Infrared and Visible Image Fusion Method Based on Degradation Model

Infrared and visible image fusion algorithms based on deep learning rely on artificially designed similarity functions to measure the similarity between input and output. The unsupervised learning method can not effectively utilize the ability of neural networks to extract deep features, resulting in unsatisfactory fusion results. Considering this problem, a new fusion degradation model of infrared and visible image is proposed in this paper, which regards infrared and visible images as the degraded images produced by ideal fusion images through mixed degradation processes. Secondly, a data enhancement scheme for simulating image degradation is proposed, and a large number of simulated degradation images are generated by using high-definition datasets for training the network. Finally, a simple and efficient end- to-end network model and its network training framework are designed based on the proposed degradation model. The experimental results show that the method proposed in this paper not only has good visual effects and performance indicators, but also can effectively suppress interferences such as illumination, smoke and noise.