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A Sparse Analysis-Based Single Image Super-Resolution

1
Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, 14778-93855 Tehran, Iran
2
Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran
*
Author to whom correspondence should be addressed.
Computers 2019, 8(2), 41; https://doi.org/10.3390/computers8020041
Received: 1 May 2019 / Revised: 22 May 2019 / Accepted: 25 May 2019 / Published: 27 May 2019
In the current study, we were inspired by sparse analysis signal representation theory to propose a novel single-image super-resolution method termed “sparse analysis-based super resolution” (SASR). This study presents and demonstrates mapping between low and high resolution images using a coupled sparse analysis operator learning method to reconstruct high resolution (HR) images. We further show that the proposed method selects more informative high and low resolution (LR) learning patches based on image texture complexity to train high and low resolution operators more efficiently. The coupled high and low resolution operators are used for high resolution image reconstruction at a low computational complexity cost. The experimental results for quantitative criteria peak signal to noise ratio (PSNR), root mean square error (RMSE), structural similarity index (SSIM) and elapsed time, human observation as a qualitative measure, and computational complexity verify the improvements offered by the proposed SASR algorithm. View Full-Text
Keywords: image patches; patch ordering; operator learning; sparse analysis; super-resolution image patches; patch ordering; operator learning; sparse analysis; super-resolution
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Anari, V.; Razzazi, F.; Amirfattahi, R. A Sparse Analysis-Based Single Image Super-Resolution. Computers 2019, 8, 41.

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