Infrared and visual image registration based on mutual information with a combined particle swarm optimization – Powell search algorithm

Infrared and visual image registration has widespread applications in the remote sensing and military fields. The use of mutual information has proved effective and successful in the infrared and visual image registration process. Optimization algorithms, such as particle swarm optimization (PSO) or the Powell search method, are often used to find the most appropriate registration parameters. The PSO algorithm has a high global search capacity and the search speed is fast initially, but the main weakness is its poor search performance in the later search stage. The Powell search method has a powerful local search capacity, but the search performance and time requirements are highly sensitive to the initial values. Therefore, in this study, we propose a novel hybrid algorithm, which combines the PSO algorithm and Powell search method. First, the PSO algorithm is used to obtain a registration parameter that is close to the global minimum. Using this result, the Powell search method aims to find a more precision registration parameter. Our experimental results demonstrate that the algorithm can correct the scale, rotation, and translation in an effective manner without requiring an additional optimization algorithm. Our method may be a good solution for registering the infrared and visible images, and it obtains better performance in terms of time and precision compared with traditional method.