Optimal LED Spectral Multiplexing for NIR2RGB Translation
Optimal LED Spectral Multiplexing for NIR2RGB Translation
复制标题
DOI：
10.1109/cvpr52688.2022.01232
复制
发表时间：
2022-06
期刊：
影响因子：
--
通讯作者：
Lei Liu;Yuze Chen;Junchi Yan;Yinqiang Zheng
中科院分区：
文献类型：
--
作者：
Lei Liu;Yuze Chen;Junchi Yan;Yinqiang Zheng
夜间视频监控的行业惯例是使用辅助的近红外（NIR） led，通常以850nm或940nm为中心，用于场景照明。使用近红外led可以节省功耗，同时可以隐藏肉眼看不到的监控覆盖区域。捕获的图像几乎是单色的，视觉颜色和纹理往往会消失，这阻碍了人类和机器的感知。现有的一些研究试图通过深度学习将这种近红外图像转换为RGB图像，但不能提供令人满意的结果，也不能很好地推广到训练数据集之外。在本文中，我们旨在通过研究单片硅基RGB相机在近红外照明下的成像机制，打破可靠的NIR-to-RGB （NIR2RGB）转换的基本限制，并提出通过深度学习检索最佳LED复用。实验结果表明，与使用850nm和940nm的led相比，适当复用接近可见光谱范围的近红外led可以显著改善这种转换任务。
The industry practice for night video surveillance is to use auxiliary near-infrared (NIR) LEDs, usually centered at 850nm or 940nm, for scene illumination. NIR LEDs are used to save power consumption while hiding the surveillance coverage area from naked human eyes. The captured images are almost monochromatic, and visual color and texture tend to disappear, which hinders human and machine perception. A few existing studies have tried to convert such NIR images to RGB images through deep learning, which can not provide satisfying results, nor generalize well beyond the training dataset. In this paper, we aim to break the fundamental restrictions on reliable NIR-to-RGB (NIR2RGB) translation by examining the imaging mechanism of single-chip silicon-based RGB cameras under NIR illuminations, and propose to retrieve the optimal LED multiplexing via deep learning. Experimental results show that this translation task can be significantly improved by properly multiplexing NIR LEDs close to the visible spectral range than using 850nm and 940nm LEDs.