This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks. A direct and integrated transfer between NIR and RGB pixels is trained. The trained model does not require any user guidance or a reference image database in the recall phase to produce images with a natural appearance. To preserve the rich details of the NIR image, its high frequency features are transferred to the estimated RGB image. The presented approach is trained and evaluated on a real-world dataset containing a large amount of road scene images in summer. The dataset was captured by a multi-CCD NIR/RGB camera, which ensures a perfect pixel to pixel registration.
@article{arxiv.1604.02245,
title = {Infrared Colorization Using Deep Convolutional Neural Networks},
author = {Matthias Limmer and Hendrik P. A. Lensch},
journal= {arXiv preprint arXiv:1604.02245},
year = {2016}
}
Comments
8 pages, 11 figures, 1 table, submitted to ICMLA2016