CDTFusion: Crossing Domain and Task for Infrared and Visible Image Fusion

Infrared and visible images present different domains that hinder the fusion process, thereby losing texture details. Besides, the low-level fusion and subsequent high-level segmentation appear cross-task feature gap that impedes their mutual promotion, causing blurred object edges. Addressing the above issues, this paper proposes a novel infrared and visible image fusion method that simultaneously crosses domain and task. Firstly, a swap image translation strategy is built to transfer the features of visible and infrared images into an adaptive domain. Meanwhile, a global-local constraint is introduced to achieve overall domain space transfer, and shorten their feature distance. Secondly, a task interaction & query module is designed to explore the cross-task feature interactive relationship, which is then used as a bridge to realize the gradient backpropagation. Thus, a fine-grained mapping from the segmentation feature to fusion feature is obtained. Extensive experiments demonstrate that the proposed method exhibits superior fusion and segmentation performance than the state-of-the-art methods. Model and code are available at https://github.com/wangwenbo26/CDTFusion.