Computer Science ›› 2019, Vol. 46 ›› Issue (5): 266-271.doi: 10.11896/j.issn.1002-137X.2019.05.041

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Adaptive Dictionary Learning Algorithm Based on Image Gray Entropy

DU Xiu-li, ZUO Si-ming, QIU Shao-ming   

  1. (Key Laboratory of Communication and Network,Dalian University,Dalian,Liaoning 116622,China)
    (College of Information Engineering,Dalian University,Dalian,Liaoning 116622,China)
  • Published:2019-05-15

Abstract: Aiming at the problem that the traditional dictionary learning algorithm of image sparse representation only learns a single dictionary for image training,and can not optimally sparsely represent image blocks containing different image information,through introducing the local gray entropy of image into the dictionary learning algorithm,this paper proposed an adaptive dictionary learning algorithm based on image local gray entropy.The proposed algorithm makes use of the image database as training sample.Firstly,the image database is divided into blocks,and the gray entropy of each sub-block is calculated.Then,the sub-blocks are classified according to the size of the gray entropy,and different K-Singular Value Decomposition (K-SVD) parameters are set for different categories of sub-blocks to perform dictionary training respectively,thus obtaining a plurality of different dictionaries.Lastly,a well-trained dictionary is selected for the image sub-blocks to conduct sparse representation according to the size of the gray entropy.Simulation experiment results show that the proposed algorithm can sparsely represent the images better,and the effect of image reconstruction is also improved significantly.

Key words: Sparse representation, Dictionary learning, K-Singular value decomposition, Gray entropy

CLC Number: 

  • TP391.4
[1]CANDES E J,ROMBERG T,TAO T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information.IEEE Transactions on Information Theo-ry,2006,52(2):489-509.
[2]DONOHO D L.Compressed sensing[J].IEEE Transactions on Inform Theory,2006,52(4):1289-1306.
[3]SHRIVIDYA G,BHARATHI S H.Application of Compressed Sensing on Magnetic Resonance Imaging:A brief survey∥IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology.Bangalore,India,2016:2037-2041.
[4]LIAN Q S,SHI B S,CHEN S Z.Research Advances on Dictio-nary Learning Models,Algorithms and Applications[J].Acta Automatica Sinica,2015,41(2):240-260.(in Chinese)练秋生,石保顺,陈书贞.字典学习模型、算法及其应用研究进展[J].自动化学报,2015,41(2):240-260.
[5]RENE V,YI M,SHANKAR S.Generalized principal compo-nent analysis[J].IEEE Transactions Pattern Anal Mach Intell,2005,27(12):1945-1959.
[6]LIU Z,SONG X N,YU D J,et al.Super-resolution reconstruction algorithm based on multi-component dictionary and sparse representation[J].Journal of Nanjing University of Science and Technology,2014,38(1):1-5.(in Chinese)刘梓,宋晓宁,於东军,等.基于多成分字典和稀疏表示的超分辨率重建算法[J].南京理工大学学报,2014,38(1):1-5.
[7]YANG S Y,JIN H H,WANG M,et al.Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification[J].IEEE Geoscience and Remote Sensing Letters,2014,11(2):479-483.
[8]LIU X M,LIU Y M.Color image denoising with block K-SVD dictionary learning[J].Journal of Nanjing University of Science and Technology,2016,40(5):607-612.(in Chinese)刘晓曼,刘永民.基于分块K-SVD字典学习的彩色图像去噪[J].南京理工大学学报,2016,40(5):607-612.
[9]ENGAN K,AASE S O,HUSOY J H.Method of optimal direc-tions for frame design.http://xueshu.baidu.com/usercenter/paper/show?paperid=dc1a54ef946aaaa15e2aa439a9d-116c9&site=xueshu_se.
[10]MAIRAL J,BACH F,PONCE J,et al.Online learning for matrix factorization and sparse coding[J].Journal of Machine Learning Research,2010,11(1):19-60.
[11]AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:an algo-rithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transaction on Signal Processing,2006,54(11):4311-4322.
[12]MITTU G P,VIVEK M,JOONKI P.Imaging inverse problemusing sparse representation with adaptive dictionary learning[C]∥IEEE International Advance Computing Conference.2015.
[13]ZHANG S Y.Research on Image Compressive Sensing Techno-logy Based on Redundant Dictionary[D].Jiling:JiLing University,2016.(in Chinese)张书扬.基于冗余字典的图像压缩感知技术研究[D].吉林:吉林大学,2016.
[14]CONG Y L,ZHANG S Y,LIAN Y Y.K-SVD dictionary lear-ning and image reconstructionbased on variance of image patches[C]∥8th International Symposium on Computational Intelligence and Design.2015:254-257.
[15]SUN J D,ZHAO H H.Sparse Representation and Applications in Image Processing[J].Infrared Technology,2014,36(7):533-537.(in Chinese)孙君顶,赵慧慧.图像稀疏表示及其在图像处理中的应用[J].红外技术,2014,36(7):533-537.
[16]CANDES E,ROMBERG J,TAO T.Stablesignal recovery from incomplete and inaccurate measurements[J].Communications on Pure and Applied Mathematics,2006,59(8):1207-1223.
[17]ZHANG D S,ZHANG L H.Research on fast dictionary learning algorithm under compressed sensing framework[J].Research and exploration in laboratory,2015,34(11):94-98.(in Chinese)张得生,张莉华.压缩感知框架下快速字典的学习算法[J].实验室研究与探索,2015,34(11):94-98.
[18]ZHANG Y L,WANG Y,LU H Z.Block objects detection based on entropy of brightness[J].Systems Engineering and Electro-nic,2008,30(2):201-204.(in Chinese)张永亮,汪洋,卢焕章.基于图像灰度熵的团块目标检测方法[J].系统工程与电子技术,2008,30(2):201-204.
[19]KHANH Q D,HIUK J S,JEON B.Weighted Overlapped Recovery for Blocking Artefacts Reduction in Block-based Compressive Sensing of Images[J].Electronics Letters,2015,51(1):48-50.
[20]ZHANG B,LIU Y L.A novel block compressed sensing based on matrix permutation∥Visual Communications and Image Processing.2016:1-4. [21]ZHU X,LIU L,JIN P.Morphological component decomposition combined with compressed sensing for image compression∥2016 IEEE International Conference on Information and Automation.2016:1726-1731.
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