论文标题
来自4通道图像的照明不变图像：近红外数据在阴影中的影响
Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal
论文作者
论文摘要
在许多计算机视觉应用中，例如对象识别和语义细分，删除图像中照明变化的效果已被证明是有益的。尽管以前在文献中已经研究了生成照明不变的图像，但尚未对实际的4通道（4D）数据进行了研究。在这项研究中，我们研究了由红色，绿色，蓝色和近红外（RGBN）数据产生的照明不变图像的质量。我们的实验表明，近红外通道实质上有助于去除照明。如我们的数值和视觉结果所示，与单独的RGB相比，通过RGBN数据获得的照明不变图像优越。
Removing the effect of illumination variation in images has been proved to be beneficial in many computer vision applications such as object recognition and semantic segmentation. Although generating illumination-invariant images has been studied in the literature before, it has not been investigated on real 4-channel (4D) data. In this study, we examine the quality of illumination-invariant images generated from red, green, blue, and near-infrared (RGBN) data. Our experiments show that the near-infrared channel substantively contributes toward removing illumination. As shown in our numerical and visual results, the illumination-invariant image obtained by RGBN data is superior compared to that obtained by RGB alone.
