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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)
|July 8, 2023
Summary
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.
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.
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