LE2Fusion: A novel local edge enhancement module for infrared and
  visible image fusion
Yongbiao Xiao Hui Li Chunyang Cheng Xiaoning Song
Infrared and visible image fusion task aims to generate a fused image which
contains salient features and rich texture details from multi-source images.
However, under complex illumination conditions, few algorithms pay attention to
the edge information of local regions which is crucial for downstream tasks. To
this end, we propose a fusion network based on the local edge enhancement,
named LE2Fusion. Specifically, a local edge enhancement (LE2) module is
proposed to improve the edge information under complex illumination conditions
and preserve the essential features of image. For feature extraction, a
multi-scale residual attention (MRA) module is applied to extract rich
features. Then, with LE2, a set of enhancement weights are generated which are
utilized in feature fusion strategy and used to guide the image reconstruction.
To better preserve the local detail information and structure information, the
pixel intensity loss function based on the local region is also presented. The
experiments demonstrate that the proposed method exhibits better fusion
performance than the state-of-the-art fusion methods on public datasets.