Geometric-Aware Low-Light Image and Video Enhancement via Depth Guidance
Yingqi Lin Xiaogang Xu Yan Han Jiafei Wu Zhe Liu
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.