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

Abstract : 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.
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Contributor : Samia Ainouz-Zemouche Connect in order to contact the contributor
Submitted on : Monday, April 29, 2019 - 4:50:56 PM
Last modification on : Wednesday, March 2, 2022 - 10:10:10 AM


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  • HAL Id : hal-02114528, version 1


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



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