This project aims to use deep learning method for shadow removal. Inspired by physical models of shadow formation, they use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. The model uses two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively.
Le, H., & Samaras, D. (2019). Shadow removal via shadow image decomposition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 8578-8587).
The python file train.py under the src/model directory contains the training code. We use VGG16 and UNet-256 for our training over the images.
To run inference use the inference.py file also under the similar dir using the command :
python3 inference.py
The train.py file also holds the option() function which can be used to change the model parameters.
