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Single Image Super-Resolution Reconstruction Technique based on A Single Hybrid Dictionary

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Abstract

A new sparse domain approach is proposed in this paper to realize the single image super-resolution (SR) reconstruction based upon one single hybrid dictionary, which is deduced from the mixture of both the high resolution (HR) image patch samples and the low resolution (LR) ones. Moreover, a linear model is proposed to characterize the relationship between the sparse representations of both the HR image patches and the corresponding LR ones over the same hybrid dictionary. It is shown that, the requirement on the identical sparse representation of both HR and LR image patches over the corresponding HR dictionary and the LR dictionary can be relaxed. It is unveiled that, the use of one single hybrid dictionary can not only provide a more flexible framework to keep the similar sparse characteristics between the HR patches and the corresponding degenerated LR patches, but also to accommodate their differences. On this basis, the sparse domain based SR reconstruction problem is reformulated. Moreover, the proposed linear model between the sparse representations of both the HR patch and the corresponding LR patch over the same hybrid dictionary offers us a new method to interpret the image degeneration characteristics in sparse domain. Finally, practical experimental results are presented to test and verify the proposed SR approach.

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Acknowledgments

The authors would like to thank the NSFC under grant No. 61271246 and no. 61371165 for financial support.

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Authors and Affiliations

  1. Key Lab of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China

    Chanzi Liu, Qingchun Chen & Hengchao Li

Authors
  1. Chanzi Liu
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  2. Qingchun Chen
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  3. Hengchao Li
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Correspondence to Chanzi Liu.

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Liu, C., Chen, Q. & Li, H. Single Image Super-Resolution Reconstruction Technique based on A Single Hybrid Dictionary. Multimed Tools Appl 76, 14759–14779 (2017). https://doi.org/10.1007/s11042-016-4022-x

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  • DOI: https://doi.org/10.1007/s11042-016-4022-x

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