基於深度強化學習的低光夜景自動白平衡校正技術

摘要

夜間色彩恆常性問題在計算攝影領域仍具挑戰性，主要源於低光環境下的噪聲干擾與複雜照明條件。本文提出RL-AWB創新框架，結合統計方法與深度強化學習實現夜間白平衡校正。我們首先設計專為夜間場景優化的統計算法，融合顯著灰色像素檢測與新型光照估計技術。在此基礎上，開發首個以統計算法為核心的深度強化學習色彩恆常性方法，通過動態優化每張影像的參數，模擬專業AWB調校專家的決策過程。為促進跨感測器評估，我們建構首個多感測器夜間影像資料集。實驗結果表明，該方法在低光照與正常光照影像上均展現卓越的泛化能力。專案頁面：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/

基於深度強化學習的低光夜景自動白平衡校正技術

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

摘要

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