A Foreground-Guided Fusion Network for Infrared and Visible Images

Abstract

Infrared and visible image fusion aims to combine the complementary information from both modalities into a single image that simultaneously retains salient thermal targets and rich texture details. However, current fusion approaches mainly emphasize visual quality of the fused images, overlooking the compatibility with the downstream tasks. To address this issue, this paper proposes a foreground-guided fusion framework that adaptively enhances target regions while preserving global contextual information. Specifically, we design a two-branch network where the fusion branch aims to reconstructs high quality fused images while the foreground extraction branch captures semantic representations of salient objects to guide the fusion process toward target-related regions. To validate the effectiveness of the proposed framework, we build an aircraft key-point dataset named VIRcraft to assess the performance. The fused images are also applied to semantic segmentation and object detection to verify the generalization of the proposed framework. The experimental results on different tasks demonstrate the superiority and generalization of the proposed fusion framework.

Keywords

infrared and visible image fusion / foreground guidance / generalization / aircraft keypoint detection