Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement.

翻译：低光照图像增强（LLIE）旨在研究如何提升光照并生成正常光照图像。现有方法大多以全局统一方式改善低光照图像，未考虑不同区域的语义信息。缺乏语义先验会导致网络容易偏离区域原始色彩。为解决该问题，我们提出一种新颖的语义感知知识引导框架（SKF），可帮助低光照增强模型学习语义分割模型所蕴含的丰富且多样的先验信息。我们重点从三个关键方面融合语义知识：语义感知嵌入模块在特征表示空间中智能整合语义先验信息；语义引导颜色直方图损失保持不同实例的色彩一致性；语义引导对抗损失通过语义先验生成更自然的纹理。所提出的SKF可作为LLIE任务的通用框架。大量实验表明，装备SKF的模型在多个数据集上显著超越基线方法，且SKF能良好泛化至不同模型与场景。代码已开源至Semantic-Aware-Low-Light-Image-Enhancement。