Nighttime color constancy 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 demonstrate that our method achieves superior generalization capability across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/

翻译：夜间色彩恒常性因低光照噪声与复杂光照条件，在计算摄影学中仍是一个具有挑战性的问题。我们提出RL-AWB，一种将统计方法与深度强化学习相结合的新型夜间白平衡框架。我们的方法始于一个专为夜间场景设计的统计算法，该算法将显著灰度像素检测与新颖的光照估计相结合。在此基础上，我们开发了首个用于色彩恒常性的深度强化学习方法，该方法以该统计算法为核心，通过动态优化每幅图像的参数来模拟专业AWB调校专家的行为。为了促进跨传感器评估，我们引入了首个多传感器夜间数据集。实验结果表明，我们的方法在低光照和良好光照图像上均实现了卓越的泛化能力。项目页面：https://ntuneillee.github.io/research/rl-awb/