Reflection Removal With NIR and RGB Image Feature Fusion
Yuchen Hong, Youwei Lyu, Si Li, Gang Cao, Boxin Shi
Abstract
Removing undesirable reflections in photographs benefits both human perceptions and downstream computer vision tasks, but it is a highly ill-posed problem based on a single RGB image. Different from RGB images, near-infrared (NIR) images captured by an active NIR camera are less likely to be affected by reflections when glass and camera planes form certain angles, while textures on objects could “vanish” in some situations. Based on this observation, we propose a cascaded reflection removal network with an image feature fusion strategy to utilize auxiliary information in active NIR images. To tackle the insufficiency of training data, we propose a data generation pipeline to approximate perceptual properties and the reflection-suppressing nature of active NIR images. We further build a dataset with synthetic and real images to facilitate the research. Experimental results show that the proposed method outperforms state-of-the-art reflection removal methods in both quantitative metrics and visual quality.