Registration of coin images under different illuminant is the predecessor of coin surface defect detection. However, the traditional multimodal registration method based on mutual information is slow and low accuracy, and the existing image registration methods realized by homography estimation based on deep learning only work in single-mode tasks. A homography estimation method based on deep learning for multimodal coin images is proposed in this paper, and image registration can be realized with the estimated homography. First, the homography estimation layer is used to estimate the homography between the pair of input images, and the homography is used for perspective transformation of the image to be registered; Then, the image translation layer is used to translate the pair of images to the same domain, and this layer can be removed in inference so as to reduce the inference time; Finally, train the network with the loss calculated using the pair of images in the same domain. Experiments show that the average distance error of the proposed method on the test set is 3.417 pixels, which is 38.71% lower than the traditional multimodal registration method based on mutual information. The inference time of the proposed method is 17.74 ms, which is much less than 6368.49 ms of the traditional multimodal registration method based on mutual information.