  In this paper we propose a novel method for infrared and visible image fusion
where we develop nest connection-based network and spatial/channel attention
models. The nest connection-based network can preserve significant amounts of
information from input data in a multi-scale perspective. The approach
comprises three key elements: encoder, fusion strategy and decoder
respectively. In our proposed fusion strategy, spatial attention models and
channel attention models are developed that describe the importance of each
spatial position and of each channel with deep features. Firstly, the source
images are fed into the encoder to extract multi-scale deep features. The novel
fusion strategy is then developed to fuse these features for each scale.
Finally, the fused image is reconstructed by the nest connection-based decoder.
Experiments are performed on publicly available datasets. These exhibit that
our proposed approach has better fusion performance than other state-of-the-art
methods. This claim is justified through both subjective and objective
evaluation. The code of our fusion method is available at
https://github.com/hli1221/imagefusion-nestfuse
