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

• URL: http://arxiv.org/abs/2210.09847v1
• Date: Tue, 18 Oct 2022 13:30:52 GMT
• Title: Multimodal Image Fusion based on Hybrid CNN-Transformer and Non-local Cross-modal Attention
• Authors: Yu Yuan and Jiaqi Wu and Zhongliang Jing and Henry Leung and Han Pan
• Abstract summary: We present a hybrid model consisting of a convolutional encoder and a Transformer-based decoder to fuse multimodal images. A branch fusion module is designed to adaptively fuse the features of the two branches.
• Score: 12.167049432063132
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The fusion of images taken by heterogeneous sensors helps to enrich the information and improve the quality of imaging. In this article, we present a hybrid model consisting of a convolutional encoder and a Transformer-based decoder to fuse multimodal images. In the encoder, a non-local cross-modal attention block is proposed to capture both local and global dependencies of multiple source images. A branch fusion module is designed to adaptively fuse the features of the two branches. We embed a Transformer module with linear complexity in the decoder to enhance the reconstruction capability of the proposed network. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed method by comparing it with existing state-of-the-art fusion models. The source code of our work is available at https://github.com/pandayuanyu/HCFusion.

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