Region-Aware Reflection Removal With Unified Content and Gradient Priors
Removing the undesired reflections in images taken through the glass is of broad application to various image processing and computer vision tasks. Existing single image-based solutions heavily rely on scene priors such as separable sparse gradients caused by different levels of blur, and they are fragile when such priors are not observed. In this paper, we notice that strong reflections usually dominant a limited region in the whole image, and propose a region-aware reflection removal approach by automatically detecting and heterogeneously processing regions with and without reflections. We integrate content and gradient priors to jointly achieve missing contents restoration, as well as background and reflection separation, in a unified optimization framework. Extensive validation using 50 sets of real data shows that the proposed method outperforms state-of-the-art on both quantitative metrics and visual qualities.