Enhancement of thermal imagery using a low-cost high-resolution visual spectrum camera for scene understanding

Thermal-infrared cameras are used for signal/image processing and computer vision in numerous military and civilian applications. However, the cost of high quality (e.g., low noise, accurate temperature measurement, etc.) and high resolution thermal sensors is often a limiting factor. On the other hand, high resolution visual spectrum cameras are readily available and typically inexpensive. Herein, we outline a way to upsample thermal imagery with respect to a high resolution visual spectrum camera using Markov random field theory. This paper also explores the tradeoffs and impact of upsampling, both qualitatively and quantitatively. Our preliminary results demonstrate the successful use of this approach for human detection and accurate propagation of thermal measurements in an image for more general tasks like scene understanding. A tradeoff analysis of the cost-to-performance as the resolution of the thermal camera decreases is provided.