Computer Science ›› 2019, Vol. 46 ›› Issue (5): 266-271.doi: 10.11896/j.issn.1002-137X.2019.05.041

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Adaptive Dictionary Learning Algorithm Based on Image Gray Entropy

DU Xiu-li, ZUO Si-ming, QIU Shao-ming   

  1. (Key Laboratory of Communication and Network,Dalian University,Dalian,Liaoning 116622,China)
    (College of Information Engineering,Dalian University,Dalian,Liaoning 116622,China)
  • Published:2019-05-15

Abstract: Aiming at the problem that the traditional dictionary learning algorithm of image sparse representation only learns a single dictionary for image training,and can not optimally sparsely represent image blocks containing different image information,through introducing the local gray entropy of image into the dictionary learning algorithm,this paper proposed an adaptive dictionary learning algorithm based on image local gray entropy.The proposed algorithm makes use of the image database as training sample.Firstly,the image database is divided into blocks,and the gray entropy of each sub-block is calculated.Then,the sub-blocks are classified according to the size of the gray entropy,and different K-Singular Value Decomposition (K-SVD) parameters are set for different categories of sub-blocks to perform dictionary training respectively,thus obtaining a plurality of different dictionaries.Lastly,a well-trained dictionary is selected for the image sub-blocks to conduct sparse representation according to the size of the gray entropy.Simulation experiment results show that the proposed algorithm can sparsely represent the images better,and the effect of image reconstruction is also improved significantly.

Key words: Dictionary learning, Gray entropy, K-Singular value decomposition, Sparse representation

CLC Number: 

  • TP391.4
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