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This paper is concerned with specular reflection removal based on tensorlow-rank decomposition framework with the help of polarization information. Ourmethod is motivated by the observation that the specular highlight of an imageis sparsely distributed while the remaining diffuse reflection can be wellapproximated by a linear combination of several distinct colors using alow-rank and sparse decomposition framework. Unlike current solutions, ourtensor low-rank decomposition keeps the spatial structure of specular anddiffuse information which enables us to recover the diffuse image under strongspecular reflection or in saturated regions. We further define and impose a newpolarization regularization term as constraint on color channels. Thisregularization boosts the performance of the method to recover an accuratediffuse image by handling the color distortion, a common problem ofchromaticity-based methods, especially in case of strong specular reflection.Through comprehensive experiments on both synthetic and real polarizationimages, we demonstrate that our method is able to significantly improve theaccuracy of highlight specular removal, and outperform the competitive methodsto recover the diffuse image, especially in regions of strong specularreflection or in saturated areas.

Highlight Specular Reflection Separation based on Tensor Low-rank and Sparse Decomposition Using Polarimetric Cues