Fog Image Cleaning via Atmospheric Scattering Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for removing fog from images in real-time applications, such as traffic monitoring systems, are impractical due to the need for multiple images, complex calculations, and unsuitability for color cameras, leading to inefficiencies and inaccuracies in fog removal.
Innovation Solution
A method using Sobel image processing, normalization values, and atmospheric scattering theory to determine fog levels and enhance image visibility by analyzing luminance, chromaticity, and attenuation coefficients, allowing for real-time fog removal in single images without requiring multiple images or extensive calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple images are used for fog removal, then fog removal accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential information needed for fog removal (luminance, chromaticity, attenuation coefficient) from the single input image, eliminating the need for multiple images while maintaining fog removal accuracy through targeted feature analysis
Solution Approach 2:
The patent segments the image processing into distinct modules: luminance analysis, chromaticity analysis, attenuation coefficient calculation, and fog removal execution. This segmentation allows independent optimization of each module and reduces overall system complexity
2Measurement precision
If complex calculations are used for fog removal, then fog removal accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent changes the calculation parameters to compute only the essential atmospheric scattering parameters (luminance, chromaticity, attenuation coefficient) rather than performing comprehensive image restoration calculations, achieving accurate fog removal with reduced computational complexity and faster processing speed
Solution Approach 2:
The patent applies partial action by calculating only the necessary atmospheric parameters needed for fog removal rather than performing complete image processing, achieving sufficient accuracy without the computational burden of exhaustive calculations
3Reliability
If traditional fog removal methods are used, then fog removal capability is achieved, but color information accuracy is lost
Solution Approach 1:
The patent explicitly analyzes chromaticity changes in the image to understand fog effects and uses this information to preserve and enhance color accuracy during fog removal, rather than treating color information as secondary to fog removal capability
Solution Approach 2:
The patent combines multiple analysis approaches (luminance analysis, chromaticity analysis, attenuation coefficient calculation) into a composite fog removal system that maintains color information accuracy while achieving reliable fog removal
4Speed
If real-time processing is implemented, then response time is improved, but calculation complexity increases
Solution Approach 1:
The patent extracts only the essential calculation steps needed for real-time fog removal (luminance computation, chromaticity analysis, attenuation coefficient calculation), eliminating unnecessary computational steps while maintaining real-time processing capability through targeted processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively removes fog from images in real-time, improving image clarity and accuracy for applications like traffic monitoring, enhancing safety and efficiency in low-visibility conditions.
Implementation Method 1
a cleaning method for foggy images based on atmospheric scattering theory and color analysis
Implementation Method 2
performing Sobel image processing on the input image to generate a Sobel image
Data Source
AI summary
Determining if an input image is a foggy image includes determining an average luminance gray level of the input image, performing Sobel image processing on the input image to generate a Sobel image of the input image when the average luminance gray level of the input image is between a first image average luminance and a second image average luminance, determining a first normalization value and a second normalization value of the input image, determining a mean value and a standard deviation of the Sobel image when the first normalization value and the second normalization value are less than a first threshold value, and determining the input image as a foggy image when a sum of the mean value and the standard deviation is less than a second threshold value.