  Since specular reflection often exists in the real captured images and causes
deviation between the recorded color and intrinsic color, specular reflection
separation can bring advantages to multiple applications that require
consistent object surface appearance. However, due to the color of an object is
significantly influenced by the color of the illumination, the existing
researches still suffer from the near-duplicate challenge, that is, the
separation becomes unstable when the illumination color is close to the surface
color. In this paper, we derive a polarization guided model to incorporate the
polarization information into a designed iteration optimization separation
strategy to separate the specular reflection. Based on the analysis of
polarization, we propose a polarization guided model to generate a polarization
chromaticity image, which is able to reveal the geometrical profile of the
input image in complex scenarios, such as diversity of illumination. The
polarization chromaticity image can accurately cluster the pixels with similar
diffuse color. We further use the specular separation of all these clusters as
an implicit prior to ensure that the diffuse components will not be mistakenly
separated as the specular components. With the polarization guided model, we
reformulate the specular reflection separation into a unified optimization
function which can be solved by the ADMM strategy. The specular reflection will
be detected and separated jointly by RGB and polarimetric information. Both
qualitative and quantitative experimental results have shown that our method
can faithfully separate the specular reflection, especially in some challenging
scenarios.
