FeMIP: detector-free feature matching for multimodal images with policy gradient

Feature matching for multimodal images is an important task in image processing. However, most methods perform image feature detection, description, and matching sequentially, resulting in a large loss, low matching accuracy, and slow performance. To tackle these challenges, we propose a detector-free method called FeMIP for feature matching of multimodal images. We design coarse matching and fine regression modules to implement accurate multimodal image feature matches in a coarse-to-fine manner. Furthermore, we add a novel data augmentation method enabling FeMIP to achieve feature matching faster and more accurately. The coarse-to-fine module automatically generates pixel-level labels on the original image, enabling FeMIP to perform pixel-level matching on data with only image-level labels. In addition, we use the principle of reinforcement learning to design a policy gradient method to improve the solution to the problem of discreteness in matching. Extensive experiments show that FeMIP has good generalization and achieves excellent matching performances. The code will be released at: https://github.com/LiaoYun0x0/FeMIP.