<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Huang, Yan</style></author><author><style face="normal" font="default" size="100%">Liao, Xiaoshan</style></author><author><style face="normal" font="default" size="100%">Liang, Jinxiu</style></author><author><style face="normal" font="default" size="100%">Quan, Yuhui</style></author><author><style face="normal" font="default" size="100%">Shi, Boxin</style></author><author><style face="normal" font="default" size="100%">Yong Xu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zero-Shot Low-Light Image Enhancement via Latent Diffusion Models</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>