Work

Graph Representation Learning for Infrared and Visible Image Fusion

Year: 2025

Type: article

Abstract: Infrared and visible image fusion aims to extract complementary features to synthesize a single fused image. In our method, we covert the regular image format into the graph space and conduct graph convolutional networks (GCNs) to extract NLss for the reliable infrared and visible image fusion. More specifically, GCNs are first performed on each intra-modal set to aggregate the features and propagate the inherent information, thereby extracting independent intra-modal NLss. Then, such intra-modal non-local self-similarity (NLss) features of infrared and visible images are concatenated to explore cross-domain NLss inter-modally and reconstruct the fused images. Extensive experiments show the superior performance of our method with the qualitative and quantitative analysis on the TNO, RoadScene and M3FD datasets, respectively, outperforming many state-of-the-art (SOTA) methods for the robust and effective infrared and visible image fusion.

Source: IEEE Transactions on Automation Science and Engineering

DOI: 10.1109/tase.2025.3557234

Authors: Jing Li, Lu Bai, Bin Yang, Chang Li, Lingfei Ma

Institutions: East China Normal University, Beijing Normal University, Hunan University, Hefei University of Technology

Language: English

Topic: Advanced Image Fusion Techniques

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G1614057970, G4063472708, G4777608190, G8587577622