  In computer vision and image processing tasks, image fusion has evolved into
an attractive research field. However, recent existing image fusion methods are
mostly built on pixel-level operations, which may produce unacceptable
artifacts and are time-consuming. In this paper, a symmetric encoder-decoder
with a residual block (SEDR) for infrared and visible image fusion is proposed.
For the training stage, the SEDR network is trained with a new dataset to
obtain a fixed feature extractor. For the fusion stage, first, the trained
model is utilized to extract the intermediate features and compensation
features of two source images. Then, extracted intermediate features are used
to generate two attention maps, which are multiplied to the input features for
refinement. In addition, the compensation features generated by the first two
convolutional layers are merged and passed to the corresponding deconvolutional
layers. At last, the refined features are fused for decoding to reconstruct the
final fused image. Experimental results demonstrate that the proposed fusion
method (named as SEDRFuse) outperforms the state-of-the-art fusion methods in
terms of both subjective and objective evaluations.
