  Existing image fusion methods pay few research attention to image fusion
efficiency and network architecture. However, the efficiency and accuracy of
image fusion has an important impact in practical applications. To solve this
problem, we propose an \textit{efficient autonomous evolution image fusion
method, dubed by AE-Netv2}. Different from other image fusion methods based on
deep learning, AE-Netv2 is inspired by human brain cognitive mechanism.
Firstly, we discuss the influence of different network architecture on image
fusion quality and fusion efficiency, which provides a reference for the design
of image fusion architecture. Secondly, we explore the influence of pooling
layer on image fusion task and propose an image fusion method with pooling
layer. Finally, we explore the commonness and characteristics of different
image fusion tasks, which provides a research basis for further research on the
continuous learning characteristics of human brain in the field of image
fusion. Comprehensive experiments demonstrate the superiority of AE-Netv2
compared with state-of-the-art methods in different fusion tasks at a real time
speed of 100+ FPS on GTX 2070. Among all tested methods based on deep learning,
AE-Netv2 has the faster speed, the smaller model size and the better
robustness.
