计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 266-271.doi: 10.11896/j.issn.1002-137X.2019.05.041
杜秀丽, 左思铭, 邱少明
DU Xiu-li, ZUO Si-ming, QIU Shao-ming
摘要: 针对传统图像稀疏表示字典学习算法仅对图像训练学习单一字典,不能很好地对包含不同图像信息的图像块进行最优稀疏表示的问题,将图像灰度熵的思想引入到字典学习算法中,提出基于图像灰度熵的自适应字典学习算法。该算法将图像库作为训练样本,对图像库图像进行分块,计算各子块的灰度熵大小,依据灰度熵大小对子块进行分类,针对不同类别子块,设定不同K-奇异值分解算法参数,分别进行字典训练,从而得到多个不同的字典。根据灰度熵大小选择训练好的字典对待表示图像子块进行稀疏表示。仿真实验及结果表明,所提算法能够对图像进行较好的稀疏表示,图像的重构效果也得到了明显提升。
中图分类号:
[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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