  Infrared and visible image fusion (IVIF) aims to preserve thermal radiation
information from infrared images while integrating texture details from visible
images. The differences that infrared images primarily express thermal
radiation through image intensity while visible images mainly represent texture
details via image gradients, has long been considered a significant obstacle to
IVIF technology development. Existing dual-discriminator Generative Adversarial
Networks (GANs) use two identical discriminators to guide the model in learning
different types of information. However, given the intrinsic differences
between infrared and visible images, using two heterogeneous discriminators is
more effective. This paper proposes a novel attention-based generator and
heterogeneous dual-discriminator generative adversarial network (AHDGAN) for
infrared and visible image fusion. Specifically, the model employs two
structurally different discriminators to address the distinct learning needs of
infrared and visible image information. These include a global discriminator
for thermal radiation information and a Markovian discriminator for detailed
information. Additionally, different multi-scale attention modules are
introduced to help the discriminators focus better on their respective source
images. Based on this, to integrate the learned information from different
source images effectively, an attention mechanism is designed in the generator
to construct an information fusion layer. This approach guides the model to
learn thermal radiation information from infrared images while simultaneously
capturing texture details from visible images. Extensive experiments on various
public datasets demonstrate the superiority of our proposed AHDGAN over other
state-of-the-art (SOTA) algorithms, highlighting its enhanced potential for
practical applications.
