QR Code Image Correction via Sub-Region Compensation Vectors
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Solution Overview
Problem
Distorted QR code images captured by decoding apparatuses often require correction due to distortion or inadequate capturing capabilities, which existing methods fail to address effectively.
Innovation Solution
An image correction method that involves performing a feature point search on a QR code image, dividing the coded area into sub-regions, determining compensation vectors for each sub-region, and compensating and correcting them to obtain a corrected image, thereby avoiding interference between sub-regions and improving distortion correction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If global correction methods are used for distorted QR code images, then the correction process is simple, but the correction accuracy is insufficient due to interference between different regions
Solution Approach 1:
The patent divides the QR code image into multiple sub-regions and performs correction on each sub-region independently using local feature points and compensation vectors. This segmentation approach avoids interference between different regions while maintaining correction simplicity, resolving the contradiction between process simplicity and correction accuracy.
2Measurement precision
If regional correction with compensation vectors is implemented, then correction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by using local feature points within each sub-region to determine compensation vectors specific to that region. This approach improves correction accuracy for each local area while keeping the overall computational complexity manageable through localized processing rather than global computation.
Solution Approach 2:
By segmenting the image into sub-regions and processing them independently with local feature points, the patent reduces the computational burden compared to global methods while maintaining or improving accuracy. Each sub-region is corrected using only its local feature points, avoiding the need to process the entire image globally.
3Loss of information
If feature point search is performed on the entire QR code image, then all feature points are captured, but processing time increases
Solution Approach 1:
The patent performs feature point search only within each sub-region rather than across the entire QR code image. This segmentation of the feature point search process reduces processing time while ensuring that all necessary feature points are captured within their respective local regions, maintaining feature point completeness.
Solution Approach 2:
Instead of performing exhaustive feature point search on the entire image, the patent applies partial action by searching only within relevant sub-regions. This approach captures sufficient feature points for accurate correction without the time cost of searching the entire image, achieving an optimal balance between completeness and efficiency.
Data Source
AI summary
An image correction method and a processor are disclosed. The method includes performing a feature point search on a quick response (QR) code image to determine multiple feature points, dividing a coded area of the QR code image into multiple sub-regions according to the multiple feature points, determining a compensation vector for each sub-region according to the feature points corresponding to each sub-region, and compensating and correcting each sub-region according to the compensation vector of each sub-region to obtain a corrected image. Thus, the solution provided by the present application can avoid interference between different sub-regions by means of correcting the QR code image in a regional manner using the compensation vectors, thereby more accurately correcting the distortion of the QR code image.