Effective image registration model using optimized KAZE algorithm

The incompatible problem with velocity and accuracy has been restricting the application of the KAZE algorithm. In order to resolve this shortage, we propose the effective image registration model using the optimized KAZE algorithm. This effective image registration model consist of four stages. First of all, to reduce the input data of image registration, the original registration images are preprocessed by the fusion preprocessing method based on the average and the perceptual hashing algorithms. Second, to extract image features quickly, we utilize the FAST algorithm to extract image features instead of the local extremum based on the Hessian matrix and the Taylor principle. Third, in order to accelerate the velocity of image features matching, the compressed sensing principle is used to reduce the dimension of the image feature descriptors. Finally, the two-step strategy is adopted to ensure the accuracy of image registration, the step one is that the hybrid matching method based on the FLANN and the KNN algorithms is used to rough matching, and the step two is that adopt the RANSAC algorithm to further accurate matching. This paper utilizes two groups of the experiments to verify the effective model, the experiment results show that the effective model has velocity advantage compared with other current image registration methods, and also achieves the compatible with velocity and accuracy in the case of the highest matching score. This model provides an effective solution for the application of image registration, and also has great significance for the development of image registration.