Zero-Shot Low-Light Image Enhancement via Latent Diffusion Models

Abstract

Low-light image enhancement (LLIE) aims to improve visibility and signal-to-noise ratio in images captured under poor lighting conditions. While deep learning has shown promise in this domain, current approaches require extensive paired training data, limiting their practical utility. We present a novel framework that reformulates low-light image enhancement as a zero-shot inference problem using pre-trained latent diffusion models (LDMs), eliminating the need for task-specific training data. Our key insight is that the rich natural image priors encoded in LDMs can be leveraged to recover well-lit images through a carefully designed optimization process. To address the ill-posed nature of low-light degradation and the complexity of latent space optimization, our framework introduces an exposure-aware degradation module that adaptively models illumination variations and a principled latent regularization scheme with adaptive guidance that ensures both enhancement quality and natural image statistics. Experimental results demonstrate that our framework outperforms existing zero-shot methods across diverse real-world scenarios.