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TGLFusion: A Temperature-Guided Lightweight Fusion Method for Infrared and Visible Images.

Bao Yan1, Longjie Zhao1, Kehua Miao1

  • 1School of Aerospace Engineering, Xiamen University, Xiamen 361102, China.

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Summary
This summary is machine-generated.

This study introduces a novel infrared and visible image fusion method that addresses information imbalance and computational complexity. The new approach enhances temperature and edge details for improved accuracy, especially on edge devices.

Keywords:
deep learningelectric power equipmentimage fusioninfrared and visible sensor imageslightweight model

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Infrared and visible image fusion is crucial for replacing manual observation, particularly on edge devices for real-time applications.
  • Existing fusion methods struggle with information imbalance between infrared and visible spectra, often neglecting critical temperature and edge texture details.
  • Computational complexity hinders the deployment of current fusion techniques on resource-constrained edge devices.

Purpose of the Study:

  • To develop an efficient and lightweight image fusion method that overcomes information imbalance issues.
  • To enhance the emphasis on temperature and edge texture information in fused images.
  • To enable adaptive fusion weight allocation for improved performance on edge devices.

Main Methods:

  • Proposes a novel method calculating infrared pixel value distribution to determine adaptive fusion weights.
  • Introduces a weight allocation mechanism and a lightweight MobileBlock with a multispectral information complementary module.
  • Employs a temperature-color-perception loss function for adaptive weight adjustment during training.

Main Results:

  • The proposed method demonstrates superior performance compared to existing mainstream fusion techniques.
  • Achieves enhanced highlighting of temperature and edge texture information, reducing misinterpretations.
  • The model is computationally efficient and suitable for deployment on edge devices.

Conclusions:

  • The developed fusion method effectively addresses information imbalance and computational challenges in infrared and visible image fusion.
  • The innovations lead to strengthened fusion capabilities, lightweight design, and essential information compensation.
  • The approach shows significant promise for real-world applications, particularly in electric power equipment monitoring.