Abstract:

The traditional normalized mutual information neglects the spatial information, so the registration result will be incorrect when the image mixes with noises. Edge is the basic character of image, in order to solve the drawback of the normalized mutual information method, improve precision and speed up the convergence, we combines the image edge information with the gray information adaptively to form the normalized edge mutual information measure (NCMI) and propose an adaptive accelerate particle swarm optimization algorithm (AAPSO) based on the accelerated factor. The AAPSO is used for image registration based on the NCMI. By sorting the solutions, a specified number of worst solutions will be forced to accelerate in order to determine the direction of global solution, and we also improve the adaptive inertia weight formula, thereby it improves convergence, prevents premature convergence and increases the diversity of the optimal solution. Meanwhile, the AAPSO algorithm adds the accelerated factor to improve convergence speed. Result shows that the method has a high registration precision, fast registration speed, and it has strong applicability.

Key words: image registration;normalized mutual information;normalized edge mutual information;particle swarm algorithm;AAPSO algorithm