Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise
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| Format: | Preprint |
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2025
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| _version_ | 1866914589119086592 |
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| author | Lin, Ruirui Huang, Guoxi Anantrasirichai, Nantheera |
| author_facet | Lin, Ruirui Huang, Guoxi Anantrasirichai, Nantheera |
| contents | Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy real-world noise because they do not sufficiently exploit long-term temporal cues. We propose DWTA-Net, a novel deep-learning recurrent LLVE framework with a recurrent design. DWTA-Net adopts an integrated two-stage architecture: Stage I restores local structure and color via multi-frame alignment for temporally consistent Mamba-based enhancement, while Stage II performs recurrent refinement using a novel dynamic weight-based temporal aggregation guided by optical flow, functioning as a recurrent denoiser that adapts to motion. We further introduce a texture-adaptive loss that preserves fine details in textured regions while suppressing noise in homogeneous areas. Experiments on real-world low-light footage show that DWTA-Net achieves stronger noise suppression and fewer artifacts, delivering superior visual quality compared with state-of-the-art methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_09450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise Lin, Ruirui Huang, Guoxi Anantrasirichai, Nantheera Computer Vision and Pattern Recognition Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy real-world noise because they do not sufficiently exploit long-term temporal cues. We propose DWTA-Net, a novel deep-learning recurrent LLVE framework with a recurrent design. DWTA-Net adopts an integrated two-stage architecture: Stage I restores local structure and color via multi-frame alignment for temporally consistent Mamba-based enhancement, while Stage II performs recurrent refinement using a novel dynamic weight-based temporal aggregation guided by optical flow, functioning as a recurrent denoiser that adapts to motion. We further introduce a texture-adaptive loss that preserves fine details in textured regions while suppressing noise in homogeneous areas. Experiments on real-world low-light footage show that DWTA-Net achieves stronger noise suppression and fewer artifacts, delivering superior visual quality compared with state-of-the-art methods. |
| title | Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.09450 |