DFSMamba: A Spatial–Frequency Collaborative Modeling Framework for Remote Sensing Image Super-Resolution.
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| Title: | DFSMamba: A Spatial–Frequency Collaborative Modeling Framework for Remote Sensing Image Super-Resolution. |
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| Authors: | Yu, Jie1,2 (AUTHOR), Li, Hui2,3 (AUTHOR), Zheng, Xiangyong3,4 (AUTHOR), Zhong, Cheng1,2,4 (AUTHOR), Sun, Qiao5 (AUTHOR) sunqiao_zju@zju.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1910. 26p. |
| Subjects: | Remote sensing, Discrete Fourier transforms, Image reconstruction, High resolution imaging, State-space methods |
| Abstract: | Highlights: What are the main findings? A spatial–frequency synergistic super-resolution network, DFSMamba, is proposed for remote sensing images, combining DFTM and ASSMamba. DFSMamba achieves SOTA performance with fewer parameters and lower computation, excelling in edge and texture detail reconstruction. What are the implications of the main findings? The method provides a new solution to insufficient global receptive fields and weak high-frequency recovery in RS image SR. DFTM and ASSMamba can be used as plug-and-play modules for other remote sensing image processing tasks. Existing single-image super-resolution methods for remote sensing images suffer from insufficient global receptive fields, weak high-frequency texture recovery, and excessive computational complexity. To address these issues, this paper proposes DFSMamba, a novel spatial–frequency collaborative modeling framework. First, Semantic Continuous-Sparse Attention enhances semantic perception through dynamic chunking and sparse connections while maintaining linear complexity, effectively alleviating the semantic truncation problem caused by fixed window partitioning. Second, the Adaptive State-Space Module employs parallel forward and backward state-space model branches to achieve bidirectional long-range dependency modeling and introduces an activation-guided feature fusion mechanism to adaptively enhance semantically relevant regions. Third, the Discrete Fourier Transform Module maps images to the frequency domain, establishes a global lossless receptive field, and explicitly enhances high-frequency details, compensating for the insufficient utilization of frequency-domain information in pure spatial-domain methods. Experiments on five public datasets demonstrate that DFSMamba outperforms mainstream CNN, Transformer, and Mamba-based methods across ×2 to ×4 scales. On the AID×3 task, it achieves a PSNR of 31.48 dB, exceeding MambaIRv2 by 1.07 dB. Ablation studies verify the positive synergistic effect of the three modules, with the full configuration achieving a PSNR improvement of 0.85 dB over the single-module setup. Fine-grained category, multi-scale input, and loss function experiments further confirm its robustness and generalization capability, particularly in edge and texture detail reconstruction. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A spatial–frequency synergistic super-resolution network, DFSMamba, is proposed for remote sensing images, combining DFTM and ASSMamba. DFSMamba achieves SOTA performance with fewer parameters and lower computation, excelling in edge and texture detail reconstruction. What are the implications of the main findings? The method provides a new solution to insufficient global receptive fields and weak high-frequency recovery in RS image SR. DFTM and ASSMamba can be used as plug-and-play modules for other remote sensing image processing tasks. Existing single-image super-resolution methods for remote sensing images suffer from insufficient global receptive fields, weak high-frequency texture recovery, and excessive computational complexity. To address these issues, this paper proposes DFSMamba, a novel spatial–frequency collaborative modeling framework. First, Semantic Continuous-Sparse Attention enhances semantic perception through dynamic chunking and sparse connections while maintaining linear complexity, effectively alleviating the semantic truncation problem caused by fixed window partitioning. Second, the Adaptive State-Space Module employs parallel forward and backward state-space model branches to achieve bidirectional long-range dependency modeling and introduces an activation-guided feature fusion mechanism to adaptively enhance semantically relevant regions. Third, the Discrete Fourier Transform Module maps images to the frequency domain, establishes a global lossless receptive field, and explicitly enhances high-frequency details, compensating for the insufficient utilization of frequency-domain information in pure spatial-domain methods. Experiments on five public datasets demonstrate that DFSMamba outperforms mainstream CNN, Transformer, and Mamba-based methods across ×2 to ×4 scales. On the AID×3 task, it achieves a PSNR of 31.48 dB, exceeding MambaIRv2 by 1.07 dB. Ablation studies verify the positive synergistic effect of the three modules, with the full configuration achieving a PSNR improvement of 0.85 dB over the single-module setup. Fine-grained category, multi-scale input, and loss function experiments further confirm its robustness and generalization capability, particularly in edge and texture detail reconstruction. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121910 |
