  Infrared and visible image fusion, as a hot topic in image processing and
image enhancement, aims to produce fused images retaining the detail texture
information in visible images and the thermal radiation information in infrared
images. A critical step for this issue is to decompose features in different
scales and to merge them separately. In this paper, we propose a novel
dual-stream auto-encoder (AE) based fusion network. The core idea is that the
encoder decomposes an image into base and detail feature maps with low- and
high-frequency information, respectively, and that the decoder is responsible
for the original image reconstruction. To this end, a well-designed loss
function is established to make the base/detail feature maps
similar/dissimilar. In the test phase, base and detail feature maps are
respectively merged via an additional fusion layer, which contains a saliency
weighted-based spatial attention module and a channel attention module to
adaptively preserve more information from source images and to highlight the
objects. Then the fused image is recovered by the decoder. Qualitative and
quantitative results demonstrate that our method can generate fusion images
containing highlighted targets and abundant detail texture information with
strong reproducibility and meanwhile is superior to the state-of-the-art (SOTA)
approaches.
