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: this https URL

提出一种语义结构保持的红外与可见光图像融合方法 (SSPFusion)，通过结构特征提取器和多尺度结构保持融合模块，在保持语义结构一致性的基础上生成高质量的融合图像，提升下游计算机视觉任务的性能。实验结果表明该方法在定性和定量评估上优于八个先进的图像融合方法。