  In this paper, we present a novel deep learning architecture for infrared and
visible images fusion problem. In contrast to conventional convolutional
networks, our encoding network is combined by convolutional layers, fusion
layer and dense block in which the output of each layer is connected to every
other layer. We attempt to use this architecture to get more useful features
from source images in encoding process. And two fusion layers(fusion
strategies) are designed to fuse these features. Finally, the fused image is
reconstructed by decoder. Compared with existing fusion methods, the proposed
fusion method achieves state-of-the-art performance in objective and subjective
assessment. Code and pre-trained models are available at
https://github.com/hli1221/imagefusion_densefuse
