ReF-LLE: Personalized Low-Light Enhancement via Reference-Guided Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2506.22216v1
• Date: Fri, 27 Jun 2025 13:35:34 GMT
• Title: ReF-LLE: Personalized Low-Light Enhancement via Reference-Guided Deep Reinforcement Learning
• Authors: Ming Zhao, Pingping Liu, Tongshun Zhang, Zhe Zhang,
• Abstract summary: ReF-LLE is a novel personalized low-light image enhancement method that operates in the Fourier frequency domain and incorporates deep reinforcement learning.<n>In inference phase, ReF-LLE employs a personalized adaptive iterative strategy, guided by the zero-frequency component in the Fourier domain.<n>Experiments on benchmark datasets demonstrate that ReF-LLE outperforms state-of-the-art methods.
• Score: 7.873244458995218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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