Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

Authors

  • Xiuxiu Liao School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, China
  • Kejia Bai School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, China
  • Qian Zhang School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, China
  • Xiping Jia School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, China
  • Shaopeng Liu School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, China
  • Jin Zhan School of Computer Science, Guangdong Polytechnic Normal University, 510665 Guangzhou, China

DOI:

https://doi.org/10.31577/cai_2019_6_1301

Keywords:

Image patch classification, multi-class dictionaries, phase congruency, sparse coding, super-resolution

Abstract

Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures.

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Published

2020-02-29

How to Cite

Liao, X., Bai, K., Zhang, Q., Jia, X., Liu, S., & Zhan, J. (2020). Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries. Computing and Informatics, 38(6), 1301–1319. https://doi.org/10.31577/cai_2019_6_1301