STFNet: Self-Supervised Transformer for Infrared and Visible Image Fusion

Most of the existing infrared and visible image fusion algorithms rely on hand-designed or simple convolution-based fusion strategies. However, these methods cannot explicitly model the contextual relationships between infrared and visible images, thereby limiting their robustness. To this end, we propose a novel Transformer-based feature fusion network for robust image fusion that can explicitly model the contextual relationship between the two modalities. Specifically, our fusion network consists of a detail self-attention module to capture the detail information of each modality and a saliency cross attention module to model contextual relationships between the two modalities. Since these two attention modules can obtain the pixel-level global dependencies, the fusion network has a powerful detail representation ability which is critical to the pixel-level image generation task. Moreover, we propose a deformable convolution-based feature align network to address the slight misaligned problem of the source image pairs, which is beneficial for reducing artifacts. Since there is no ground-truth for the infrared and visible image fusion task, it is essential to train the proposed method in a self-supervised manner. Therefore, we design a self-supervised multi-task loss which contains a structure similarity loss, a frequency consistency loss, and a Fourier spectral consistency loss to train the proposed algorithm. Extensive experimental results on four image fusion benchmarks show that our algorithm obtains competitive performance compared to state-of-the-art algorithms.