Recently, deep learning has become a rapidly developing tool in the field of image fusion. An innovative image fusion method for fusing infrared images and visible-light images is proposed. The backbone network is an autoencoder. Different from previous autoencoders, the information extraction capability of the encoder is enhanced, and the ability to select the most effective channels in the decoder is optimized. First, the features of the source image are extracted during the encoding process. Then, a new effective fusion strategy is designed to fuse these features. Finally, the fused image is reconstructed by the decoder. Compared with the existing fusion methods, the proposed algorithm achieves state-of-the-art performance in both objective evaluation and visual quality.