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

TL;DR

RL-AWB introduces a deep reinforcement learning framework combined with statistical methods to improve auto white balance correction in low-light nighttime scenes, demonstrating superior generalization across sensors.

Contribution

It is the first to integrate statistical algorithms with deep reinforcement learning for nighttime white balance correction, enhancing adaptability and performance.

Findings

Achieves better generalization across different sensors and lighting conditions.

Outperforms existing methods in nighttime white balance correction.

Introduces a new multi-sensor nighttime dataset for evaluation.

Abstract

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