Low light enhancement has gained increasing importance with the rapid development of visual creation and editing. However, most existing enhancement algorithms are designed to homogeneously increase the brightness of images to a pre-defined extent, limiting the user experience. To address this issue, we propose Controllable Light Enhancement Diffusion Model, dubbed CLE Diffusion, a novel diffusion framework to provide users with rich controllability. Built with a conditional diffusion model, we introduce an illumination embedding to let users control their desired brightness level. Additionally, we incorporate the Segment-Anything Model (SAM) to enable user-friendly region controllability, where users can click on objects to specify the regions they wish to enhance. Extensive experiments demonstrate that CLE Diffusion achieves competitive performance regarding quantitative metrics, qualitative results, and versatile controllability. Project page: \url{https://yuyangyin.github.io/CLEDiffusion/}

翻译：随着视觉创作与编辑技术的快速发展，低光照增强任务日益重要。然而，现有多数增强算法设计为对图像亮度进行同质性提升至预设程度，限制了用户体验。为应对该问题，我们提出可控光增强扩散模型（简称CLE Diffusion），这是一种新颖的扩散框架，旨在为用户提供丰富的可控性。基于条件扩散模型构建，我们引入光照嵌入（illumination embedding），使用户可控制所需的亮度等级。此外，我们集成Segment-Anything模型（SAM），实现用户友好的区域可控性——用户可通过点击对象来指定需要增强的区域。大量实验表明，CLE Diffusion在定量指标、定性结果及多功能可控性方面均取得了具有竞争力的性能。项目页面：\url{https://yuyangyin.github.io/CLEDiffusion/}