Infrared and visible images fusion method based on unsupervised learning

Abstract

Aiming at the problem that the current infrared and visible image fusion based on deep learning has no labels, this paper proposes an infrared and visible image fusion algorithm based on unsupervised learning. This method utilizes the characteristics of unsupervised learning, and introduces infrared image information with high gray value into the visible image to obtain the fusion image. The deep learning network proposed in this paper is composed of 6 layers of convolution blocks, and a dual attention module is also designed to make the fusion image pay more attention to the high gray value area in the infrared image. By introducing skip connections, the shallow features are fused with the deep features, so that the details of the entire fused image are richer and the appearance of halos is reduced. A large number of experimental results show that the fusion method proposed in this paper can accurately highlight the target object while maintaining the visible texture details, enhance the visual effect of the human eye, and improve the target recognition. At the same time, the quantitative experimental results show that the fusion algorithm proposed in this paper has obvious advantages in multiple indicators.