Decomposition-based and Interference Perception for Infrared and Visible
  Image Fusion in Complex Scenes
Xilai Li Xiaosong Li Haishu Tan
Infrared and visible image fusion has emerged as a prominent research in
computer vision. However, little attention has been paid on complex scenes
fusion, causing existing techniques to produce sub-optimal results when suffers
from real interferences. To fill this gap, we propose a decomposition-based and
interference perception image fusion method. Specifically, we classify the
pixels of visible image from the degree of scattering of light transmission,
based on which we then separate the detail and energy information of the image.
This refined decomposition facilitates the proposed model in identifying more
interfering pixels that are in complex scenes. To strike a balance between
denoising and detail preservation, we propose an adaptive denoising scheme for
fusing detail components. Meanwhile, we propose a new weighted fusion rule by
considering the distribution of image energy information from the perspective
of multiple directions. Extensive experiments in complex scenes fusions cover
adverse weathers, noise, blur, overexposure, fire, as well as downstream tasks
including semantic segmentation, object detection, salient object detection and
depth estimation, consistently indicate the effectiveness and superiority of
the proposed method compared with the recent representative methods.