Open Access

ARTICLE

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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

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

* Corresponding Author: Israa Ismail. Email:

Computers, Materials & Continua 2024, 79(2), 3193-3209. https://doi.org/10.32604/cmc.2023.043873

Received 14 July 2023; Accepted 14 November 2023; Issue published 15 May 2024

Abstract

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs. Super-resolution is of paramount importance in the context of remote sensing, satellite, aerial, security and surveillance imaging. Super-resolution remote sensing imagery is essential for surveillance and security purposes, enabling authorities to monitor remote or sensitive areas with greater clarity. This study introduces a single-image super-resolution approach for remote sensing images, utilizing deep shearlet residual learning in the shearlet transform domain, and incorporating the Enhanced Deep Super-Resolution network (EDSR). Unlike conventional approaches that estimate residuals between high and low-resolution images, the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high- and low-resolution image. The shearlet transform is chosen for its excellent sparse approximation capabilities. Initially, remote sensing images are transformed into the shearlet domain, which divides the input image into low and high frequencies. The shearlet coefficients are fed into the EDSR network. The high-resolution image is subsequently reconstructed using the inverse shearlet transform. The incorporation of the EDSR network enhances training stability, leading to improved generated images. The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery, effectively restoring both global topology and local edge detail information, thereby enhancing image quality. Compared to other networks, our proposed approach outperforms the state-of-the-art in terms of image quality, achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

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Keywords

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