论文标题
从阴影细分到删除阴影
From Shadow Segmentation to Shadow Removal
论文作者
论文摘要
对配对的阴影和无阴影图像的要求限制了阴影去除数据集的大小和多样性，并阻碍了训练大规模，稳健的阴影去除算法的可能性。我们提出了一种阴影去除方法，该方法只能使用阴影图像本身裁剪的阴影和非阴影贴片进行训练。我们的方法是按照阴影形成的物理模型通过对抗框架训练的。我们的核心贡献是一组基于物理的约束，可以实现这种对抗性训练。与经过完全配对的阴影和无阴影图像训练的最新方法相比，我们的方法可实现竞争性阴影的删除结果。我们培训制度的优势在删除视频的阴影中更加明显。我们的方法可以在测试视频中进行微调，其中只有由预训练的阴影探测器生成的阴影面具，并且在此挑战性测试中胜过最先进的方法。我们在建议的视频删除数据集上说明了我们方法的优势。
The requirement for paired shadow and shadow-free images limits the size and diversity of shadow removal datasets and hinders the possibility of training large-scale, robust shadow removal algorithms. We propose a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow images themselves. Our method is trained via an adversarial framework, following a physical model of shadow formation. Our central contribution is a set of physics-based constraints that enables this adversarial training. Our method achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. The advantages of our training regime are even more pronounced in shadow removal for videos. Our method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and outperforms state-of-the-art methods on this challenging test. We illustrate the advantages of our method on our proposed video shadow removal dataset.
