Temporal-Synergistic Policy Optimization for Unsupervised Low-Light Image Enhancement

Abstract

Diffusion models show significant potential for low-light image enhancement. However, this task requires satisfying human perceptual preferences and content fidelity transcending simple brightness and color improvement. Existing methods rely on heuristic physical priors or incorporate perceptual metrics directly into timestep-wise training objectives. Such proxy constraints fail to provide reliable trajectory-level guidance for perceptual alignment. Furthermore, they often lead to artifacts or unnatural visual effects in this ill-posed inverse problem. To address these issues, we propose an unsupervised low-light enhancement framework based on Group Relative Policy Optimization (GRPO), which utilizes perceptual preferences to directly optimize the diffusion policy. We introduce a Sliding Window Hybrid ODE-SDE Sampling strategy that confines stochasticity to dynamic sub-intervals, thereby achieving efficient coarse-to-fine exploration and precise advantage attribution. To meet strict fidelity constraints, we construct a Synergistic Perception-Fidelity Reward and introduce an Independent Advantage Estimation strategy to mitigate signal collapse and gradient suppression in multi-objective optimization. Furthermore, we design a Temporal-Aware Dynamic Weighting Mechanism that adaptively adjusts perceptual weights across denoising stages to balance visual enhancement with structural preservation. Extensive experiments on multiple real-world benchmarks demonstrate that our method effectively improves the perceptual performance of existing generative models, yielding results that better align with human aesthetics.