论文标题
摆脱融合规则：完全语义驱动的红外和可见图像融合
Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion
论文作者
论文摘要
红外且可见的图像融合在计算机视野领域起着至关重要的作用。以前的方法努力在损失功能中设计各种融合规则。但是，这些实验设计的融合规则使方法越来越复杂。此外，其中大多数仅着眼于增强视觉效果，因此在后续高级视力任务中表现出不满意的性能。为了应对这些挑战，在这封信中，我们开发了一个语义级融合网络，以充分利用语义指导，从而解散实验设计的融合规则。此外，为了获得对特征融合过程的更好的语义理解，以多尺度方式介绍了基于变压器的融合块。此外，我们设计了一个正规化损失功能以及培训策略，以完全利用高级视觉任务的语义指导。与最先进的方法相比，我们的方法不取决于手工制作的融合损失函数。尽管如此，它仍然在视觉质量上取得了卓越的性能以及后续的高级视觉任务。
Infrared and visible image fusion plays a vital role in the field of computer vision. Previous approaches make efforts to design various fusion rules in the loss functions. However, these experimental designed fusion rules make the methods more and more complex. Besides, most of them only focus on boosting the visual effects, thus showing unsatisfactory performance for the follow-up high-level vision tasks. To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules. In addition, to achieve a better semantic understanding of the feature fusion process, a fusion block based on the transformer is presented in a multi-scale manner. Moreover, we devise a regularization loss function, together with a training strategy, to fully use semantic guidance from the high-level vision tasks. Compared with state-of-the-art methods, our method does not depend on the hand-crafted fusion loss function. Still, it achieves superior performance on visual quality along with the follow-up high-level vision tasks.
