A fast and efficient image fusion method is presented to generate near-natural colors from panchromatic visual and thermal imaging sensors. Firstly, a set of daytime color reference images are analyzed and the false color mapping principle is proposed according to human's visual and emotional habits. That is, object colors should remain invariant after color mapping operations, differences between infrared and visual images should be enhanced and the background color should be consistent with the main scene content. Then a novel nonlinear color mapping model is given by introducing the geometric average value of the input visual and infrared image gray and the weighted average algorithm. To determine the control parameters in the mapping model, the boundary conditions are listed according to the mapping principle above. Fusion experiments show that the new fusion method can achieve the near-natural appearance of the fused image, and has the features of enhancing color contrasts and highlighting the infrared brilliant objects when comparing with the traditional TNO algorithm. Moreover, it owns the low complexity and is easy to realize real-time processing. So it is quite suitable for the nighttime imaging apparatus.
Aiming at the infrared object detection applications, a novel generalized cumulative sum processing is presented. Since in a typical IRST application system, object appearing and vanishing can be regarded as the change-point detection problem in Statistics. One of the effective solutions is the generalized cumulative sum processing (GCUSUM). Analyses are focused on the detection threshold value selection of GCUSUM algorithm and relations among the threshold value and false alarm rate, detection probability and signal-noise rate. The further researches extend a uniform band IRST system into the multiple band IRST system and improve the realization of GCUSUM algorithm. Results of theoretical analysis and simulation show that our modified algorithm has excellent object detection performance in an infrared image sequences from a real IRST system.