MSDFusion:A Multi-scale Semantic-driven Fusion Network for Infrared and Visible Image Fusion

Fusing infrared and visible images is an essential aspect of image processing, designed to integrate both types of data into a single image, thereby improving visual clarity and detail.Most methods for image fusion using deep neural networks often focus on fusion at the pixel or feature level, overlooking the semantic information embedded in the images at a more abstract level. To meet the needs of complex vision tasks and enhance image fusion performance in these scenarios, this paper introduces a multi-scale semantic-focused fusion network for infrared and visible images, named MSDFusion. This network integrates a semantic segmentation module with an image fusion module, allowing it to preserve more semantic information. Experimental results demonstrate that MSDFusion achieves superior visual effects and richer semantic information relative to other leading-edge algorithms