CASPFuse: An Infrared and Visible Image Fusion Method Based on Dual-Cycle Crosswise Awareness and Global Structure-Tensor Preservation

The inconsistency in the feature spaces of cross-modality images makes it challenging to adaptively fuse images of different modalities. The fusion rules inevitably have global or local proneness toward one modality. To address the challenge, this article proposes a novel fusion method based on dual-cycle crosswise awareness and global structure-tensor preservation (CASPFuse), which is a modal transition network and contrives images taking part in the fusion operation to have similar feature spaces. First, a dual-cycle modality transition (DMT) network is proposed. Under the guidance of cross-aware-based adversarial learning and the constraints of edge prior, it allows the generated images better inherit the specialties of original images on the basis of pursuing the modal transition. Second, a set of global structure-tensor preserving (STP) models and STP-aware loss are designed to enhance the capabilities of the network in structural preservation and modal consistency perception. STP-aware loss, collaborate with cycle-consistency loss and cross-aware loss, enable our network to effectively supervise the generation of high-quality pseudo images and to eliminate the adverse artifacts and structural degradation. Third, we devise a progressively adaptive fusion (PAF) network, which sequentially generates pairwise images with unified modalities and fine-tunes their structures to overcome the challenge of effective aggregation of different modal attributes. Extensive comparative experiments demonstrate that our CASPFuse outperforms state-of-the-art fusion methods in adequately expressing the advantageous complementary information of different modalities. The source code is available at https://github.com/xbsj-cool/CASPFuse.