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Updated: Jul 7, 2026
04:43
Visualizing Visual Adaptation
Published on: April 24, 2017
Improving image quality in poor visibility conditions using a physical model for contrast degradation
1School of Engineering, University of Manchester, Manchester M13 9PL, UK. j.oakley@man.ac.uk
Summary
This study presents a novel method to enhance image contrast degraded by atmospheric haze and fog using a physically-based model and inverse problem-solving. The technique effectively restores image quality, especially when combined with temporal filtering for airborne imaging.
Area of Science:
- Computer Vision
- Atmospheric Optics
- Image Processing
Background:
- Image contrast is significantly reduced in daylight by atmospheric aerosols like haze and fog.
- Light scattering by aerosol particles and attenuation of reflected light cause contrast loss.
- Scene geometry is crucial for effective contrast restoration.
Purpose of the Study:
- To introduce a physically-based method for reducing contrast degradation caused by atmospheric aerosols.
- To develop an image processing algorithm that recovers scene information lost due to scattering and attenuation.
- To analyze the signal-to-noise ratio (SNR) of the enhanced images and propose solutions for range-dependent degradation.
Main Methods:
- A two-step method involving solving an inverse problem to recover three physical model parameters.
- Estimating the relative contributions of scattered and reflected light for each pixel.
- Subtracting estimated scatter contribution and scaling the remainder to compensate for attenuation.
- Implementing a temporal filter structure to address SNR decrease with range.
Main Results:
- Satisfactory agreement between the physical model and experimental data in hazy conditions.
- Demonstrated significant improvement in image quality using the contrast enhancement algorithm.
- The proposed temporal filter structure effectively addresses the exponential decrease in SNR with range.
- Enhanced images from airborne sequences in hazy and clear conditions showed improved contrast and detail.
Conclusions:
- The developed method effectively restores image contrast degraded by atmospheric aerosols.
- The combination of the contrast enhancement algorithm and temporal filtering significantly improves image quality for airborne applications.
- The physically-based model provides a reliable characterization of image degradation in hazy conditions.
- Further research can explore the application of this method in various remote sensing and surveillance scenarios.
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