@inproceedings{f27a9a72c86a4b61ba663812f13a0f5d,
title = "Deep multiresolution color constancy",
abstract = "In this paper, a computational color constancy method is proposed via estimating the illuminant chromaticity in a scene by pooling from many local estimates. To this end, first, for each image in a dataset, we form an image pyramid consisting of several scales of the original image. Next, local patches of certain size are extracted from each scale in this image pyramid. Then, a convolutional neural network is trained to estimate the illuminant chromaticity per-patch. Finally, two more consecutive trainings are conducted, where the estimation is made per-image via taking the mean (1st training) and median (2nd training) of local estimates. The proposed method is shown to outperform the state-of-the-art in a widely used color constancy dataset.",
keywords = "Color constancy, Deep learning, Illuminant chromaticity estimation, Local estimation, Multi-resolution",
author = "Caglar Aytekin and Jarno Nikkanen and Moncef Gabbouj",
note = "jufoid=57423; IEEE International Conference on Image Processing ; Conference date: 01-01-1900",
year = "2018",
month = feb,
day = "20",
doi = "10.1109/ICIP.2017.8296980",
language = "English",
publisher = "IEEE COMPUTER SOCIETY PRESS",
pages = "3735--3739",
booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",
}