提出DAF-Net模型，提升红外与可见光图像融合质量。
DAF-Net: A Dual-Branch Feature Decomposition Fusion Network with Domain Adaptive for Infrared and Visible Image Fusion
- 双分支结构分别提取全局结构与细节纹理特征
- 引入多核最大均值差异对齐特征空间，提升融合效果
- 适合需要高质量多模态图像融合的应用场景
红外与可见光图像融合旨在结合两种模态的互补信息，实现更全面的场景理解。然而，由于两模态间存在显著差异，融合过程中保持关键特征仍具挑战。为此，本文提出一种具有领域自适应能力的双分支特征分解融合网络（DAF-Net），将多核最大均值差异（MK-MMD）引入基础编码器，并设计适用于红外与可见光图像融合的混合核函数。基础编码器基于Restormer网络捕捉全局结构信息，细节编码器基于可逆神经网络（INN）提取细节纹理信息。通过引入MK-MMD，DAF-Net有效对齐了可见光与红外图像的潜在特征空间，从而提升融合图像质量。实验结果表明，所提方法在多个数据集上优于现有技术，显著提升了视觉质量和融合性能。相关Python代码已公开于https://github.com/xujian000/DAF-Net。
原文摘要 · Abstract (English)
Infrared and visible image fusion aims to combine complementary information from both modalities to provide a more comprehensive scene understanding. However, due to the significant differences between the two modalities, preserving key features during the fusion process remains a challenge. To address this issue, we propose a dual-branch feature decomposition fusion network (DAF-Net) with domain adaptive, which introduces Multi-Kernel Maximum Mean Discrepancy (MK-MMD) into the base encoder and designs a hybrid kernel function suitable for infrared and visible image fusion. The base encoder built on the Restormer network captures global structural information while the detail encoder based on Invertible Neural Networks (INN) focuses on extracting detail texture information. By incorporating MK-MMD, the DAF-Net effectively aligns the latent feature spaces of visible and infrared images, thereby improving the quality of the fused images. Experimental results demonstrate that the proposed method outperforms existing techniques across multiple datasets, significantly enhancing both visual quality and fusion performance. The related Python code is available at https://github.com/xujian000/DAF-Net.
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