Computer Science ›› 2015, Vol. 42 ›› Issue (Z6): 151-153.

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Super-resolution Reconstruction of Medical Images Based on Group Sparse Representation

HUANG Hao-feng and XIAO Nan-feng   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Medical diagnosis needs a lot of medical image processing.Due to the technological and economical limits,the medical diagnosis is not able to get the clear medical images.Therefore,it is necessary to reconstruct the medical images with super-resolution methods.Based on super-resolution reconstruction of single image by the sparse coding,and considering that there are obviously repetitive image structures in the medical images,this paper proposed a reconstruction method for the super-resolution medical images based on the group sparse representation.In addition,this paper also presented an dictionary train algorithm which combines the Group Lasso with K-SVD.The experimental results indicate that the proposed algorithms have higher performance than that of the existing methods.

Key words: Medical image,Super-resolution reconstruction,Dictionary,Group sparse

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