Semi-coupled Dictionary Learning Super-resolution Reconstruction Model with Detail Constraint Factor

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Semi-coupled Dictionary Learning Super-resolution Reconstruction Model with Detail Constraint Factor

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[1]Huang Yuda,Wang Yanran,Niu Sijie,et al.Semi-coupled Dictionary Learning Super-resolution Reconstruction Model with Detail Constraint Factor[J].Journal of Zhengzhou University (Engineering Science),2021,42(03):59-.[doi:10.13705/j.issn.1671-6833.2021.03.010]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42 Number of periods: 2021 03 Page number: 59- Column: Public date: 2021-05-10

Title:: Semi-coupled Dictionary Learning Super-resolution Reconstruction Model with Detail Constraint Factor

Author(s):: Huang Yuda; Wang Yanran; Niu Sijie;; College of Information Engineering of Zhoukou Vocational and Technical College; School of Network Engineering, Zhoukou Teachers College; School of Information Science and Engineering, Jinan University;

Keywords:: super-resolution; semi-coupled dictionary learning; detail constraint factor; Laplacian distribution

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.03.010

Abstract:: In order to improve the super-resolution reconstruction quality of single image, an improved learning based super-resolution approach was proposed in this paper. To tackle the problem of low details of semi-coupled dictionary learning super-resolution algorithm, the paper presented learning strategy where detail constraint factor and semi-coupled dictionary learning were performed in turn. In reconstruction stage, detail constraint factor was designed by the gradient in both horizontal and vertical direction. Combined with semi-coupled dictionary learning, detail constraint factor was used to further improve the super-resolution reconstruction quality. In order to improve the contribution of detail constraint factor on preserving boundary information, the adaptive regular parameter was explored via the approximate Laplacian distribution of edge difference. Compared with the semi coupled dictionary learning super-resolution algorithm, the peak signal-to-noise ratio of this method was increased by 1.5% on average. Experiments demonstrated that the proposed method could achieve better reconstruction effect in both subjective and objective evaluation and improve the quality of super-resolution.

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