Computer Science ›› 2016, Vol. 43 ›› Issue (2): 307-310.doi: 10.11896/j.issn.1002-137X.2016.02.064

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Enhanced Block Compressed Sensing of Images Based on Total Variation Using Texture Information

WANG Yue, ZHOU Cheng, XIONG Cheng-yi and SHU Zhen-yu   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Block compressed sensing of images solves the problems of high computational complexity and large storage space required by blocking an image and downsizing measurement matrix.But such a practice will result in blocking artifacts,which needs to be filtered.Existing algorithms do not consider how to recover textural features of images,which will result in quality degradation of image reconstruction.In order to solve this problem,this paper proposed an algorithm which uses an adaptive sampling model based on gray entropy at first,and then analyzed the reason why blocking artifacts generate and are reduced by adaptive sampling.At last,in the proposed algorithm TV filter is joined with SPL process,and a DDWT/TV filter model based on texture information is built to replace the former filtering process in reconstruction.The model can preserve more details of images after decreasing block artifacts by using adaptive sampling.Experimental results show that the proposed algorithm can remarkably improve the subjective and objective quality of the reconstructed image and can effectively hold more texture information of images compared to some existing methods.

Key words: Block compressed sensing,Adaptive sampling,Total variation filter,De-blocking filter

[1] Donoho D L.Compressed sensing [J].IEEE Transactions on Information Theory,2006,54(4):1289-1306
[2] Candes E.Compressive sampling [J].International Congress of Mathematicians,2006,3:1433-1452
[3] Candes E,Wakin M B.An introduction to compressive sampling [J].IEEE Signal Process Magazine,2008,25(2):21-30
[4] Donoho D L.For most large underdetermined systems of linear equations,the minimal l1 norm solution is also the sparsest solution [J].Communications on Pure and Applied Mathematics,2006,59(6):797-829
[5] Tropp J,Gilbert A.Signal Recovery from Random Measure-ments via Orthogonal Matching Pursuit [J].IEEE Trans.on Information Theory,2007,53(12):4655-4666
[6] Mun S,Fowler E.Block compressed sensing of images using directional transforms [C]∥Proc.IEEE Intern.Conf.on Image Processing.USA,2009:3021-3024
[7] Wang Rong-fang,Jiao Li-cheng,Liu Fang,et al.Block-basedadaptive compressed sensing of image using texture information [J].Acta Electronica Sinica,2013,41(38):1506-1514(in Chinese) 王荣芳,焦李成,刘芳,等.利用纹理信息的图像分块自适应压缩感知 [J].电子学报,2013,41(38):1506-1514
[8] Khanh Q D,Shim H B J.Deblocking filter for artifact reduction in distributed compressive video sensing [C]∥Visual Communications and Image Processing (VCIP).2012:1-5
[9] Mun S,Fowler J E.Residual reconstruction for block-basedcompressed sensing of video [C]∥Proc.of Data Compression Conf..USA,2011:183-192
[10] Candes E,Romberg J,Tao T.Stable signal recovery from incomplete and inaccurate measurements [C]∥Comm.on Pureand Applied Mathematics.2006:1207-1223
[11] Li Ran,Gang Zong-liang,Zhu Xiu-chang .A Global Reconstruction Model of Images Using Block Compressed Sensing [J].Singal Processing,2012,28(10):1416-1422(in Chinese) 李然,干宗良,朱秀昌.基于分块压缩感知的图像全局重构模型[J].信号处理,2012,28(10):1416-1422
[12] Wang Shang-li.Research on image recovery method based oncompressed sensing [D].Xi’an:Xidian University,2012(in Chinese) 王尚礼.压缩感知图像重建算法研究[D].西安:西安电子科技大学,2012
[13] Gan L.Block compressed sensing of natural images [C]∥International Conference on Digital Signal Processing.Cardiff,2007:403-406
[14] Rudin L I,Oseher S,Fatemi E.Nonlinear total variation based noise removeal algorithms [J].Physica D,1992,60:259-268
[15] Z Hai-bo,Zhu Xiu-chang.Sampling adaptive block compressed sensing reconstruction algorithm for images based on edge detection [J].Journal of China Universities of Posts and Telecommunications,2013,20(3):97-103
[16] 中国百科网.详解ISO12233 Chart(分辨率测试标板)使用方法[EB/OL].http://www.chinabaike.com/t/35899/2013/0802/1357958.html

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