Radial Distance Mapping for Image Data Correction
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Solution Overview
Problem
Existing methods fail to effectively correct image data distorted by optical systems, particularly lenses, which affects the accuracy of image processing and requires complex computational efforts.
Innovation Solution
A correction method that involves reading image data from a calibration pattern, simulating a straight line through a reference point, determining sequences of measured and target values, and applying a mapping rule to correct image data, reducing computational complexity and ensuring stability and precision across the image field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing correction methods are used to correct image data distorted by optical systems, then correction can be achieved, but computational complexity increases and accuracy is insufficient
Solution Approach 1:
The patent transforms the correction problem from correcting the entire image to correcting only radial distance parameters. By changing the parameter space from 2D image coordinates to 1D radial distances from the optical center, the computational complexity is reduced while maintaining correction accuracy across the entire image field.
Solution Approach 2:
The patent extracts the essential distortion characteristic (radial distance from optical center) from the complex 2D image correction problem. By focusing only on the radial distance parameter that causes optical distortion, the method simplifies the correction process while achieving accurate results throughout the image.
2Manufacturing precision
If complex correction algorithms are applied to correct optical distortion, then correction precision may improve, but computational effort and processing time increase
Solution Approach 1:
The patent changes the correction approach from complex 2D coordinate transformations to simple 1D radial distance scaling. This parameter reduction enables real-time correction processing while maintaining high precision, as the correction factor can be calculated directly from the radial distance without iterative optimization.
Solution Approach 2:
The patent replaces complex iterative correction algorithms with a direct mathematical relationship based on radial distance. By substituting the mechanical/iterative correction process with a straightforward distance-based scaling operation, processing time is dramatically reduced while preserving correction accuracy.
3Stability of the object's composition
If conventional correction methods are used, then some correction can be achieved, but stability and precision cannot be maintained across the entire image field from center to corners
Solution Approach 1:
The patent applies local quality correction by using the radial distance from the optical center as the basis for correction. Each pixel is corrected according to its specific radial position, ensuring that the correction adapts locally to the distortion characteristics at each location while maintaining global consistency across the entire image field.
Solution Approach 2:
By transforming the correction parameter from global 2D coordinates to local radial distance, the patent achieves uniform precision across the image field. The radial distance parameter naturally captures the local distortion characteristics at each position while maintaining a consistent correction model throughout the entire image.
Data Source
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AI summary
The invention relates to a correction method, comprising the steps A), B), C), D), E), F). In step A), image data are read in, wherein the image data are representative of a calibration image (1') of a calibration pattern (1') recorded by means of an optics system (2). The calibration pattern comprises a plurality of structures (10), and the calibration image comprises correspondingly imaged structures (10'). In step B), a line (4) is simulated such that the line extends through a reference point (5), which subdivides the line into a first half-line (41) and a second half-line (42), and the first and second half-lines intersect the imaged structures at one or more intersection points (40). In step C), a first and a second sequence of measured values are determined, which represent the intersection points on the first half-line and the second half-line to the reference point. In step D), a third and a fourth sequence of target values are predetermined or determined, wherein the target values represent target distances of the intersection points on the first half-line and on the second half-line as reference point. In step E), a mapping rule is determined, which at least approximately maps the target values of the third and fourth sequences to the measured values of the first and second sequence. In step F), image data of an image recorded via an optics system are corrected by means of the mapping rule determined in step E).