Low-Light Enhancement (LLE) is aimed at improving the quality of photos/videos captured under low-light conditions. It is worth noting that most existing LLE methods do not take advantage of geometric modeling. We believe that incorporating geometric information can enhance LLE performance, as it provides insights into the physical structure of the scene that influences illumination conditions. To address this, we propose a Geometry-Guided Low-Light Enhancement Refine Framework (GG-LLERF) designed to assist low-light enhancement models in learning improved features for LLE by integrating geometric priors into the feature representation space. In this paper, we employ depth priors as the geometric representation. Our approach focuses on the integration of depth priors into various LLE frameworks using a unified methodology. This methodology comprises two key novel modules. First, a depth-aware feature extraction module is designed to inject depth priors into the image representation. Then, Hierarchical Depth-Guided Feature Fusion Module (HDGFFM) is formulated with a cross-domain attention mechanism, which combines depth-aware features with the original image features within the LLE model. We conducted extensive experiments on public low-light image and video enhancement benchmarks. The results illustrate that our designed framework significantly enhances existing LLE methods.

翻译：低光照增强（LLE）旨在提升低光条件下拍摄的照片/视频质量。值得注意的是，现有大多数LLE方法尚未充分利用几何建模。我们认为，融入几何信息能够提升LLE性能，因为几何信息提供了影响光照条件的场景物理结构洞察。为此，我们提出了一种几何引导的低光照增强精炼框架（GG-LLERF），通过将几何先验融入特征表示空间，辅助低光照增强模型学习更优的LLE特征。本文采用深度先验作为几何表征。我们的方法聚焦于通过统一方法论将深度先验集成到各类LLE框架中。该方法论包含两个关键创新模块：首先，设计深度感知特征提取模块，将深度先验注入图像表示；随后，构建分层深度引导特征融合模块（HDGFFM），该模块采用跨域注意力机制，将深度感知特征与LLE模型中的原始图像特征相结合。我们在公开的低光照图像与视频增强基准上进行了大量实验。结果表明，所提出的框架能显著提升现有LLE方法的性能。