Image Fusion Based on Feature Decoupling and Proportion Preserving

Image fusion is a widely used technique for generating a new image by combining information from multiple input images. However, existing image fusion algorithms are often domain-specific, which limits their generalization ability and processing capacity. In this paper, we propose a fast unified fusion network called FDF, based on feature decoupling and intensity and gradient feature proportion maintenance. FDF is an end-to-end network that can perform multiple image fusion tasks. We first decouple the features of the source images into intensity features and texture features and then fuse them using the intensity and gradient paths. To improve the generalization ability, we design a unified loss function that can adapt to different fusion tasks. We evaluate FDF on three image fusion tasks, namely visible and infrared image fusion, multi-exposure image fusion, and medical image fusion. Our experimental results show that FDF outperforms state-of-the-art methods in terms of visual effects and multiple quantitative metrics. The proposed method has the potential to be applied to other image fusion tasks and domains, making it a promising approach for future research. Overall, FDF provides a fast and unified solution for image fusion tasks, which can significantly improve the efficiency and effectiveness of image fusion applications.