Advancing RGB-IR Image Fusion: Exploiting Hyperbolic Geometry

Infrared and visible image fusion is essential for remote sensing applications, especially for obtaining high-quality imagery of terrestrial environments. Hierarchical feature information is crucial for image fusion as it captures the intricate relationships between different modalities, which are v...

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Veröffentlicht in:IEEE journal of selected topics in applied earth observations and remote sensing Jg. 18; S. 6007 - 6016
Hauptverfasser: Peng, Chenyu, Shen, Tao, Wang, Qingwang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computational modeling
Computer vision
Convolution
Data models
Deep learning
Euclidean geometry
Euclidean space
Feature extraction
Geometry
Hyperbolic convolution
Hyperbolic coordinates
hyperbolic space
Image fusion
Image quality
Infrared imagery
Infrared imaging
Multisensor fusion
Neural networks
Object detection
Remote sensing
Similarity
Terrestrial environments
Transformers
ISSN:1939-1404, 2151-1535
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Zusammenfassung:Infrared and visible image fusion is essential for remote sensing applications, especially for obtaining high-quality imagery of terrestrial environments. Hierarchical feature information is crucial for image fusion as it captures the intricate relationships between different modalities, which are vital for producing detailed and accurate composite images. However, most existing methods operate within the confines of Euclidean space, which, due to its inherently "flat" geometric nature, often struggles to effectively measure the similarities and differences between modalities, thus failing to maintain their distinctiveness. Hyperbolic space, with its constant negative curvature, excels at leveraging these hierarchical structures. It can more effectively gauge the similarities and differences between modalities, preserving their distinctiveness. In this study, we propose a novel fusion method for infrared and visible image fusion in hyperbolic space, named HbFNet. We have developed innovative hyperbolic feature extraction modules, including Hyperbolic Invertible Neural Networks and Hyperbolic Lite Transformer blocks, specifically designed to capitalize on the hierarchical nature of features. Our method emerges as a promising solution for enhancing hierarchical information and elevating the quality of fusion. Extensive experiments across three public datasets have demonstrated that our method outperforms most state-of-the-art image fusion techniques.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2025.3540304