Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal
This work addresses illumination variation in images for computer vision applications like object recognition, but it is incremental as it extends existing methods to new data.
The study tackled the problem of generating illumination-invariant images from 4-channel RGBN data to remove illumination effects like shadows, and found that including near-infrared data significantly improves quality over RGB alone, as shown in numerical and visual results.
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.