PT Journal
AU Rada Deeb
   Joost Van de Weijer
   Damien Muselet
   Mathieu Hebert
   Alain Tremeau
TI Deep spectral reflectance and illuminant estimation from self-interreflections
SO Journal of the Optical Society of America A
JI JOSA A
PY 2019
BP 105
EP 114
VL 31
IS 1
AB 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.
ER