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QWNet: A quaternion wavelet network for spatial-frequency aware multi-modal image fusion
Jietao Yang1, Miaoshan Lin1, Guoheng Huang1
1Guangdong University of Technology, Guangzhou, 510006, Guangdong Province, China.
Summary
QWNet, a novel Quaternion Wavelet Network, improves multi-modal image fusion by integrating frequency and spatial information. This approach enhances visual tasks like semantic segmentation with superior fusion quality and efficiency.
Area of Science:
- Computer Vision
- Signal Processing
- Deep Learning
Background:
- Multi-modal Image Fusion (MMIF) combines image modalities to enhance visual tasks.
- Existing MMIF methods lack frequency-domain awareness and overlook inter-channel relationships.
- Challenges include adaptive fusion and modeling complex dependencies.
Purpose of the Study:
- Propose QWNet, a Quaternion Wavelet Network for enhanced MMIF.
- Address limitations of existing frequency-domain and channel combination techniques.
- Improve object visibility, texture details, and downstream task performance.
Main Methods:
- Utilize wavelet transforms for spatial and frequency decomposition.
- Represent components as quaternions to model complex inter-channel dependencies.
- Introduce Bidirectional Adaptive Attention Module (BAAM) and Quaternion Cross-modal Fusion Module (QCFM).
Main Results:
- QWNet demonstrates superior fusion quality compared to existing methods.
- Achieved state-of-the-art performance in downstream tasks like semantic segmentation.
- Efficient with only 4.27 K parameters and 0.30G FLOPs.
Conclusions:
- QWNet effectively harnesses spatial and frequency information for MMIF.
- The proposed modules enhance feature interaction and fusion.
- QWNet offers a promising, efficient solution for advanced visual tasks.
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