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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Infrared-Visible Image Fusion Based on Semantic Guidance and Visual Perception.

Xiaoyu Chen1, Zhijie Teng1, Yingqi Liu1

  • 1Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, China.

Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces the SGVPGAN, an advanced infrared-visible image fusion framework. It enhances night-vision for intelligent vehicles by improving target saliency and visual perception.

Keywords:
infrared-visible image fusionintelligent vehiclessemantic guidancevisual perception

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Infrared-visible fusion is crucial for intelligent vehicle night vision.
  • Existing fusion methods struggle with target saliency and visual perception.
  • Lack of effective fusion rules leads to poor contrast and target visibility.

Purpose of the Study:

  • To propose the SGVPGAN, an adversarial framework for high-quality infrared-visible image fusion.
  • To enhance target highlighting and natural visual appearance in fused images.
  • To improve night-vision capabilities for intelligent vehicles.

Main Methods:

  • Developed the SGVPGAN framework with Adversarial Semantic Guidance (ASG) and Adversarial Visual Perception (AVP) modules.
  • ASG module transfers semantics for target highlighting.
  • AVP module analyzes visual features to guide adaptive weight map generation for natural appearance.

Main Results:

  • The ASG and AVP modules effectively guide the fusion process.
  • Preserves details from visible images and salient information from infrared images.
  • SGVPGAN shows significant improvements over existing fusion methods.

Conclusions:

  • The proposed SGVPGAN framework successfully enhances infrared-visible image fusion.
  • Achieves superior performance in target saliency and visual naturalness.
  • Offers significant potential for intelligent vehicle night-vision systems.