Computer Science ›› 2018, Vol. 45 ›› Issue (8): 277-282.doi: 10.11896/j.issn.1002-137X.2018.08.050

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

GLCM-based Adaptive Block Compressed Sensing Method for Image

DU Xiu-li, ZHANG Wei, GU Bin-bin, CHEN Bo, QIU Shao-ming   

  1. Key Laboratory of Communications Network and Information Processing,Dalian University,Dalian 116622,China
  • Received:2017-06-23 Online:2018-08-29 Published:2018-08-29

Abstract: The method of block compressed sensing really makes up for the defects of the consumed resource and time in reconstruction of large-size images.However,there is an obvious block effect in the reconstructed image.Aiming to solve the problem of inaccurate anlysis of texture complexity that hinders reduction of the block effect in adaptive sampling compressed sensing method,this paper proposed an adaptive block compressed sensing method based on co-occurrence matrices.The texture feature of image is analyzed by the co-occurrence matrix,and then the sampling rate is adaptively allocated according to the texture feature.Under the premise that the total sampling rate is not changed,the image with complex texture obtains higher sampling rate,and the image with simple texture obtains lower sampling rate.At last,SAMP (Sparsity Adaptive Matching Pursuit) is used to conduct reconstruction.The simulation results show that the proposed method can effectively eliminate the block effect,especially for the partial blocks,and the performance of the reconstructed block is obviously improved.

Key words: Block compressed sensing, Co-occurrence matrices, Entropy, Sampling rate

CLC Number: 

  • TN911.7
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