Multi-mode infrared image colorization

Infrared (IR) imaging sensors designed to acquire the 0.9 to 14 micrometers wavelength band offer unique advantages over the daylight cameras for a multitude of consumer, industrial and defense applications. However, IR images lack natural color information and can be quite challenging to interpret without sensor specific training. As a result, transforming IR images into perceptually realistic color images is a valuable research problem with a substantial potential for commercial value. Recently, various research works that use deep neural networks to colorize single mode (near or thermal) infrared images have been reported. In this paper, we present a novel convolutional auto-encoder architecture that takes multiple images captured with different imaging modes (near IR, thermal IR and low-light) to perform colorization using the visual cues that exist in all imaging modes. We present visual results demonstrating that using multiple IR imaging modes improves the overall visual quality of the results.