A Pyramid Network Using Scale Agnostic Attention for Single Image Reflection Removal

Single Image Reflection Removal (SIRR) is a hot topic in downstream tasks of computer vision, with the aim of eliminating undesirable reflections in degraded images taken through glass. However, due to the ill-posed property of SIRR and the lack of large-scale real world mixture images contaminated by reflections, existing methods degrade on real datasets and suffer from the problem of reflection residue. In this work, we propose an efficient SIRR pyramid model that can robustly recover real images degraded by reflections. Specifically, our network uses a Nonlinear Activation Free Network (NAFNet) as a baseline, a variant of U-Net, which is capable of extracting feature maps of different scales. Furthermore, to learn the pixel-level long-distance feature correspondence between multi-scale features, a pyramid fusion module based on scale-agnostic attention has been embedded into the baseline. Comprehensive experimental results demonstrate the effectiveness of our model.