Irtorgb

Introduction

Near-Infrared (NIR) images have broad application in remote sensing and surveillance for its capacity to segment images according to object’s material. Although NIR images made object detection an easier task, its monochrome nature is conflicted with human visual perception and thus might not be user friendly. Lack of color discrimination or wrong color representation on NIR images would limit people’s understanding towards the image or even lead to wrong judgement. So colorizing the NIR grayscales images would be desired.

Colorization of NIR images is a difficult and challenging task since a single channel is mapped into a three dimensional space without interchannel correlation, which greatly reduces the effect of traditional color correction/transfer method. Moreover, since surface reflection in the NIR spectrum band is material dependent, some object might be missing from the NIR scenes due to their transparency to NIR. Therefore, IR colorization requires estimating not only chromiance, but also illuminance, which add a lot complexity to the problem.

In this project, we applied several machine learning algorithms like L3 (Local, Linear and Learned) and a neural network based model to find the appropriate mapping from NIR to RGB visible spectrum which human eyes are more sensitive to. We evaluate the results based on the CIELAB delta E metric for measuring difference in human perception level and the RMSE metric for quantitatively measuring pixels differences across image.

Appendix II

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