ReF-LLE: Personalized Low-Light Enhancement via Reference-Guided Deep Reinforcement Learning
This work addresses personalized enhancement for low-light images, which is incremental as it builds on existing methods by adding personalization and reinforcement learning.
The paper tackled the challenges of low-light image enhancement by proposing ReF-LLE, a method that uses deep reinforcement learning in the Fourier domain to personalize enhancement based on user reference images, achieving superior perceptual quality and adaptability compared to state-of-the-art methods.
Low-light image enhancement presents two primary challenges: 1) Significant variations in low-light images across different conditions, and 2) Enhancement levels influenced by subjective preferences and user intent. To address these issues, we propose ReF-LLE, a novel personalized low-light image enhancement method that operates in the Fourier frequency domain and incorporates deep reinforcement learning. ReF-LLE is the first to integrate deep reinforcement learning into this domain. During training, a zero-reference image evaluation strategy is introduced to score enhanced images, providing reward signals that guide the model to handle varying degrees of low-light conditions effectively. In the inference phase, ReF-LLE employs a personalized adaptive iterative strategy, guided by the zero-frequency component in the Fourier domain, which represents the overall illumination level. This strategy enables the model to adaptively adjust low-light images to align with the illumination distribution of a user-provided reference image, ensuring personalized enhancement results. Extensive experiments on benchmark datasets demonstrate that ReF-LLE outperforms state-of-the-art methods, achieving superior perceptual quality and adaptability in personalized low-light image enhancement.