  Infrared and visible image fusion targets to provide an informative image by
combining complementary information from different sensors. Existing
learning-based fusion approaches attempt to construct various loss functions to
preserve complementary features, while neglecting to discover the
inter-relationship between the two modalities, leading to redundant or even
invalid information on the fusion results. Moreover, most methods focus on
strengthening the network with an increase in depth while neglecting the
importance of feature transmission, causing vital information degeneration. To
alleviate these issues, we propose a coupled contrastive learning network,
dubbed CoCoNet, to realize infrared and visible image fusion in an end-to-end
manner. Concretely, to simultaneously retain typical features from both
modalities and to avoid artifacts emerging on the fused result, we develop a
coupled contrastive constraint in our loss function. In a fused image, its
foreground target / background detail part is pulled close to the infrared /
visible source and pushed far away from the visible / infrared source in the
representation space. We further exploit image characteristics to provide
data-sensitive weights, allowing our loss function to build a more reliable
relationship with source images. A multi-level attention module is established
to learn rich hierarchical feature representation and to comprehensively
transfer features in the fusion process. We also apply the proposed CoCoNet on
medical image fusion of different types, e.g., magnetic resonance image,
positron emission tomography image, and single photon emission computed
tomography image. Extensive experiments demonstrate that our method achieves
state-of-the-art (SOTA) performance under both subjective and objective
evaluation, especially in preserving prominent targets and recovering vital
textural details.
