Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 200-204.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Self-adapting Regular Constraint Algorithm in Super-resolution of Single-frame Images

LI Hai-xue1, LIN Hai-tao2, CHEN Jin2   

  1. Naval Aviation University,Yantai,Shangdong 264001,China1;
    School of Electronic Engineering,Naval University of Engineering,Wuhan 430033,China2
  • Online:2019-06-14 Published:2019-07-02

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Abstract: As a typical undetermined problem,super-resolution of single-frame images needs to be constrained by regular terms in the process of optimization,so as to improve the stability of super-resolution reconstruction.As a regular term commonly used in super-resolution,smoothness regularities may lead to the loss of high frequency information in images,cause the blurring of marginal areas in images,and affect the visual effects of reconstructed images.Based on Markov random field(MRF),this paper built the model of local image,characterized the correlation between the pixels in the local image block and realized self-adapting regular constraint in the process of super-resolution,which can effectively avoid the blurring effect in the marginal areas and other positions in the images,and improve the performance of the image reconstruction.

Key words: Markov random field, Self-adapting, Super-resolution of single-frame images

CLC Number: 

  • TP751
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