Computer Science ›› 2018, Vol. 45 ›› Issue (3): 263-267.doi: 10.11896/j.issn.1002-137X.2018.03.042

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Single Video Super-resolution Algorithm Based on Non-local Means and Total Variation Minimization

CHEN Cheng, CHANG Kan, MO Cai-wang, LI Tian-yi and QIN Tuan-fa   

  • Online:2018-03-15 Published:2018-11-13

Abstract: The traditional reconstruction-based single video super-resolution algorithms are able to solve the video super-resolution problem well.However,the existing algorithms have not fully exploited the correlation in intra-frames and inter-frames,which leaves much space for further improvement.This paper proposed a new single video super-resolution algorithm to solve this problem.When exploiting the spatial correlations,the non-local means model is used to get the non-local structural property and the total variation model is utilized to get the local structural property.In order to exploit inter-frame correlation,optical flow method is applied to perform inter-frame estimation.Finally,to solve the established optimization problem,a split-Bregman method based fast iteration algorithm was proposed.The experimental results demonstrate the effectiveness of the proposed algorithm.Compared with other algorithms,the proposed algorithm is able to achieve better subjective and objective results.

Key words: Video super-resolution,Non-local means,Total variation,Optical flow

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