TY  - JOUR
AU  - Rada Deeb
AU  - Joost Van de Weijer
AU  - Damien Muselet
AU  - Mathieu Hebert
AU  - Alain Tremeau
PY  - 2019//
TI  - Deep spectral reflectance and illuminant estimation from self-interreflections
T2  - JOSA A
JO  - Journal of the Optical Society of America A
SP  - 105
EP  - 114
VL  - 31
IS  - 1
N2  - 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.
UR  - https://doi.org/10.1364/JOSAA.36.000105
L1  - http://refbase.cvc.uab.es/files/DWM2019.pdf
N1  - LAMP; 600.120
ID  - Rada Deeb2019
ER  -