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Image fusion is an important task for computer vision as a diverse range of applications are benefiting from the fusion operation. The existing image fusion methods are largely implemented at the pixel level, which may introduce artifacts and/or inconsistencies, while the computational complexity is relatively high. In this article, we propose a symmetric encoder-decoder with residual block (SEDRFuse) network to fuse infrared and visible images for night vision applications. At the training stage, the SEDRFuse network is trained to create a fixed feature extractor. At the fusing stage, the trained extractor is utilized to extract the intermediate and compensation features, which are generated by the residual block and the first two convolutional layers from the input source images, respectively. Two attention maps, which are derived from the intermediate features, are then multiplied by the intermediate features for fusion …