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Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal

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.

preprint2020arXivOpen access
Sorour MohajeraniMark S. DrewParvaneh Saeedi
Computer VisionGraphics
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Open access3 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Sorour MohajeraniMark S. DrewParvaneh Saeedi

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