RL-AWB：基于深度强化学习的低光照夜间场景自动白平衡校正

摘要

夜间色彩恒常性因低光照噪声和复杂照明条件，始终是计算摄影领域的难点。我们提出RL-AWB——一种融合统计方法与深度强化学习的夜间白平衡创新框架。该方法首先采用专为夜间场景设计的统计算法，将显著灰度像素检测与新型光照估计相结合。在此基础上，我们开发了首个以该统计算法为核心的色彩恒常性深度强化学习方法，通过动态优化每张图像的参数，模拟专业AWB调校专家的操作流程。为促进跨传感器评估，我们构建了首个多传感器夜间数据集。实验结果表明，该方法在低光照与正常光照图像上均展现出卓越的泛化能力。项目页面：https://ntuneillee.github.io/research/rl-awb/

English

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/

RL-AWB：基于深度强化学习的低光照夜间场景自动白平衡校正

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

摘要

Support