The goal of a deep learning-based general image fusion method is to solve multiple image fusion tasks with a single model, thereby facilitating the deployment of models in practical applications. However, existing methods fail to provide an efficient and comprehensive solution from both model training and network design perspectives. Regarding model training, current approaches cannot effectively leverage complementary information across different tasks. In terms of network design, they rely on experience-based network designs. To address these issues, we propose a comprehensive framework for general image fusion using the newly proposed gradient transfer learning and fusion rule unfolding. To leverage complementary information across different tasks during training, we propose a sequential gradient-transfer framework based on the idea that different image fusion tasks often exhibit complementary structural details and that image gradients effectively capture these details. To move beyond heuristic-based network design, we evolved a fundamental image fusion rule and integrated it into a deep equilibrium model, resulting in a more efficient and versatile image fusion network capable of uniformly handling various fusion tasks. Considering three different image fusion tasks, i.e., multi-focus image fusion, multi-exposure image fusion, and infrared and visible image fusion, our method not only produces images with richer structural information but also achieves highly competitive objective metrics. Furthermore, the results of generalization experiments on previously unseen image fusion tasks, i.e., medical image fusion, demonstrate that our method significantly outperforms competing approaches. The code will be available upon possible acceptance.
Wang et al. (Thu,) studied this question.