Low-Light Image Enhancement via Structure Modeling and Guidance
This addresses the problem of improving image quality in low-light conditions for computer vision applications, but it is incremental as it builds on existing enhancement methods.
The paper tackles low-light image enhancement by modeling both appearance and structure, using structural features to guide enhancement for sharper results. It achieves state-of-the-art performance on multiple datasets in sRGB and RAW domains.
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.