Low-Light Image Enhancement via Structure Modeling and Guidance
Xiaogang Xu Ruixing Wang Jiangbo Lu
This paper proposes a new framework for low-light image enhancement by
simultaneously conducting the appearance as well as structure modeling. It
employs the structural feature to guide the appearance enhancement, leading to
sharp and realistic results. The structure modeling in our framework is
implemented as the edge detection in low-light images. It is achieved with a
modified generative model via designing a structure-aware feature extractor and
generator. The detected edge maps can accurately emphasize the essential
structural information, and the edge prediction is robust towards the noises in
dark areas. Moreover, to improve the appearance modeling, which is implemented
with a simple U-Net, a novel structure-guided enhancement module is proposed
with structure-guided feature synthesis layers. The appearance modeling, edge
detector, and enhancement module can be trained end-to-end. The experiments are
conducted on representative datasets (sRGB and RAW domains), showing that our
model consistently achieves SOTA performance on all datasets with the same
architecture.