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 neural network 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. Two 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/exceptionLi/imagefusion densefuse

翻译：在本文中,我们展示了红外和可见图像聚合问题的新颖的深层学习结构。与传统的革命网络不同,我们的编码网络由进化神经网络层和密度块结合而成,每个层的输出与所有其他层相连。我们试图利用这一结构从编码过程中的源图像中获取更有用的特征。我们设计了两种融合策略来融合这些特征。最后,引信图像由解密器重建。与现有的融合方法相比,拟议的融合方法在客观和主观评估中达到了最先进的性能。Code和预先培训的模型可在https://github.com/ExcionLi/imageluculation bentfuse查阅。