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  3. Infrared And Visible Image Fusion Network Based On Self-compensating Lightweight Convolution.
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  3. Infrared And Visible Image Fusion Network Based On Self-compensating Lightweight Convolution.

Related Experiment Videos

Infrared and Visible Image Fusion Network Based on Self-Compensating Lightweight Convolution.

Ruolin Li1, Hongmei Wang1, Qiaorong Wu1

  • 1School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces LWC-DenseFuse, a novel deep learning network for infrared and visible image fusion. It efficiently fuses images with fewer parameters, preserving crucial texture and thermal details for better performance.

Keywords:
convolutional neural networksdepthwise separable convolutionimage fusionmodel lightweighting

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Deep learning enhances infrared and visible image fusion quality.
  • Existing networks are complex with many parameters, hindering efficiency.
  • Lightweight methods often degrade feature interactions and information.

Purpose of the Study:

  • To develop a lightweight infrared and visible image fusion network.
  • To address feature degradation and information loss in lightweight fusion models.
  • To balance fusion performance with computational efficiency.

Main Methods:

  • Proposed LWC-DenseFuse network with a self-compensating lightweight convolution module.
  • Decoupled spatial and channel correlations using depthwise and pointwise convolutions.
  • Integrated channel attention and channel shuffle for enhanced feature interaction and compensation.

    • Main Results:

    • Significantly reduced model parameters and achieved real-time inference.
    • Effectively mitigated performance degradation common in lightweight architectures.
    • Improved information entropy and visual fidelity in fused images.

    Conclusions:

    • LWC-DenseFuse offers an efficient and effective solution for infrared and visible image fusion.
    • The self-compensating lightweight convolution module successfully preserves critical information.
    • The proposed method achieves a superior balance between fusion performance and model efficiency.