Near-Infrared Colorization using Neural Networks for In-Cabin Enhanced Video Conferencing

Madalina Chitu
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引用次数: 2

Abstract

Classical color pictures can not be captured in every scenario, due to the light condition limitations, especially when driving during the night. In those situations, cameras that capture the Near-Infrared spectrum of frequencies prevail. However, pictures from this domain have numerous differences to the ones in the visible spectrum. Therefore, processing Near-Infrared images is a task that requires extensive consideration. In this paper, a method for Near-Infrared image segmentation using artificial neural networks is proposed, together with a colorization algorithm and a novel pipeline for obtaining real-time realistic RGB images, with virtual background for in-cabin enhanced video conferencing.
基于神经网络的机舱增强型视频会议近红外着色
由于光线条件的限制,经典的彩色照片不能在每个场景中都被捕捉到,尤其是在夜间驾驶时。在这些情况下,捕捉近红外光谱频率的相机占上风。然而,这一领域的图像与可见光谱中的图像有许多不同之处。因此,处理近红外图像是一项需要广泛考虑的任务。本文提出了一种基于人工神经网络的近红外图像分割方法,并结合一种着色算法和一种获取实时逼真RGB图像的新管道,用于舱内增强视频会议的虚拟背景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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20
审稿时长
24 weeks
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