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Remote Sensing
Volume 18
Issue 17
10.3390/rs18172850
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Article

Towards Lightweight and Accurate Remote-Sensing Image Super-Resolution via Reparameterized Feature Enhancement Network

by
Feng Huang
1,
Renhui Wei
1,
Liqiong Chen
1,
Zhaobing Qiu
1,
Xiangkun Yang
1,
Gaozhu Ran
1 and
Yangping Yuan
2,*
1
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
2
Zhipu AI, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(17), 2850; https://doi.org/10.3390/rs18172850
Submission received: 29 June 2026 / Revised: 10 August 2026 / Accepted: 20 August 2026 / Published: 22 August 2026
(This article belongs to the Special Issue Lightweight Artificial-Intelligence Techniques for Remote-Sensing Image Processing)
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Abstract

Remote sensing image super-resolution (RSISR) provides an effective means of improving spatial detail for Earth observation and satellite image interpretation. However, existing methods often rely on increasingly complex network designs with deeper hierarchies and expanded channel capacities to pursue higher performance, resulting in heavy models with high computational cost, which restricts their deployment on resource-constrained platforms. To address this challenge, we propose a novel reparameterized feature enhancement network (RepFEN) for lightweight and accurate RSISR tasks. Specifically, a multi-scale reparameterized module (MRepM) is designed to capture multi-scale spatial information and enhance texture representation. Furthermore, a partial-channel gated attention module (PCGAM) is introduced to selectively enhance discriminative features along the channel dimension, effectively improving fine-grained detail restoration. By integrating structural reparameterization and multi-scale lightweight modules, the proposed method achieves a better balance between reconstruction accuracy and inference efficiency. Extensive experiments on both remote sensing and natural image super-resolution benchmarks demonstrate that our method achieves superior performance compared to existing state-of-the-art methods, while maintaining minimal computational overhead, showing significant potential for real-world applications.
Keywords: remote sensing images; super-resolution reconstruction; lightweight network; reparameterization; multi-scale feature extraction; channel attention mechanism; deep learning remote sensing images; super-resolution reconstruction; lightweight network; reparameterization; multi-scale feature extraction; channel attention mechanism; deep learning

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MDPI and ACS Style

Huang, F.; Wei, R.; Chen, L.; Qiu, Z.; Yang, X.; Ran, G.; Yuan, Y. Towards Lightweight and Accurate Remote-Sensing Image Super-Resolution via Reparameterized Feature Enhancement Network. Remote Sens. 2026, 18, 2850. https://doi.org/10.3390/rs18172850

AMA Style

Huang F, Wei R, Chen L, Qiu Z, Yang X, Ran G, Yuan Y. Towards Lightweight and Accurate Remote-Sensing Image Super-Resolution via Reparameterized Feature Enhancement Network. Remote Sensing. 2026; 18(17):2850. https://doi.org/10.3390/rs18172850

Chicago/Turabian Style

Huang, Feng, Renhui Wei, Liqiong Chen, Zhaobing Qiu, Xiangkun Yang, Gaozhu Ran, and Yangping Yuan. 2026. "Towards Lightweight and Accurate Remote-Sensing Image Super-Resolution via Reparameterized Feature Enhancement Network" Remote Sensing 18, no. 17: 2850. https://doi.org/10.3390/rs18172850

APA Style

Huang, F., Wei, R., Chen, L., Qiu, Z., Yang, X., Ran, G., & Yuan, Y. (2026). Towards Lightweight and Accurate Remote-Sensing Image Super-Resolution via Reparameterized Feature Enhancement Network. Remote Sensing, 18(17), 2850. https://doi.org/10.3390/rs18172850

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