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Updated: Oct 16, 2025
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Published on: September 18, 2012
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Simulation of Atmospheric Visibility Impairment
Summary
Simulating atmospheric visibility impairment is crucial for developing adaptive computer vision algorithms. This study introduces novel simulation strategies for mist, fog, haze, smog, and dust, creating a large-scale dataset for research.
Area of Science:
- Computer Vision
- Atmospheric Science
- Image Processing
Background:
- Visibility impairment affects outdoor vision systems.
- Collecting real-world data for low visibility is challenging.
- Existing simulation methods have limitations and violate physical laws.
Purpose of the Study:
- Analyze limitations of current atmospheric visibility simulation.
- Develop accurate simulation strategies for common visibility impairments.
- Create a large-scale dataset for computer vision research.
Main Methods:
- Investigated atmospheric science literature.
- Developed simulation strategies for mist, fog, natural haze, smog, and Asian dust.
- Generated a synthetic dataset of 40,000 clear and 800,000 impaired images.
Main Results:
- Identified irrationalities in existing visibility simulation schemes.
- Proposed physically plausible simulation methods for diverse atmospheric conditions.
- Established a link between atmospheric science and computer vision simulation.
Conclusions:
- The proposed methods enable realistic simulation of atmospheric visibility.
- The generated dataset facilitates the development of robust vision algorithms.
- This work supports reproducible research in environmental computer vision.
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