  Infrared and visible image fusion, a hot topic in the field of image
processing, aims at obtaining fused images keeping the advantages of source
images. This paper proposes a novel auto-encoder (AE) based fusion network. The
core idea is that the encoder decomposes an image into background and detail
feature maps with low- and high-frequency information, respectively, and that
the decoder recovers the original image. To this end, the loss function makes
the background/detail feature maps of source images similar/dissimilar. In the
test phase, background and detail feature maps are respectively merged via a
fusion module, and the fused image is recovered by the decoder. Qualitative and
quantitative results illustrate that our method can generate fusion images
containing highlighted targets and abundant detail texture information with
strong robustness and meanwhile surpass state-of-the-art (SOTA) approaches.
