  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.
