@Article{XavierSoria2018,
author="Xavier Soria
and Angel Sappa
and Riad I. Hammoud",
title="Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images",
journal="Sensors",
year="2018",
volume="18",
number="7",
pages="2059",
optkeywords="RGB-NIR sensor",
optkeywords="multispectral imaging",
optkeywords="deep learning",
optkeywords="CNNs",
abstract="Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700--1100 nm) cross-talking with the visible bands (400--700 nm).This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in differentscenarios and using different similarity metrics. Both of them improve the state of the art approaches.",
optnote="ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3145), last updated on Tue, 25 Jan 2022 09:56:41 +0100",
doi="10.3390/s18072059",
opturl="https://doi.org/10.3390/s18072059",
file=":http://refbase.cvc.uab.es/files/SSH2018.pdf:PDF"
}