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Related Experiment Video

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Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

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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.

Neural Networks : the Official Journal of the International Neural Network Society

|November 29, 2025

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.

Keywords:

Multi-modal image fusion Quaternion Spatial-frequency aware Wavelet transform

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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.