  The human visual perception system has strong robustness in image fusion.
This robustness is based on human visual perception system's characteristics of
feature selection and non-linear fusion of different features. In order to
simulate the human visual perception mechanism in image fusion tasks, we
propose a multi-source image fusion framework that combines illuminance factors
and attention mechanisms. The framework effectively combines traditional image
features and modern deep learning features. First, we perform multi-scale
decomposition of multi-source images. Then, the visual saliency map and the
deep feature map are combined with the illuminance fusion factor to perform
high-low frequency nonlinear fusion. Secondly, the characteristics of high and
low frequency fusion are selected through the channel attention network to
obtain the final fusion map. By simulating the nonlinear characteristics and
selection characteristics of the human visual perception system in image
fusion, the fused image is more in line with the human visual perception
mechanism. Finally, we validate our fusion framework on public datasets of
infrared and visible images, medical images and multi-focus images. The
experimental results demonstrate the superiority of our fusion framework over
state-of-arts in visual quality, objective fusion metrics and robustness.
