A Multi-scale Information Integration Framework for Infrared and Visible
  Image Fusion
Guang Yang Jie Li Hanxiao Lei Xinbo Gao
Infrared and visible image fusion aims at generating a fused image containing
the intensity and detail information of source images, and the key issue is
effectively measuring and integrating the complementary information of
multi-modality images from the same scene. Existing methods mostly adopt a
simple weight in the loss function to decide the information retention of each
modality rather than adaptively measuring complementary information for
different image pairs. In this study, we propose a multi-scale dual attention
(MDA) framework for infrared and visible image fusion, which is designed to
measure and integrate complementary information in both structure and loss
function at the image and patch level. In our method, the residual downsample
block decomposes source images into three scales first. Then, dual attention
fusion block integrates complementary information and generates a spatial and
channel attention map at each scale for feature fusion. Finally, the output
image is reconstructed by the residual reconstruction block. Loss function
consists of image-level, feature-level and patch-level three parts, of which
the calculation of the image-level and patch-level two parts are based on the
weights generated by the complementary information measurement. Indeed, to
constrain the pixel intensity distribution between the output and infrared
image, a style loss is added. Our fusion results perform robust and informative
across different scenarios. Qualitative and quantitative results on two
datasets illustrate that our method is able to preserve both thermal radiation
and detailed information from two modalities and achieve comparable results
compared with the other state-of-the-art methods. Ablation experiments show the
effectiveness of our information integration architecture and adaptively
measure complementary information retention in the loss function.