Thermal image colorization using Markov decision processes

Abstract

Displaying night-vision thermal images with day-time colors is paramount for scene interpretation and target tracking. In this paper, we employ object recognition methods for colorization, which amounts to segmenting thermal images into plants, buildings, sky, water, roads and others, then calculating colors to each class. The main thrust of our work is the introduction of Markov decision processes (MDP) to deal with the computational complexity of the colorization problem. MDP provides us with the approaches of neighborhood analysis and probabilistic classification which we exploit to efficiently solve chromatic estimation. We initially label the segments with a classifier, paving the way for the neighborhood analysis. We then update classification confidences of each class by MDP under the consideration of neighboring consistency and scenery layout. Finally we calculate the colors for every segment by blending the characteristic colors of each class it belongs to in a probabilistic way. Experimental results show that the colorized appearance of our algorithm is satisfactory and harmonious; the computational speed is quite fast as well.