LCFusion: Infrared and visible image fusion network based on local contour enhancement

Abstract

Infrared and visible light image fusion aims to generate integrated representations that synergistically preserve salient thermal targets in the infrared modality and high-resolution textural details in the visible light modality. However, existing methods face two core challenges: First, high-frequency noise in visible images, such as sensor noise and nonuniform illumination artifacts, is often highly coupled with effective textures. Traditional fusion paradigms readily amplify noise interference while enhancing details, leading to structural distortion and visual graininess in fusion results. Second, mainstream approaches predominantly rely on simple aggregation operations like feature stitching or linear weighting, lacking deep modeling of cross-modal semantic correlations. This prevents adaptive interaction and collaborative enhancement of complementary information between modalities, creating a significant trade-off between target saliency and detail preservation. To address these challenges, we propose a dual-branch fusion network based on local contour enhancement. Specifically, it distinguishes and enhances meaningful contour details in a learnable manner while suppressing meaningless noise, thereby purifying the detail information used for fusion at its source. Cross-attention weights are computed based on feature representations extracted from different modal branches, enabling a feature selection mechanism that facilitates dynamic cross-modal interaction between infrared and visible light information. We evaluate our method against 11 state-of-the-art deep learning-based fusion approaches across four benchmark datasets using both subjective assessments and objective metrics. The experimental results demonstrate superior performance on public datasets. Furthermore, YOLOv12-based detection tests reveal that our method achieves higher confidence scores and better overall detection performance compared to other fusion techniques.