Visible-infrared image patch matching based on attention mechanism.

Image matching has a wide range of applications in computer vision. Existing image matching is mostly used for homologous images. In the matching of visible images and infrared images, the imaging principles for visible and infrared images differ significantly, resulting in substantial differences between the images. The matching network used for homologous images cannot achieve satisfactory results when applied to visible and infrared images. This inadequacy stems primarily from the absence of feature extraction networks. This dearth results in the inability to perform effective feature representation for visible and infrared images. In addition, deep learning is a data-driven method. The scarcity of visible-infrared image matching datasets hampers the learning process of network models, making it impossible for the network model to learn the best parameters and achieve the best performance. Regarding the above issues, we propose a visible-infrared image matching network based on the attention mechanism. The matching network adopts a Siamese structure, and the two branches use the same CNN. We add an attention module after the last layer of the CNN to improve the feature extraction ability of the network for visible and infrared images. We extend the dataset by reorganizing and re-labeling the existing sequence of visible-infrared datasets to obtain sufficiently rich training and testing data. To improve the quality of the dataset, we use the focal loss to solve the dataset's positive and negative sample imbalance problems during the training process. Compared with other methods, experimental results show that our method achieves better matching results. [ABSTRACT FROM AUTHOR]