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arXiv · 2601.05249

RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes

Yuan-Kang Lee·Kuan-Lin Chen·Chia-Che Chang·Yu-Lun Liu
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Abstract

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 illuminant 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 determining image-specific parameters at inference time, without requiring ground-truth illuminants or reference images. To further facilitate cross-sensor evaluation, we introduce the first multi-sensor nighttime dataset. Experiment results demonstrate that our method achieves strong generalization capability across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/

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Yuan-Kang Lee, Kuan-Lin Chen, Chia-Che Chang, Yu-Lun Liu. 2026-07-08. RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes. https://arxiv.org/abs/2601.05249

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