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

Fan Wang (1) , Samia Ainouz (2) , Caroline Petitjean (3) , Abdelaziz Bensrhair (4)

1 LIDyl - Laboratoire Interactions, Dynamiques et Lasers (ex SPAM)
2 INSA Rouen Normandie - Institut national des sciences appliquées Rouen Normandie
3 QuantIF-LITIS - Equipe Quantification en Imagerie Fonctionnelle
4 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

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 di↵use 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.

Keywords: specularity removal; polarization; Diffuse component; separation; energy minimization

Domains: Computer Science [cs]; Computer Vision and Pattern Recognition [cs.CV]

Fan Wang, Samia Ainouz, Caroline Petitjean, Abdelaziz Bensrhair. Specularity removal: a global energy minimization approach based on polarization imaging. Computer Vision and Image Understanding, 2017, ⟨10.1016/j.cviu.2017.03.003⟩. ⟨hal-02114528⟩