Image registration method based on Generative Adversarial Networks
Yujie Sun, QI He-ping, Chuanyou Wang, Lei Tao · 2020
Image registration technology has been gradually applied in military and civil fields, such as unmanned aerial vehicle (UAV) target recognition, remote sensing image registration, 3D object reconstruction and so on. The registration accuracy of traditional registration algorithms, such as sift and surf, are not high and even mismatch, when the image content changes or affine transformation occurs. In order to solve this problem, an image registration method (IR-GAN method) based on Generative adversarial networks (GAN) was proposed, in view of the content change and affine transformation of images for training. At the same time, the sample images are expanded by translation, rotation, reflection, scaling, shearing, brightness transformation to improve the precision and accuracy of the registration algorithm. Simulation results show that our method is correct in principle and can improve the registration accuracy of both images with content changing and affine transform images.