ACFNet: An adaptive cross-fusion network for infrared and visible image fusion

Abstract

Considering the prospects for image fusion, it is necessary to guide the fusion to adapt to downstream vision tasks. In this paper, we propose an Adaptive Cross-Fusion Network (ACFNet) that utilizes an adaptive approach to fuse infrared and visible images, addressing cross-modal differences to enhance object detection performance. In ACFNet, a hierarchical cross-fusion module is designed to enrich the features at each level of the reconstructed images. In addition, a special adaptive gating selection module is proposed to realize feature fusion in an adaptive manner so as to obtain fused images without the interference of manual design. Extensive qualitative and quantitative experiments have demonstrated that ACFNet is superior to current state-of-the-art fusion methods and achieves excellent results in preserving target information and texture details. The fusion framework, when combined with the object detection framework, has the potential to significantly improve the precision of object detection in low-light conditions.