Super-resolution Imaging Based on Global Interpolation and Structural Similarities

Yuan Zhou, Shuwei Huo, Ying Chen, Sun Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a double dictionary learning method for image super-resolution (SR) reconstruction. Different from existing dictionary learning based super-resolution, we combine both self-similarity and external images to construct a double dictionary learning method. A new optimization model is established using self-similarities and external-similarities as regularization terms. Furthermore, we propose a global interpolation method to reconstruct an accurate initial estimation at the edges. Experimental results show that the proposed algorithm can produce high-quality reconstruction results both perceptually and quantitatively in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as compared to existing algorithms.

Original languageEnglish (US)
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2977-2982
Number of pages6
ISBN (Electronic)9781538637883
DOIs
StatePublished - Nov 26 2018
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: Aug 20 2018Aug 24 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period8/20/188/24/18

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Zhou, Y., Huo, S., Chen, Y., & Kung, S. Y. (2018). Super-resolution Imaging Based on Global Interpolation and Structural Similarities. In 2018 24th International Conference on Pattern Recognition, ICPR 2018 (pp. 2977-2982). [8545608] (Proceedings - International Conference on Pattern Recognition; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8545608