Deep spectral reflectance and illuminant estimation from self-interreflections

Rada Deeb, Joost van de Weijer, Damien Muselet, Mathieu Hebert, Alain Tremeau

    Research output: Contribution to journalArticleResearch

    9 Citations (Scopus)


    © 2018 Optical Society of America. In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.
    Original languageEnglish
    Pages (from-to)105-114
    JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
    Publication statusPublished - 1 Jan 2019


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