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

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