Infrared and visual image registration has a wide application in the fields of remote sensing and military. Mutual
information (MI) has proved effective and successful in infrared and visual image registration process. To find the most
appropriate registration parameters, optimal algorithms, such as Particle Swarm Optimization (PSO) algorithm or Powell
search method, are often used. The PSO algorithm has strong global search ability and search speed is fast at the beginning,
while the weakness is low search performance in late search stage. In image registration process, it often takes a lot of time to do useless search and solution’s precision is low. Powell search method has strong local search ability. However, the search performance and time is more sensitive to initial values. In image registration, it is often obstructed by local
maximum and gets wrong results. In this paper, a novel hybrid algorithm, which combined PSO algorithm and Powell search method, is proposed. It combines both advantages that avoiding obstruction caused by local maximum and having higher precision. Firstly, using PSO algorithm gets a registration parameter which is close to global minimum. Based on the result in last stage, the Powell search method is used to find more precision registration parameter. The experimental result shows that the algorithm can effectively correct the scale, rotation and translation additional optimal algorithm. It can be a
good solution to register infrared difference of two images and has a greater performance on time and precision than
traditional and visible images.