Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 234-238.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Improved Sparsity Adaptive Matching Pursuit Algorithm

WANG Fu-chi1,ZHAO Zhi-gang1,LIU Xin-yue1,LV Hui-xian2,WANG Guo-dong1,XIE Hao1   

  1. College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China1
    College of Automation and Electrical Engineering,Qingdao University,Qingdao,Shandong 266071,China2
  • Online:2018-06-20 Published:2018-08-03

Abstract: Sparsity adaptive matching pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing under the condition that the sparsity is unknown.In order to optimize the performance of SAMP algorithm,an improved sparsity adaptive matching pursuit(ISAMP) algorithm was proposed.The proposed algorithm introduces generalized Dice coefficient for matching criterion,which improves its performance in selecting the most matching atom from measurement matrix for residual signal.Meanwhile,it uses threshold method to select preliminary set and adopts exponential variable step during the iteration.Experimental results show that the proposed algorithm improves reconstruction quality and computational time.

Key words: Compressive sensing, Dice coefficient, Matching pursuit, Reconstruction algorithm

CLC Number: 

  • TN911.7
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