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Mathematics
Volume 13
Issue 15
10.3390/math13152400
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Robust System for Super-Resolution Imaging in Remote Sensing via Attention-Based Residual Learning

by
Rogelio Reyes-Reyes
1,
Yeredith G. Mora-Martinez
1,
Beatriz P. Garcia-Salgado
1,
Volodymyr Ponomaryov
1,*,
Jose A. Almaraz-Damian
2,
Clara Cruz-Ramos
1 and
Sergiy Sadovnychiy
3
1
Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Mexico City 04440, Mexico
2
Centro de Investigación Científica y de Educación Superior de Ensenada, Unidad Académica Tepic, Tepic 63173, Mexico
3
Instituto Mexicano del Petróleo, Mexico City 07730, Mexico
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(15), 2400; https://doi.org/10.3390/math13152400
Submission received: 13 June 2025 / Revised: 16 July 2025 / Accepted: 23 July 2025 / Published: 25 July 2025
(This article belongs to the Special Issue Computing in Image Processing for Remote Sensing and Biomedical Applications)
Versions Notes

Abstract

Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality.
Keywords: super-resolution; remote sensing; deep learning; balanced trade-off; Large Kernel Attention super-resolution; remote sensing; deep learning; balanced trade-off; Large Kernel Attention

Share and Cite

MDPI and ACS Style

Reyes-Reyes, R.; Mora-Martinez, Y.G.; Garcia-Salgado, B.P.; Ponomaryov, V.; Almaraz-Damian, J.A.; Cruz-Ramos, C.; Sadovnychiy, S. A Robust System for Super-Resolution Imaging in Remote Sensing via Attention-Based Residual Learning. Mathematics 2025, 13, 2400. https://doi.org/10.3390/math13152400

AMA Style

Reyes-Reyes R, Mora-Martinez YG, Garcia-Salgado BP, Ponomaryov V, Almaraz-Damian JA, Cruz-Ramos C, Sadovnychiy S. A Robust System for Super-Resolution Imaging in Remote Sensing via Attention-Based Residual Learning. Mathematics. 2025; 13(15):2400. https://doi.org/10.3390/math13152400

Chicago/Turabian Style

Reyes-Reyes, Rogelio, Yeredith G. Mora-Martinez, Beatriz P. Garcia-Salgado, Volodymyr Ponomaryov, Jose A. Almaraz-Damian, Clara Cruz-Ramos, and Sergiy Sadovnychiy. 2025. "A Robust System for Super-Resolution Imaging in Remote Sensing via Attention-Based Residual Learning" Mathematics 13, no. 15: 2400. https://doi.org/10.3390/math13152400

APA Style

Reyes-Reyes, R., Mora-Martinez, Y. G., Garcia-Salgado, B. P., Ponomaryov, V., Almaraz-Damian, J. A., Cruz-Ramos, C., & Sadovnychiy, S. (2025). A Robust System for Super-Resolution Imaging in Remote Sensing via Attention-Based Residual Learning. Mathematics, 13(15), 2400. https://doi.org/10.3390/math13152400

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