A Model-Guided Unfolding Network for Single Image Reflection Removal

Dongliang Shao, Yunhui Shi, Jin Wang, Nam Ling, Baocai Yin

Abstract: Removing undesirable reflections from a single image captured through a glass surface is of broad application to various image processing and computer vision tasks, but it is an ill-posed and challenging problem. Existing traditional single image reflection removal(SIRR) methods are often less efficient to remove reflection due to the limited description ability of handcrafted priors. State-of-the-art learning based methods often cause instability problems because they are designed as unexplainable black boxes. In this paper, we present an explainable approach for SIRR named model-guided unfolding network(MoG-SIRR), which is unfolded from our proposed reflection removal model with non-local autoregressive prior and dereflection prior. In order to complement the transmission layer and the reflection layer in a single image, we construct a deep learning framework with two streams by integrating reflection removal and non-local regularization into trainable modules. Extensive experiments on public benchmark datasets demonstrate that our method achieves superior performance for single image reflection removal.