Registration algorithm of infrared and visible images based on improved gradient normalized mutual information and particle swarm optimization

In order to solve the problem that using classical mutual information measure in infrared and visible images registration may suffer from local extremum, and large amount of calculation by using classical gradient normalized mutual information measure, an improved gradient normalized mutual information measure based on classical gradient and mutual information was proposed, which counted mutual information of gradient image directly to combine image gradient information with gray information effectively. Compared with classical gradient normalized mutual information measure, the new measure could improve registration precision and reduce computation cost. During the optimization of parameters, for the defect of sinking into local extremum for classic particle swarm optimization algorithm, the improved particle swarm optimization algorithm was proposed, which included chaos optimization idea and hybridization idea in genetic algorithm. The improved particle swarm optimization algorithm can restrain local extremum and accelerate convergence. Experimental results demonstrate that this new algorithm can achieve high registration efficiency.