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

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

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 chall...

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Bibliographic Details
Main Authors: Rogelio Reyes-Reyes, Yeredith G. Mora-Martinez, Beatriz P. Garcia-Salgado, Volodymyr Ponomaryov, Jose A. Almaraz-Damian, Clara Cruz-Ramos, Sergiy Sadovnychiy
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Mathematics
Subjects:
super-resolution
remote sensing
deep learning
balanced trade-off
Large Kernel Attention
Online Access:https://www.mdpi.com/2227-7390/13/15/2400
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