NEAR-INFRARED IMAGE GUIDED REFLECTION REMOVAL

Removing reflections from a single RGB image is a highly ill-posed problem. Unlike RGB images, near-infrared (NIR) images obtained through 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" under certain circumstances. Based on this observation, we propose a two-stream neural network to remove undesired reflections in an RGB image with the guidance of an NIR image. To tackle the insufficiency of training data, we propose a synthetic data generation pipeline that simulates the reflection-suppressing nature of the active NIR imaging and build a dataset mixed with synthetic and real data. Experimental results show that the proposed method outperforms state-of-the-art reflection removal methods in both quantitative metrics and visual quality.