Specularity removal: A global energy minimization approach based on polarization imaging

Concentration of light energy in images causes strong highlights (specular reflection), and challenges the robustness of a large variety of vision algorithms, such as feature extraction and object detection. Many algorithms indeed assume perfect diffuse surfaces and ignore the specular reflections; specularity removal may thus be a preprocessing step to improve the accuracy of such algorithms. Regarding specularity removal, traditional color-based methods generate severe color distortions and local patch-based algorithms do not integrate long range information, which may result in artifacts. In this paper, we present a new image specularity removal method which is based on polarization imaging through global energy minimization. Polarization images provide complementary information and reduce color distortions. By minimizing a global energy function, our algorithm properly takes into account the long range cue and produces accurate and stable results. Compared to other polarization-based methods of the literature, our method obtains encouraging results, both in terms of accuracy and robustness.