This study proposes a novel unsupervised network for IR/VIS fusion task, termed as RXDNFuse, which is based on the aggregated residual dense network. In contrast to conventional fusion networks, RXDNFuse is designed as an end-to-end model that combines the structural advantages of ResNeXt and DenseNet. Hence, it overcomes the limitations of the manual and complicated design of activity-level measurement and fusion rules. Our method establishes the image fusion problem into the structure and intensity proportional maintenance problem of the IR/VIS images. Using comprehensive feature extraction and combination, RXDNFuse automatically estimates the information preservation degrees of corresponding source images, and extracts hierarchical features to achieve effective fusion. Moreover, we design two loss function strategies to optimize the similarity constraint and the network parameter training, thus further improving the quality of detailed information. We also generalize RXDNFuse to fuse images with different resolutions and RGB scale images. Extensive qualitative and quantitative evaluations reveal that our results can effectively preserve the abundant textural details and the highlighted thermal radiation information. In particular, our results form a comprehensive representation of scene information, which is more in line with the human visual perception system.

这项研究提出了一种新的用于IR / VIS融合任务的无监督网络，称为RXDNFuse，它基于聚集的剩余密集网络。与常规融合网络相比，RXDNFuse被设计为端到端模型，结合了ResNeXt和DenseNet的结构优势。因此，它克服了手动和复杂的活动级别测量和融合规则设计的局限性。我们的方法将图像融合问题确立为IR / VIS图像的结构和强度比例维护问题。通过全面的特征提取和组合，RXDNFuse可以自动估计相应源图像的信息保存程度，并提取分层特征以实现有效融合。此外，我们设计了两种损失函数策略来优化相似性约束和网络参数训练，从而进一步提高详细信息的质量。我们还通用化RXDNFuse来融合具有不同分辨率的图像和RGB比例图像。广泛的定性和定量评估表明，我们的结果可以有效地保留大量的纹理细节和突出显示的热辐射信息。特别地，我们的结果形成了场景信息的全面表示，这与人类的视觉感知系统更加吻合。广泛的定性和定量评估表明，我们的结果可以有效地保留大量的纹理细节和突出显示的热辐射信息。特别地，我们的结果形成了场景信息的全面表示，这与人类的视觉感知系统更加吻合。广泛的定性和定量评估表明，我们的结果可以有效地保留大量的纹理细节和突出显示的热辐射信息。特别地，我们的结果形成了场景信息的全面表示，这与人类的视觉感知系统更加吻合。