FreeFusion: Infrared and Visible Image Fusion via Cross Reconstruction Learning

Existing fusion methods empirically design elaborate fusion losses to retain the specific features from source images. Since image fusion has no ground truth, the hand-crafted losses may not make the fused images cover all the vital features, and then affect the performance of the high-level tasks. Here, there are two main challenges: domain discrepancy among source images and semantic mismatch at different-level tasks. This paper proposes an infrared and visible image fusion via cross reconstruction learning, which doesn't using any hand-crafted fusion losses, but prompts the network to adaptively fuse complementary information of source images. Firstly, we design a cross reconstruction learning model that decouples the fusion features to reconstruct another-modality source image. Thus, the fusion network is forced to learn the domain-adaptive representations of two modal features, which enables their domain alignment in a latent space. Secondly, we propose a dynamic interactive fusion strategy that builds a correlation matrix between fusion features and object semantic features to overcome the semantic mismatch. Further, we enhance the strong correlation features and suppress the weak correlation features to improve the interactive ability. Extensive experiments on three datasets demonstrate the superior fusion performance compared to the state-of-the-art methods, concurrently facilitating the segmentation accuracy.