Open Access

ARTICLE

Super-Resolution Based on Curvelet Transform and Sparse Representation

by Israa Ismail^1,*, Mohamed Meselhy Eltoukhy^1,2, Ghada Eltaweel¹

1 Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, 51422, Egypt
2 Department of Information Technology, University of Jeddah, College of Computing and Information Technology at Khulais, Jeddah, Saudi Arabia

* Corresponding Author: Israa Ismail. Email:

Computer Systems Science and Engineering 2023, 45(1), 167-181. https://doi.org/10.32604/csse.2023.028906

Received 21 February 2022; Accepted 06 April 2022; Issue published 16 August 2022

Abstract

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s). In this paper, we proposed a single image super-resolution algorithm. It uses the nonlocal mean filter as a prior step to produce a denoised image. The proposed algorithm is based on curvelet transform. It converts the denoised image into low and high frequencies (sub-bands). Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands. In parallel, we applied sparse representation with over complete dictionary for the denoised image. The proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher resolution. The experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced edges. The comparison study shows that the proposed super-resolution algorithm outperforms the state-of-the-art. The mean absolute error is 0.021 ± 0.008 and the structural similarity index measure is 0.89 ± 0.08.

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Keywords

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