  Medical images play an important role in clinical applications. Multimodal
medical images could provide rich information about patients for physicians to
diagnose. The image fusion technique is able to synthesize complementary
information from multimodal images into a single image. This technique will
prevent radiologists switch back and forth between different images and save
lots of time in the diagnostic process. In this paper, we introduce a novel
Dilated Residual Attention Network for the medical image fusion task. Our
network is capable to extract multi-scale deep semantic features. Furthermore,
we propose a novel fixed fusion strategy termed Softmax-based weighted strategy
based on the Softmax weights and matrix nuclear norm. Extensive experiments
show our proposed network and fusion strategy exceed the state-of-the-art
performance compared with reference image fusion methods on four commonly used
fusion metrics.
