Abstract ▼ AI-Helper

Infrared (IR) imaging has a wide variety of applications such as night vision detection and tracking, meteorology radiometers and spectroscopy techniques. An infrared image is a monochrome image, usually presented in grayscale. It has been proved that colorization of IR images can reduce human error...

Infrared (IR) imaging has a wide variety of applications such as night vision detection and tracking, meteorology radiometers and spectroscopy techniques. An infrared image is a monochrome image, usually presented in grayscale. It has been proved that colorization of IR images can reduce human error and speed up reaction time. Most previously suggested coloring methods use a reference color image, whose characteristic features differ drastically from IR ones. These differences make those features less pertinent to the coloring process. In this paper, we present a novel texture-based method for automatically coloring IR images. The method uses a reference (source) color image, which is selected from a database, built in advance containing various natural scenes. The source image is selected using a texture-matching algorithm that searches for a resemblance to the IR (target) image. The source and target images are divided into texture-based segments and a color segment best match is found for every IR segment. The coloring process is performed for each pair of IR-color segments, and exploits global as well as local features. Results show that our method produces more natural-looking images than achieved heretofore.