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A dual-path feature reuse multi-scale network for remote sensing image super-resolution

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Abstract

Deep neural networks have achieved significant success in the super-resolution of remote sensing images. However, existing deep learning models still suffer from the issue of blurry pseudo-artifacts when restoring high-frequency details and textures. In this paper, a novel dual-path feature reuse multi-scale network (DFMNet) is proposed to more effectively utilize multi-scale features in remote sensing images, enhancing the detailed information in the restored images. Specifically, the designed dual-path feature reuse module adopts a symmetrical dual-path structure, with each path composed of convolutional layers of different sizes. This module enables deep feature reuse and multi-scale aggregation, improving the network’s ability to handle and restore high-frequency details in the images. Furthermore, a cross-attention module is introduced to facilitate deep interactive fusion of multi-scale image features produced by the encoder output. Comparative experiments conducted on challenging UCMerced and AID remote sensing datasets demonstrate that the proposed DFMNet achieves superior performance in both objective and subjective evaluations.

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Data availability

The data presented that support the findings of this study are available on request from the corresponding author.

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Funding

This work was supported in part by the "Land, Sea, and Air" Aerospace Science and Technology Project: Innovation and Application of Hainan Vitality Index Based on Satellite Data (ATIC-202302001); the Stable Supporting Fund of the Acoustic Science and Technology Laboratory (JCKYS2024604SSJS00301); the Undergraduate Innovation Team Project of Guangdong Ocean University (CXTD2024011); and the Open Fund of the Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching (2023B1212030003).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524000, Guangdong, China

    Huanling Xiao, Liuhui Luo & Cong Lin

  2. School of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524000, Guangdong, China

    Xintong Chen

  3. College of Information and Communication Engineering, Hainan University, Haikou, 570228, Hainan, China

    Cong Lin

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  1. Huanling Xiao
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Contributions

H.L. Xiao and X.T. Chen wrote the main manuscript text. All authors reviewed the manuscript. H.L. Xiao and L.L. Liu participated in the design of the model and experimental analysis, while C. Lin provided financial support. All authors reviewed and modified the manuscript.

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Correspondence to Cong Lin.

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Xiao, H., Chen, X., Luo, L. et al. A dual-path feature reuse multi-scale network for remote sensing image super-resolution. J Supercomput 81, 17 (2025). https://doi.org/10.1007/s11227-024-06569-w

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