IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910219025514496 |
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| author | Chen, Yifan Yin, Fei Guo, Chunle Li, Chongyi Yang, Yujiu |
| author_facet | Chen, Yifan Yin, Fei Guo, Chunle Li, Chongyi Yang, Yujiu |
| contents | In nighttime circumstances, it is challenging for individuals and machines to perceive their surroundings. While prevailing image restoration methods adeptly handle singular forms of degradation, they falter when confronted with intricate nocturnal scenes, such as the concurrent presence of weather and low-light conditions. Compounding this challenge, the lack of paired data that encapsulates the coexistence of low-light situations and other forms of degradation hinders the development of a comprehensive end-to-end solution. In this work, we contribute complex nighttime scene datasets that simulate both illumination degradation and other forms of deterioration. To address the complexity of night degradation, we propose an integration of an illumination-guided module embedded in the diffusion model to guide the illumination restoration process. Our model can preserve texture fidelity while contending with the adversities posed by various degradation in low-light scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14337 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model Chen, Yifan Yin, Fei Guo, Chunle Li, Chongyi Yang, Yujiu Computer Vision and Pattern Recognition In nighttime circumstances, it is challenging for individuals and machines to perceive their surroundings. While prevailing image restoration methods adeptly handle singular forms of degradation, they falter when confronted with intricate nocturnal scenes, such as the concurrent presence of weather and low-light conditions. Compounding this challenge, the lack of paired data that encapsulates the coexistence of low-light situations and other forms of degradation hinders the development of a comprehensive end-to-end solution. In this work, we contribute complex nighttime scene datasets that simulate both illumination degradation and other forms of deterioration. To address the complexity of night degradation, we propose an integration of an illumination-guided module embedded in the diffusion model to guide the illumination restoration process. Our model can preserve texture fidelity while contending with the adversities posed by various degradation in low-light scenarios. |
| title | IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.14337 |