@conference {huang2024zeroshot,
	title = {Zero-Shot Low-Light Image Enhancement via Latent Diffusion Models},
	booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
	year = {2025},
	abstract = {Low-light image enhancement (LLIE) aims to improve visibility and signal-to-noise ratio in images captured under poor lighting conditions. Despite impressive improvement, deep learning-based LLIE approaches require extensive training data, which is often difficult and costly to obtain. In this paper, we propose a zero-shot LLIE framework leveraging pre-trained latent diffusion models for the first time, which act as powerful priors to recover latent images from low-light inputs. Our approach introduces several components to alleviate the inherent challenges in utilizing pre-trained latent diffusion models, modeling the degradation process in an image-adaptive manner, penalizing the latent outside the manifold of natural images, and balancing the strengths of the guidance from the given low-light image during the denoising process. Experimental results demonstrate that our framework outperforms existing methods, achieving superior performance across various datasets.},
	author = {Huang, Yan and Liao, Xiaoshan and Liang, Jinxiu and Quan, Yuhui and Shi, Boxin and Yong Xu}
}
