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

Source: [Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal](<https://www.sota2.com/research/paper/68fa67791e209131cea169fb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:16:13.371Z

Authors: Sorour Mohajerani, Mark S. Drew, Parvaneh Saeedi

Published: 2020-05-04T22:51:36.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2005.01878>)

[PDF](<https://arxiv.org/pdf/2005.01878.pdf>)