Single Image Reflection Removal with Physically-based Rendering

Recently, deep learning based single image reflection separation methods have been exploited widely. To benefit the learning approach, a large number of training image pairs (i.e., with and without reflections) were synthesized in various ways, yet they are away from a physically-based direction. In this paper, physically based rendering is used for faithfully synthesizing the required training images, and corresponding network structure is proposed. We utilize existing image data to estimate mesh, then physically simulate the depth-dependent light transportation between mesh, glass, and lens with path tracing. For guiding the separation better, we additionally consider a module of removing complicated ghosting and blurring glass-effects, which allows obtaining priori information before having the glass distortion. This module is easily accommodated within our approach, since that prior information can be physically generated by our rendering process. The proposed method considering the priori information as well as the existing posterior information is validated with various real reflection images, and is demonstrated to show visually pleasant and numerically better results compared to the state-of-theart techniques.