Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909814494330880 |
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| author | Chen, Wankun Gao, Feng Gan, Yanhai Cao, Jingchao Dong, Junyu Du, Qian |
| author_facet | Chen, Wankun Gao, Feng Gan, Yanhai Cao, Jingchao Dong, Junyu Du, Qian |
| contents | Sea surface temperature (SST) is an essential indicator of global climate change and one of the most intuitive factors reflecting ocean conditions. Obtaining high-resolution SST data remains challenging due to limitations in physical imaging, and super-resolution via deep neural networks is a promising solution. Recently, Mamba-based approaches leveraging State Space Models (SSM) have demonstrated significant potential for long-range dependency modeling with linear complexity. However, their application to SST data super-resolution remains largely unexplored. To this end, we propose the Wavelet-assisted Mamba Super-Resolution (WMSR) framework for satellite-derived SST data. The WMSR includes two key components: the Low-Frequency State Space Module (LFSSM) and High-Frequency Enhancement Module (HFEM). The LFSSM uses 2D-SSM to capture global information of the input data, and the robust global modeling capabilities of SSM are exploited to preserve the critical temperature information in the low-frequency component. The HFEM employs the pixel difference convolution to match and correct the high-frequency feature, achieving accurate and clear textures. Through comprehensive experiments on three SST datasets, our WMSR demonstrated superior performance over state-of-the-art methods. Our codes and datasets will be made publicly available at https://github.com/oucailab/WMSR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution Chen, Wankun Gao, Feng Gan, Yanhai Cao, Jingchao Dong, Junyu Du, Qian Image and Video Processing Computer Vision and Pattern Recognition Sea surface temperature (SST) is an essential indicator of global climate change and one of the most intuitive factors reflecting ocean conditions. Obtaining high-resolution SST data remains challenging due to limitations in physical imaging, and super-resolution via deep neural networks is a promising solution. Recently, Mamba-based approaches leveraging State Space Models (SSM) have demonstrated significant potential for long-range dependency modeling with linear complexity. However, their application to SST data super-resolution remains largely unexplored. To this end, we propose the Wavelet-assisted Mamba Super-Resolution (WMSR) framework for satellite-derived SST data. The WMSR includes two key components: the Low-Frequency State Space Module (LFSSM) and High-Frequency Enhancement Module (HFEM). The LFSSM uses 2D-SSM to capture global information of the input data, and the robust global modeling capabilities of SSM are exploited to preserve the critical temperature information in the low-frequency component. The HFEM employs the pixel difference convolution to match and correct the high-frequency feature, achieving accurate and clear textures. Through comprehensive experiments on three SST datasets, our WMSR demonstrated superior performance over state-of-the-art methods. Our codes and datasets will be made publicly available at https://github.com/oucailab/WMSR. |
| title | Wavelet-Assisted Mamba for Satellite-Derived Sea Surface Temperature Super-Resolution |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.24334 |