The Effects of Infrared Sensor Wavelength on Panchromatic Image Colorization Performance

Infrared (IR) imaging sensors, designed to detect the wavelength range between $0.9 \mu \mathrm{m}$ and $14 \mu \mathrm{m}$, offer unique advantages over daylight cameras in consumer, industrial, and defense applications. However, IR images lack natural color information and can be challenging for individuals without sensor-specific training to interpret. Consequently, transforming IR images into perceptually realistic color images represents a valuable research endeavor with significant commercial potential. Recently, various studies utilizing deep neural networks for colorizing single-mode (near-IR or thermal) infrared images have been reported. This article will apply a common neural network architecture to images captured with different imaging modes (near-IR, thermal IR, and low-light) for colorization and compare the results. These experiments will examine the influence of perceived wavelength on the colorization process.