Regional Image Correction for Lens Tilt and Deformation
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Solution Overview
Problem
Images captured by cameras often suffer from tilting or deformation due to lens angle and light conditions, necessitating effective correction methods for accurate image processing and defect detection.
Innovation Solution
The method involves dividing distorted images into regions, selecting corresponding points, determining a transformation matrix or ratio between these regions, and correcting pixel values based on these relationships to stitch corrected regions, thereby improving image accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image correction is performed using traditional methods, then the correction process is simple, but the correction accuracy is low due to tilting and deformation
Solution Approach 1:
The patent divides the image into multiple regions (first image regions and second image regions) and performs correction on each region separately using corresponding point relationships. This segmentation approach improves correction accuracy for each local area while managing overall complexity through systematic processing of divided regions.
Solution Approach 2:
The patent determines transformation matrices or ratio relationships between corresponding points in different image regions and uses these parameter changes to correct pixel coordinates and achieve accurate alignment. This parameter-based transformation method significantly improves correction accuracy compared to traditional uniform correction approaches.
2Manufacturing precision
If the entire image is corrected as a whole, then the process is simple, but the alignment precision between different regions is poor
Solution Approach 1:
The patent divides the image into multiple first image regions and second image regions, then determines corresponding point relationships and transformation parameters for each region pair. This region-by-region correction approach achieves precise alignment between corresponding areas while maintaining manageable process complexity through systematic regional processing.
Solution Approach 2:
The patent applies different transformation relationships to different image regions based on their specific corresponding point relationships. Each region undergoes customized correction based on its local geometric characteristics, achieving high alignment precision for each area rather than applying a uniform transformation to the entire image.
3Measurement precision
If more correction parameters are used to improve accuracy, then the correction precision improves, but the computational complexity increases
Solution Approach 1:
The patent determines transformation matrices or ratio relationships between corresponding points in different image regions and uses these parameter changes to correct pixel coordinates. This approach achieves high correction precision through mathematical transformation while controlling computational complexity by focusing calculations on key corresponding points rather than all pixels.
Data Source
AI summary
In an image correction method, a distorted image and a standard image corresponding to the distorted image are obtained. The distorted image is divided into first image regions, the standard image into second image regions. For each first image region and corresponding second image region, first and second points respectively are selected. A relationship between the first image region and the second image region is determined according to differences between the first points and the second points. The first image region is corrected according to the relationship between the first image region and the second image region and corrected first image regions are stitched to obtain a corrected image of the distorted image. The image correction method improves the accuracy of image correction.