Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 188-191.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Image Super-resolution Reconstruction Algorithm with Adaptive Sparse Representationand Non-local Self-similarity

ZHANG Fu-wang, YUAN Hui-juan   

  1. School of Measurement-Control Technology and Communications Engineering,Harbin University of ;
    Science and Technology,Harbin 150080,China
  • Online:2019-06-14 Published:2019-07-02

Abstract: How to make full use of the information contained in the image for super-resolution reconstruction is still an open question.This paper proposed an image super-resolution reconstruction algorithm based on adaptive sparse representation and non-local self-similarity.In the process of training and reconstruction,the K-means algorithm is used to cluster the selected datasets,and similar image blocks are gathered together.Then PCA is used to process the adaptive selection dictionary for super-resolution reconstruction.Compared with image reconstruction through a fixed dictionary,the adaptive selection dictionary is used to reconstruct the image,and the effect of reconstructed image obtained will be more superior.The experimental results on natural images show that the super-resolution images reconstructed by the proposed algorithm are more detailed,with fewer artifacts and sharper edges.

Key words: Iterative shrinkage algorithm, Non-local self-similarity, Sparse representation, Super-resolution

CLC Number: 

  • TP391.9
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