Two-Stage Random Alternation Framework for One-Shot Pansharpening
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arXiv
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| Format: | Preprint |
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2025
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| author | Chen, Haorui Ren, Zeyu Ren, Jiaxuan Ran, Ran Shao, Jinliang Huang, Jie Deng, Liangjian |
| author_facet | Chen, Haorui Ren, Zeyu Ren, Jiaxuan Ran, Ran Shao, Jinliang Huang, Jie Deng, Liangjian |
| contents | Deep learning has substantially advanced pansharpening, achieving impressive fusion quality. However, a prevalent limitation is that conventional deep learning models, which typically rely on training datasets, often exhibit suboptimal generalization to unseen real-world image pairs. This restricts their practical utility when faced with real-world scenarios not included in the training datasets. To overcome this, we introduce a two-stage random alternating framework (TRA-PAN) that performs instance-specific optimization for any given Multispectral(MS)/Panchromatic(PAN) pair, ensuring robust and high-quality fusion. TRA-PAN effectively integrates strong supervision constraints from reduced-resolution images with the physical characteristics of the full-resolution images. The first stage introduces a pre-training procedure, which includes Degradation-Aware Modeling (DAM) to capture spectral degradation mappings, alongside a warm-up procedure designed to reduce training time and mitigate the adverse effects of reduced-resolution data. The second stage employs Random Alternation Optimization (RAO), randomly alternating between reduced- and full-resolution images to refine the fusion model progressively. This adaptive, per-instance optimization strategy, operating in a one-shot manner for each MS/PAN pair, yields superior high-resolution multispectral images. Experimental results demonstrate that TRA-PAN outperforms state-of-the-art (SOTA) methods in quantitative metrics and visual quality in real-world scenarios, underscoring its enhanced practical applicability and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06576 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Two-Stage Random Alternation Framework for One-Shot Pansharpening Chen, Haorui Ren, Zeyu Ren, Jiaxuan Ran, Ran Shao, Jinliang Huang, Jie Deng, Liangjian Computer Vision and Pattern Recognition Artificial Intelligence Deep learning has substantially advanced pansharpening, achieving impressive fusion quality. However, a prevalent limitation is that conventional deep learning models, which typically rely on training datasets, often exhibit suboptimal generalization to unseen real-world image pairs. This restricts their practical utility when faced with real-world scenarios not included in the training datasets. To overcome this, we introduce a two-stage random alternating framework (TRA-PAN) that performs instance-specific optimization for any given Multispectral(MS)/Panchromatic(PAN) pair, ensuring robust and high-quality fusion. TRA-PAN effectively integrates strong supervision constraints from reduced-resolution images with the physical characteristics of the full-resolution images. The first stage introduces a pre-training procedure, which includes Degradation-Aware Modeling (DAM) to capture spectral degradation mappings, alongside a warm-up procedure designed to reduce training time and mitigate the adverse effects of reduced-resolution data. The second stage employs Random Alternation Optimization (RAO), randomly alternating between reduced- and full-resolution images to refine the fusion model progressively. This adaptive, per-instance optimization strategy, operating in a one-shot manner for each MS/PAN pair, yields superior high-resolution multispectral images. Experimental results demonstrate that TRA-PAN outperforms state-of-the-art (SOTA) methods in quantitative metrics and visual quality in real-world scenarios, underscoring its enhanced practical applicability and robustness. |
| title | Two-Stage Random Alternation Framework for One-Shot Pansharpening |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.06576 |