Deep Multi-class Adversarial Specularity Removal
Abstract
We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single image by generating its diffuse component. To train the generative network, we define an adversarial loss on a discriminative network as in the GAN framework and combined it with a content loss. In contrast to existing GAN approaches, we implemented the discriminator to be a multi-class classifier instead of a binary one, to find more constraining features. This helps the network pinpoint the diffuse manifold by providing two more gradient terms. We also rendered a synthetic dataset designed to help the network generalize well. We show that our model performs well across various synthetic and real images and outperforms the state-of-the-art in consistency.
Cite
@article{arxiv.1904.02672,
title = {Deep Multi-class Adversarial Specularity Removal},
author = {John Lin and Mohamed El Amine Seddik and Mohamed Tamaazousti and Youssef Tamaazousti and Adrien Bartoli},
journal= {arXiv preprint arXiv:1904.02672},
year = {2019}
}