Illuminant Estimation Using Convolutional Neural Networks (CNNs)

In this paper we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a Convolutional Neural Network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard datasets with single and multiple illuminants, proves the effectiveness of our method.

Publications

Color Constancy Using CNNs
(Simone Bianco, Claudio Cusano, Raimondo Schettini) In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Deep Vision: Deep Learning in Computer Vision, pp. 81-89, 2015.

Download BibTex

@inproceedings{bianco2015color-constancy,
 author = {Bianco, Simone and Cusano, Claudio and Schettini, Raimondo},
 year = {2015},
 pages = {81-89},
 title = {Color Constancy Using CNNs},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Deep Vision: Deep Learning in Computer Vision},
 pdf = {http://arxiv.org/pdf/1504.04548v1.pdf}}