  This paper is concerned with specular reflection removal based on tensor
low-rank decomposition framework with the help of polarization information. Our
method is motivated by the observation that the specular highlight of an image
is sparsely distributed while the remaining diffuse reflection can be well
approximated by a linear combination of several distinct colors using a
low-rank and sparse decomposition framework. Unlike current solutions, our
tensor low-rank decomposition keeps the spatial structure of specular and
diffuse information which enables us to recover the diffuse image under strong
specular reflection or in saturated regions. We further define and impose a new
polarization regularization term as constraint on color channels. This
regularization boosts the performance of the method to recover an accurate
diffuse image by handling the color distortion, a common problem of
chromaticity-based methods, especially in case of strong specular reflection.
Through comprehensive experiments on both synthetic and real polarization
images, we demonstrate that our method is able to significantly improve the
accuracy of highlight specular removal, and outperform the competitive methods
to recover the diffuse image, especially in regions of strong specular
reflection or in saturated areas.
