Image Distortion Correction via Vector Product Decomposition
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Solution Overview
Problem
Current image distortion correction methods require large memory spaces while maintaining real-time capability, which is not feasible for ultrahigh-resolution video cameras and video-based driver assistance systems.
Innovation Solution
The method involves splitting a vector field into a sum of vector products, storing the terms of these products instead of the vector field values, and using simple computational operations to determine shift vectors for distortion correction, reducing memory requirements and maintaining real-time capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the values of the vector field are stored in memory for distortion correction, then the distortion correction can be performed in real time, but the memory requirement becomes very large
Solution Approach 1:
The vector field is segmented into a sum of vector products, where instead of storing the complete vector field values, only the terms of the vector products are stored. This segmentation allows the system to reconstruct the vector field values computationally during runtime, significantly reducing memory requirements while maintaining real-time correction capability
Solution Approach 2:
The vector products serve as an intermediary representation between the stored data and the actual vector field values. By storing and computing with vector product terms rather than direct vector field values, the system achieves a compact representation that reduces memory usage while enabling real-time reconstruction of correction values
2Speed
If a tabular imaging rule is used for distortion correction during reading out, then real-time correction is achieved, but the memory requirement becomes very large
Solution Approach 1:
The representation parameters of the distortion correction data are changed from storing complete vector field values to storing vector product terms. This parameter transformation enables a more efficient memory representation while maintaining the ability to perform real-time distortion correction through computational reconstruction of the correction values
Data Source
AI summary
A method for determining values which are suitable for distortion correction of an image, including the following steps: a step of splitting a vector field, which is suitable for distortion correction of the image, into a sum of vector products, and a step of determining terms of the vector products as suitable values for distortion correction of the image.