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Infrared and visible image fusion with spectral graph wavelet transform
Summary
This study introduces a new infrared and visible image fusion method using spectral graph wavelet transform (SGWT) and bilateral filtering. The novel approach enhances scene description by preserving details and improving irregular area representation.
Area of Science:
- Image analysis and computer vision.
- Signal processing and multiscale transforms.
Background:
- Infrared and visible image fusion integrates complementary information for scene description.
- Multiscale transform theory is a common signal representation for image fusion.
Purpose of the Study:
- To propose a novel infrared and visible image fusion method.
- To leverage spectral graph wavelet transform (SGWT) for enhanced image fusion.
Main Methods:
- Decomposition of source images using SGWT.
- Fusion of low- and high-frequency subbands via a weighted average method incorporating bilateral filtering.
- Utilizing spatial consistency of natural images for fusion.
Main Results:
- Effective preservation of details from source images.
- Excellent representation of irregular areas in fused images.
- Superior performance compared to seven recent fusion methods based on visual and objective metrics.
Conclusions:
- The proposed SGWT-based fusion method offers significant improvements in infrared and visible image fusion.
- The integration of bilateral filtering enhances spatial consistency in the fusion process.
- The method provides a reliable and accurate description of scenes.
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