisy pixels are likely to cause false relevance.This paper addressed this problem and proposed a novel sparsity-adaptive speeded K-SVD(SASK-SVD) algorithm based on different coefficient,which can improve the efficiency.The different coefficient is to eliminate false relevance.The sparsity K is adaptively generated by using the average correlation as the threshold.This study conducted extensive experiments to demonstrate these ideas.The experimental results show that the proposed method achieves the state-of-the-art denoising performance.

Key words: Image denoising,K-means singular value decomposition,Sparsity-adaptive,Difference coefficient

[1] YUAN S Q,TAN Y H.Difference-type noise detector for adaptive median filter[J].IEEE lectronics Letters,2006,42(8):454-455.
[2] MAGGIONI M,KATKOVNIK V,EGIAZARIAN K.Nonlocal transform-domain filter for volumetric data denoising and reconstruction [J].IEEE Transactions on Image Processing,2013,22(1):119-133.
[3] DONOHO D L.Compressed sensing[J].IEEE Transaction on Information Theory,2006,52(4):1289-1306.
[4] ELAD M,AHARON M.Image denoising via sparse and redundant representations over learned dictionaries [J].IEEE Tran-sactions on Image Processing,2006,15(12):3736-3745.
[5] LIU Q,ZHANG C,GUO Q,et al.Adaptive sparse coding on PCA dictionary for image denoising[J].The Visual Computer,2016,32(4):535-549.
[6] AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:an algo-rithm for designing overcomplete dictionaries for sparse representation [J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[7] ROMANO Y,ELAD M.Improving K-SVD denoise by post-processing its method-noise[C]∥Proceedings of International Conference on Image Processing.2013:435-439.
[8] RAJA H,BAJWA W U.Cloud K-SVD:A Collaborative Dictiona-ry Learning Algorithm for Big,Distributed Data[J].IEEE Transactions on Signal Processing,2016,4(1):173-188.
[9] JIAO L J,WANG W J.A Speeded-up K-SVD Image Denoising Algorithm[J].Mini-Micro Systems,2016,37(7):1608-1612.(in Chinese) 焦莉娟,王文剑.一种快速的K-SVD图像去噪方法[J].小型微型计算机系统,2016,37(7):1608-1612.
[10] TROPP J,GILBERT A.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transaction on Information Theory,2007,3(12):4655-4666.
[11] MALLAT S,ZHANG Z.Matching pursuits with time frequency dictionaries[J].IEEE Transactions on Signal Processing,1993,1(12):3397-3415.
[12] NEEDELL D,VERSHYNIN R.Uniform uncertainty principleand signal recovery via regularized orthogonal matching pursuit[J].Foundations of Computational Mathematics,2009,9(3):317-334.
[13] DO T T,GAN L,NGUYEN N,et al.Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]∥IEEE 42nd Asilomar Conference on Signals,Systems and Computers.2008:581-587.
[14] NEEDELL D,TROPP J A.CoSaMP:Iterative signal recovery from incomplete and inaccurate samples[J].Applied and Computational Harmonic Analysis,2009,26(3):301-321.
[15] DONOHO D L,TSAIG Y,DRORI I,et al.Sparse solution ofunderdetermined systems of linear equations by stagewise orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2012,8(2):1094-1121.
[16] KWON S,WANG J,SHIM B.Multipath matching pursuit[J].IEEE Transactions on Information Theory,2014,60(5):2986-3001.

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] 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 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] 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 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] 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, 116 .