Benefitting from the strong feature extraction capability of deep learning, infrared and visible image fusion has made a great progress. Since infrared and visible images are obtained by different sensors with different imaging mechanisms, there exists domain discrepancy, which becomes stumbling block for effective fusion. In this paper, we propose a novel self-supervised feature adaption framework for infrared and visible image fusion. We implement a self-supervised strategy that facilitates the backbone network to extract features with adaption while retaining the vital information by reconstructing the source images. Specifically, we preliminary adopt an encoder network to extract features with adaption. Then, two decoders with attention mechanism blocks are utilized to reconstruct the source images in a self-supervised way, forcing the adapted features to contain vital information of the source images. Further, considering the case that source images contain low-quality information, we design a novel infrared and visible image fusion and enhancement model, improving the fusion method’s robustness. Experiments are constructed to evaluate the proposed method qualitatively and quantitatively, which show that the proposed method achieves the state-of-art performance comparing with existing infrared and visible image fusion methods. Results are available at https://github.com/zhoafan/SFA-Fuse.

得益于深度学习强大的特征提取能力，红外与可见光图像融合取得了长足的进步。由于红外和可见光图像是由不同成像机制的不同传感器获得的，存在域差异，成为有效融合的绊脚石。在本文中，我们提出了一种用于红外和可见光图像融合的新型自监督特征适应框架。我们实施了一种自我监督的策略，该策略有助于骨干网络在通过重建源图像保留重要信息的同时提取自适应特征。具体来说，我们初步采用编码器网络来自适应提取特征。然后，利用两个带有注意力机制块的解码器以自监督的方式重建源图像，强制适应的特征包含源图像的重要信息。此外，考虑到源图像包含低质量信息的情况，我们设计了一种新颖的红外和可见光图像融合和增强模型，提高了融合方法的鲁棒性。构建实验以定性和定量地评估所提出的方法，这表明与现有的红外和可见光图像融合方法相比，所提出的方法达到了最先进的性能。结果可在 https://github.com/zhoafan/SFA-Fuse 获得。构建实验以定性和定量地评估所提出的方法，这表明与现有的红外和可见光图像融合方法相比，所提出的方法达到了最先进的性能。结果可在 https://github.com/zhoafan/SFA-Fuse 获得。构建实验以定性和定量地评估所提出的方法，这表明与现有的红外和可见光图像融合方法相比，所提出的方法达到了最先进的性能。结果可在 https://github.com/zhoafan/SFA-Fuse 获得。