Highlight Specular Reflection Separation based on Tensor Low-rank and Sparse Decomposition Using Polarimetric Cues
This addresses the problem of color distortion in specular removal for computer vision applications, but it is incremental as it builds on existing low-rank decomposition frameworks with added polarization cues.
The paper tackles specular reflection removal in images by proposing a tensor low-rank and sparse decomposition method with polarization regularization, which significantly improves accuracy in recovering diffuse images, especially in strong specular or saturated regions, outperforming competitive methods.
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.