Differential evolution algorithm with population knowledge fusion strategy for image registration

Abstract

Image registration is a challenging NP-hard problem within the computer vision field. The differential evolutionary algorithm is a simple and efficient method to find the best among all the possible common parts of images. To improve the efficiency and accuracy of the registration, a knowledge-fusion-based differential evolution algorithm is proposed, which combines segmentation, gradient descent method, and hybrid selection strategy to enhance the exploration ability in the early stage and the exploitation ability in the later stage. The proposed algorithms have been implemented and tested with CEC2013 benchmark and real image data. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of solution quality, convergence speed, and solution success rate.