Single image reflection removal with edge guidance, reflection classifier, and recurrent decomposition

Removing undesired reflection from an image captured through a glass window is a notable task in computer vision. In this paper, we propose a novel model with auxiliary techniques to tackle the problem of single image reflection removal. Our model takes a reflection contaminated image as input, and decomposes it into the reflection layer and the transmission layer. In order to ensure quality of the transmission layer, we introduce three auxiliary techniques into our architecture, including the edge guidance, a reflection classifier, and the recurrent decomposition. The contributions and the efficacy of these techniques are investigated and verified in the ablation study. Furthermore, in comparison to the state-of-the-art baselines of reflection removal, both quantitative and qualitative results demonstrate that our proposed method is able to deal with different kinds of images, achieving the best results in average.