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

Nighttime color constancy still remains a challenging prob-lem in computational photography due to low-light noise and complexillumination conditions. We present RL-AWB, a novel framework com-bining statistical methods with deep reinforcement learning for night-time white balance. Our method begins with a statistical algorithmtailored for nighttime scenes, integrating salient gray pixel detectionwith novel illuminant estimation. Building on this foundation, we de-velop the first deep reinforcement learning approach for color constancythat leverages the statistical algorithm as its core, mimicking profes-sional AWB tuning experts by dynamically determining image-specificparameters at inference time, without requiring ground-truth illumi-nants or reference images. To further facilitate cross-sensor evaluation,we introduce the first multi-sensor nighttime dataset. Experiment re-sults demonstrate that our method achieves strong generalization capa-bility across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/