  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 a corresponding network structure and loss term
are proposed. We utilize existing RGBD/RGB images to estimate meshes, then
physically simulate the light transportation between meshes, glass, and lens
with path tracing to synthesize training data, which successfully reproduce the
spatially variant anisotropic visual effect of glass reflection. For guiding
the separation better, we additionally consider a module, backtrack network
(BT-net) for backtracking the reflections, which removes complicated ghosting,
attenuation, blurred and defocused effect of glass/lens. This enables obtaining
a priori information before having the distortion. The proposed method
considering additional a priori information with physically simulated training
data is validated with various real reflection images and shows visually
pleasant and numerical advantages compared with state-of-the-art techniques.
