RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866910102032744448 |
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| author | Lee, Yuan-Kang Chen, Kuan-Lin Chang, Chia-Che Liu, Yu-Lun |
| author_facet | Lee, Yuan-Kang Chen, Kuan-Lin Chang, Chia-Che Liu, Yu-Lun |
| contents | Nighttime color constancy still remains a challenging problem in computational photography due to low-light noise and complex illumination conditions. We present RL-AWB, a novel framework combining statistical methods with deep reinforcement learning for nighttime white balance. Our method begins with a statistical algorithm tailored for nighttime scenes, integrating salient gray pixel detection with novel illumination estimation. Building on this foundation, we develop the first deep reinforcement learning approach for color constancy that leverages the statistical algorithm as its core, mimicking professional AWB tuning experts by dynamically optimizing parameters for each image. To facilitate cross-sensor evaluation, we introduce the first multi-sensor nighttime dataset. Experiment results show that our method achieves superior generalization capability across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05249 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes Lee, Yuan-Kang Chen, Kuan-Lin Chang, Chia-Che Liu, Yu-Lun Computer Vision and Pattern Recognition Nighttime color constancy still remains a challenging problem in computational photography due to low-light noise and complex illumination conditions. We present RL-AWB, a novel framework combining statistical methods with deep reinforcement learning for nighttime white balance. Our method begins with a statistical algorithm tailored for nighttime scenes, integrating salient gray pixel detection with novel illumination estimation. Building on this foundation, we develop the first deep reinforcement learning approach for color constancy that leverages the statistical algorithm as its core, mimicking professional AWB tuning experts by dynamically optimizing parameters for each image. To facilitate cross-sensor evaluation, we introduce the first multi-sensor nighttime dataset. Experiment results show that our method achieves superior generalization capability across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/ |
| title | RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.05249 |