  In order to solve the robustness and generality problems of the image fusion
task,inspired by the human brain cognitive mechanism, we propose a robust and
general image fusion method with autonomous evolution ability, and is therefore
denoted with AE-Net. Through the collaborative optimization of multiple image
fusion methods to simulate the cognitive process of human brain, unsupervised
learning image fusion task can be transformed into semi-supervised image fusion
task or supervised image fusion task, thus promoting the evolutionary ability
of network model weight. Firstly, the relationship between human brain
cognitive mechanism and image fusion task is analyzed and a physical model is
established to simulate human brain cognitive mechanism. Secondly, we analyze
existing image fusion methods and image fusion loss functions, select the image
fusion method with complementary features to construct the algorithm module,
establish the multi-loss joint evaluation function to obtain the optimal
solution of algorithm module. The optimal solution of each image is used to
guide the weight training of network model. Our image fusion method can
effectively unify the cross-modal image fusion task and the same modal image
fusion task, and effectively overcome the difference of data distribution
between different datasets. Finally, extensive numerical results verify the
effectiveness and superiority of our method on a variety of image fusion
datasets, including multi-focus dataset, infrared and visi-ble dataset, medical
image dataset and multi-exposure dataset. Comprehensive experiments demonstrate
the superiority of our image fusion method in robustness and generality. In
addition, experimental results also demonstate the effectiveness of human brain
cognitive mechanism to improve the robustness and generality of image fusion.
