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.

翻译：本文提出了一种通过同步进行外观与结构建模的低光图像增强新框架。该框架利用结构特征引导外观增强，从而生成清晰且逼真的结果。其中的结构建模通过低光图像边缘检测实现，我们设计了一个改进的生成模型，通过构建结构感知特征提取器和生成器来完成边缘检测。检测到的边缘图能够精确强调关键结构信息，且边缘预测对暗区噪声具有鲁棒性。此外，为改进采用简易U-Net实现的外观建模，我们提出了一种新型结构引导增强模块，该模块包含结构引导特征合成层。外观建模、边缘检测器和增强模块可实现端到端训练。在代表性数据集（sRGB与RAW域）上的实验表明，采用相同架构的模型在所有数据集上均持续达到最优性能。