  The GAN-based infrared and visible image fusion methods have gained
ever-increasing attention due to its effectiveness and superiority. However,
the existing methods adopt the global pixel distribution of source images as
the basis for discrimination, which fails to focus on the key modality
information. Moreover, the dual-discriminator based methods suffer from the
confrontation between the discriminators. To this end, we propose an
attention-guided and wavelet-constrained GAN for infrared and visible image
fusion (AWFGAN). In this method, two unique discrimination strategies are
designed to improve the fusion performance. Specifically, we introduce the
spatial attention modules (SAM) into the generator to obtain the spatial
attention maps, and then the attention maps are utilized to force the
discrimination of infrared images to focus on the target regions. In addition,
we extend the discrimination range of visible information to the wavelet
subspace, which can force the generator to restore the high-frequency details
of visible images. Ablation experiments demonstrate the effectiveness of our
method in eliminating the confrontation between discriminators. And the
comparison experiments on public datasets demonstrate the effectiveness and
superiority of the proposed method.
