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

Previous Articles     Next Articles

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.
[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.
[1] ZHANG Fan, HE Wen-qi, JI Hong-bing, LI Dan-ping, WANG Lei. Multi-view Dictionary-pair Learning Based on Block-diagonal Representation [J]. Computer Science, 2021, 48(1): 233-240.
[2] 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.
[3] 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.
[4] 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.
[5] WANG Jun-hao, YAN De-qin, LIU De-shan, XING Yu-jia. Algorithm with Discriminative Analysis Dictionary Learning by Fusing Extreme Learning Machine [J]. Computer Science, 2020, 47(5): 137-143.
[6] QIAN Ling-long, WU Jiao, WANG Ren-feng, LU Hui-juan. Multi-document Automatic Summarization Based on Sparse Representation [J]. Computer Science, 2020, 47(11A): 97-105.
[7] 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.
[8] LI Gui-hui,LI Jin-jiang,FAN Hui. Image Denoising Algorithm Based on Adaptive Matching Pursuit [J]. Computer Science, 2020, 47(1): 176-185.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] WU Chen, YUAN Yu-wei, WANG Hong-wei, LIU Yu, LIU Si-tong, QUAN Ji-cheng. Word Vectors Fusion Based Remote Sensing Scenes Zero-shot Classification Algorithm [J]. Computer Science, 2019, 46(12): 286-291.
[14] MAO Xia, WANG Lan, LI Jian-jun. Human Action Recognition Framework with RGB-D Features Fusion [J]. Computer Science, 2018, 45(8): 22-27.
[15] GAN Ling, ZHAO Fu-chao, YANG Meng. Self-adaptive Group Sparse Representation Method for Image Inpainting [J]. Computer Science, 2018, 45(8): 272-276.
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 .
[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 .