用强化学习自动校正夜间低光照片的白平衡，无需参考图
RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes
- 结合统计方法与深度强化学习，动态调整参数
- 在多传感器夜间数据集上实现跨场景泛化
- 适合需要自适应白平衡的夜景摄影应用
夜间色彩恒常性在计算摄影中仍是难题，主要因低光噪声和复杂光照条件。本文提出RL-AWB框架，将统计方法与深度强化学习结合，用于夜间白平衡校正。方法从针对夜间场景设计的统计算法出发，融合显著灰像素检测与新型光源估计；在此基础上，首次构建基于深度强化学习的色彩恒常性方法，以统计算法为核心，模仿专业白平衡调校专家，在推理时动态确定图像特定参数，无需真实光源或参考图像。为促进跨传感器评估，我们构建首个多传感器夜间数据集。实验表明，该方法在低光与正常光照图像间均具备强泛化能力。
原文摘要 · Abstract (English)
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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