From Shadow Segmentation to Shadow Removal

• URL: http://arxiv.org/abs/2008.00267v1
• Date: Sat, 1 Aug 2020 14:00:10 GMT
• Title: From Shadow Segmentation to Shadow Removal
• Authors: Hieu Le and Dimitris Samaras
• Abstract summary: The requirement for paired shadow and shadow-free images limits the size and diversity of shadow removal datasets. We propose a shadow removal method that can be trained using only shadow and non-shadow patches cropped from the shadow images themselves.
• Score: 34.762493656937366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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