UFFusion: An Unified Feature Space for Infrared-Visible Image Fusion Network Based on Dynamic Domain Transformation

Infrared-visible images have a high information complementarity, making fusing them highly valuable for various applications. However, infrared-visible images also exhibit strong differences, which are crucial factors limiting fusion performance. To address this issue, we propose a unified feature space in which can transfer the infrared domain to the visible domain using the dynamic domain transformation method. This approach eliminates the modality differences and provides high-quality features for image reconstructor. Notably, we propose a dense attention module used to extract common and unique features. The method permits the model to learn the correlation of different layer features, thereby enhancing the model's performance. Moreover, we design a
$\mathbf{S}^{3}\mathbf{IM}$
loss function to enhance dynamic range of fused images. The qualitative and quantitative experiments on publicly available datasets demonstrate the superiority of our UFFusion over the state-of-the-art, in terms of both visual effect and quantitative metrics.