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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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CMMDL: Cross-modal multi-domain learning method for image fusion.

Di Yuan1, Huayi Zhu1, Rui Chen1

  • 1Guangzhou Institute of Technology, Xidian University, Guangzhou, 510555, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 17, 2025
PubMed
Summary

This study introduces Cross-Modal Multi-Domain Learning (CMMDL) for image fusion, enhancing deep learning by integrating spatial and frequency domains. CMMDL achieves state-of-the-art results by effectively fusing multi-modal image information.

Keywords:
Cross-domain feature fusionCross-domain feature interactionDual-domain parallel learning strategyMulti-modality image fusion

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Signal Processing

Background:

  • Deep learning excels at end-to-end multi-modal image fusion.
  • Existing methods often neglect valuable frequency domain information, leading to loss of high-frequency details in fused images.

Purpose of the Study:

  • To propose a novel Cross-Modal Multi-Domain Learning (CMMDL) method for superior image fusion.
  • To address limitations of spatial-domain-focused methods by incorporating frequency domain analysis.

Main Methods:

  • Employed Restormer with Spatial-Frequency domain Cascaded Attention (SFCA) for detailed feature extraction.
  • Introduced a dual-domain parallel learning strategy with Spatial Domain Learning Blocks (SDLB) and Frequency Domain Learning Blocks (FDLB).
  • Utilized a Heterogeneous Domain Feature Fusion Block (HDFFB) for cross-domain feature interaction and fusion.

Main Results:

  • The CMMDL method demonstrated state-of-the-art performance across multiple datasets.
  • Achieved superior fusion results compared to existing image fusion techniques.
  • Successfully preserved and integrated high-frequency details from source images.

Conclusions:

  • CMMDL effectively leverages both spatial and frequency domains for advanced image fusion.
  • The proposed method offers a significant improvement in fusing multi-modal image data.
  • The approach provides a robust solution for complex image fusion tasks.