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The fusion of images taken by heterogeneous sensors helps to enrich theinformation and improve the quality of imaging. In this article, we present ahybrid model consisting of a convolutional encoder and a Transformer-baseddecoder to fuse multimodal images. In the encoder, a non-local cross-modalattention block is proposed to capture both local and global dependencies ofmultiple source images. A branch fusion module is designed to adaptively fusethe features of the two branches. We embed a Transformer module with linearcomplexity in the decoder to enhance the reconstruction capability of theproposed network. Qualitative and quantitative experiments demonstrate theeffectiveness of the proposed method by comparing it with existingstate-of-the-art fusion models. The source code of our work is available athttps://github.com/pandayuanyu/HCFusion.

Multimodal Image Fusion based on Hybrid CNN-Transformer and Non-local Cross-modal Attention