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.
Sorour Mohajerani, Mark S. Drew, Parvaneh Saeedi, "Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal" in Proc. IS&T London Imaging Meeting 2020: Future Colour Imaging, 2020, pp 82 - 86, https://doi.org/10.2352/issn.2694-118X.2020.LIM-06