Distortion Correction Device Using Region-Specific Approximation Formulas
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Solution Overview
Problem
Conventional image sensing devices face challenges in efficiently correcting distortion in images captured by solid-state image sensors due to the need for large memory storage for distortion correction values and limitations in handling various types of distortion, especially when the focal length differs from the set focal length.
Innovation Solution
A distortion correction device that uses approximation formulae specific to different pixel positions based on their image heights, employing a distortion data memory, a distortion calculator, and a coordinate calculator to calculate accurate distortion corrections, reducing memory requirements and enabling high-accuracy distortion correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distortion correction values for all pixel positions are stored in a data table, then distortion correction accuracy is improved, but memory capacity requirements increase significantly
Solution Approach 1:
The patent divides the image sensor array into multiple regions based on pixel position (e.g., central region, peripheral region, corner region). Each region has its own distortion correction characteristics and correction values. This segmentation allows the system to store and apply region-specific correction data, reducing the total memory required while maintaining high correction accuracy for each region.
Solution Approach 2:
The patent applies different distortion correction approaches for different regions of the image sensor. Central pixels use one correction method while peripheral and corner pixels use different methods, as each region experiences different distortion characteristics. This local quality approach optimizes correction accuracy without requiring uniform high-precision correction data for the entire sensor array.
2Quantity of substance
If a simple approximation formula is used for distortion correction, then memory requirements are reduced, but the ability to handle various distortion types deteriorates
Solution Approach 1:
The patent employs dynamic distortion correction by selecting different approximation formulas based on the pixel position and distortion characteristics. The system can adaptively switch between various correction models (e.g., linear approximation, quadratic approximation, or lookup table-based correction) depending on the specific distortion type and region, providing versatility without requiring all correction data to be stored simultaneously.
Solution Approach 2:
The patent changes the correction parameters and approximation methods based on the image height, pixel position, and distortion magnitude. By adjusting these parameters dynamically, the system can handle multiple distortion types (pincushion, barrel, mustache distortion) with a single flexible correction framework, avoiding the need for separate correction data for each distortion type.
3Reliability
If distortion correction is performed for all pixel positions uniformly, then correction completeness is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent segments the correction process by region, applying different computational methods to different pixel areas. Central regions may use faster but less precise correction while peripheral and corner regions use more precise but computationally intensive correction, as these regions typically have more significant distortion. This segmentation optimizes overall computational efficiency while maintaining complete correction coverage.
Solution Approach 2:
The patent applies partial distortion correction to certain regions where distortion is minimal or less critical, while applying full correction to regions with significant distortion. This partial action approach maintains correction completeness for the most important areas while reducing computational load overall, improving productivity without sacrificing essential correction reliability.
Data Source
AI summary
Distortion data discretely stored in a distortion data memory 8 are read out by a selector 9 and fed to a signal processor 5. For each coordinate position, the signal processor 5 calculates an approximation formula representing an image height-distortion curve based on the distortion data fed thereto, and performs distortion correction based on the approximation formula.