MGT: Modality-Guided Transformer for Infrared and Visible Image Fusion

Infrared and visible image fusion aims to generate high-quality fused images containing thermal radiation information from infrared images and texture information from visible images. Most deep learning-based methods are simple stacks of Transformer or convolution blocks and fail to further integrate the feature information of source images that may be missed in the fusion stage after generating the fused features. In this work, we develop a cross-attention-based macro framework, named Modality-Guided Transformer (MGT), that reintroduces detailed information from the two input images across multiple feature extraction layers into the initially obtained fused image. For efficiency, our MGT also introduces shared attention and multi-scale windows to reduce the computational costs of attention. Experimental results show that the proposed MGT outperforms state-of-the-art methods, especially in preserving salient targets and infrared texture details. Our code is publicly available at https://github.com/TaoYing-Zhang/MGT.