Edge Detection Bias Correction via Blurring Factor Characterization
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Solution Overview
Problem
Current edge detection algorithms in imaging systems face challenges in accurately locating curved edges due to biases caused by the imaging system's point spread function and noise removal procedures, leading to inaccuracies and false detections.
Innovation Solution
An image processing method that detects edges in images, characterizes a blurring factor, and corrects biases using a Gaussian smoothing kernel's standard deviation, allowing for precise edge localization through a look-up table-based correction mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If edge detection algorithms are applied to images, then edges can be detected, but bias in edge location occurs due to point spread function and noise removal procedures
Solution Approach 1:
The patent applies preliminary action by performing bias correction before final edge localization. The method pre-calculates bias values based on the point spread function and noise removal parameters, then applies these corrections to edge locations before outputting results. This ensures that the edge detection process accounts for systematic biases in advance, improving both measurement precision and reliability.
Solution Approach 2:
The patent implements feedback by using the characterized blurring factor to adjust and correct edge locations. The system continuously refines edge detection by comparing detected edges against the known bias characteristics and applying corrective transformations. This feedback loop ensures that edge locations are accurately corrected despite the initial bias introduced by imaging system characteristics.
2Object-affected harmful factors
If noise removal procedures are applied to images, then noise is reduced, but bias in curved edge location is introduced
Solution Approach 1:
The patent converts the harmful effect of noise removal bias into a beneficial correction mechanism. By characterizing the blurring factor and understanding the systematic bias introduced by noise removal procedures, the system calculates compensatory corrections that are applied to edge locations. This transforms the previously harmful bias into a known quantity that can be systematically corrected, improving curved edge location accuracy.
3Reliability
If point spread function effects are considered, then image formation is more accurate, but bias in edge location increases
Solution Approach 1:
The patent introduces an intermediary correction step between image formation and edge detection. The characterized blurring factor acts as an intermediary that mediates the relationship between the point spread function effects and the final edge location measurements. By applying bias corrections based on this intermediary characterization, the system maintains the accuracy benefits of considering point spread function effects while eliminating the resulting edge location bias.
Data Source
AI summary
An imaging system for correcting a bias in the location edges in an image is provided. The imaging system comprises an image processor configured to detect edges in an image of a given substructure, characterize a blurring factor in the image and correct a bias in the detected edges in the of a given substructure using the blurring factor.