Physics Based Image Deshadowing Using Local Linear Model

Tamir Einy, Efrat Immer, Gilad Vered, Shai Avidan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 3012-3020

Image deshadowing algorithms remove shadows from images. This requires both detecting where the shadow is and, once detected, removing it from the image. This work focuses on the shadow removal part. We follow a common physical shadow formation model and learn its parameters using a deep neural network. Our model consists of an existing network for shadow detection, and a novel network for shadow removal. The shadow removal network gets the predicted mask of the shadow region and the shadow image and predicts six parameters per pixel. Remarkably, a straightforward network architecture, that is considerably smaller compared to alternative methods, produces better results on standard datasets.