Most existing learning-based infrared and visible image fusion (IVIF) methods exhibit massive redundant information in the fusion images, i.e., yielding edge-blurring effect or unrecognizable for object detectors. To alleviate these issues, we propose a semantic structure-preserving approach for IVIF, namely SSPFusion. At first, we design a Structural Feature Extractor (SFE) to extract the structural features of infrared and visible images. Then, we introduce a multi-scale Structure-Preserving Fusion (SPF) module to fuse the structural features of infrared and visible images, while maintaining the consistency of semantic structures between the fusion and source images. Owing to these two effective modules, our method is able to generate high-quality fusion images from pairs of infrared and visible images, which can boost the performance of downstream computer-vision tasks. Experimental results on three benchmarks demonstrate that our method outperforms eight state-of-the-art image fusion methods in terms of both qualitative and quantitative evaluations. The code for our method, along with additional comparison results, will be made available at: https://github.com/QiaoYang-CV/SSPFUSION.

翻译：现有基于学习的红外与可见光图像融合方法大多在融合图像中存在大量冗余信息，即产生边缘模糊效应或导致目标检测器无法识别。为解决这些问题，我们提出了一种语义结构保持的红外与可见光图像融合方法，即SSPFusion。首先，我们设计了一个结构特征提取器（SFE）用于提取红外与可见光图像的结构特征；其次，引入多尺度结构保持融合（SPF）模块对红外与可见光图像的结构特征进行融合，同时保持融合图像与源图像之间语义结构的一致性。得益于这两个有效模块，我们的方法能够从红外与可见光图像对中生成高质量融合图像，从而提升下游计算机视觉任务的性能。在三个基准数据集上的实验结果表明，我们的方法在定性和定量评估中均优于八种最先进的图像融合方法。本方法的代码及更多对比结果将在https://github.com/QiaoYang-CV/SSPFUSION 上公开。