Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of aligned shadowed and non-shadowed images which are difficult to obtain. We introduce a novel weakly supervised shadow removal framework UnShadowNet trained using contrastive learning. It is composed of a DeShadower network responsible for the removal of the extracted shadow under the guidance of an Illumination network which is trained adversarially by the illumination critic and a Refinement network to further remove artefacts. We show that UnShadowNet can be easily extended to a fully-supervised set-up to exploit the ground-truth when available. UnShadowNet outperforms existing state-of-the-art approaches on three publicly available shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully supervised setups.

翻译：阴影是常见的自然现象，在自动驾驶等实际场景中会严重阻碍计算机视觉感知系统的性能。解决该问题的一种方法是在感知系统处理前消除图像中的阴影区域。然而，训练此类方法需要对齐的含阴影与无阴影图像对，这类数据难以获取。我们提出一种新型弱监督阴影去除框架UnShadowNet，采用对比学习进行训练。该框架包含一个阴影去除网络，其在光照网络引导下对提取的阴影进行去除；光照网络通过光照判别器的对抗训练进行优化；此外还包含一个细化网络以进一步消除伪影。我们证明UnShadowNet可轻松扩展为全监督设置，在具备真实标注时充分利用数据。在三个公开阴影数据集（ISTD、adjusted ISTD、SRD）上，UnShadowNet在弱监督和全监督 setups 下均优于现有最先进方法。