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
this https URL

本文介绍了一种新的深度学习架构，用于红外和可见图像融合问题。相对于常规卷积网络，我们的编码网络由卷积层，融合层和稠密块组合而成，其中每个层的输出与每个其他层相连。我们尝试使用这种架构从源图像中获得更有用的特征。并且设计了两个融合层（融合策略）来融合这些特征。最后，使用解码器重建融合图像。与现有的融合方法相比，所提出的融合方法在客观和主观评估方面均取得了最先进的性能。代码和预训练模型可在此 https URL 上获得。