RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes
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/
6960a8ce5b7998385e639615
Yuan-Kang Lee
claimed_verified
2026-01-09T08:24:03.428Z
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YuanKangNeilLee
NeilLeeNTU
user
NeilLeeNTU
6960a8ce5b7998385e639616
Kuan-Lin Chen
6960a8ce5b7998385e639617
Chia-Che Chang
6960a8ce5b7998385e639618
Yu-Lun Liu
admin_assigned
2026-01-09T15:50:20.802Z
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/avatars/57430d1bbde3a2fe5586e5fbcafb0e74.svg
Yu-Lun Liu
yulunliu
user
yulunliu
RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenes
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/
A novel nighttime color constancy framework combines statistical methods with deep reinforcement learning to improve white balance adjustment under low-light conditions.
deep reinforcement learning color constancy white balance statistical algorithms illumination estimation multi-sensor dataset