Blur Correction System Using Segment-Specific Multipliers
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Solution Overview
Problem
Blur in images generated by cameras leads to inaccurate range measurements due to the enlargement of object extents, causing biased range determinations and inefficiencies in manual correction processes.
Innovation Solution
A method for determining blur correction parameters using multipliers and constants that result in mean error thresholds, selecting optimal multipliers and constants to create a data file for a platform computer system, enabling accurate and efficient correction of blur in images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If blur correction parameters are determined manually, then flexibility in adjustment is maintained, but the process is time-consuming and inefficient
Solution Approach 1:
The system automatically determines blur correction parameters by processing test images and calculating statistics without human intervention. The computer system performs all operations including determining blur radii, calculating mean errors, and selecting optimal parameters independently, eliminating the time-consuming manual process while maintaining correction effectiveness
Solution Approach 2:
The system performs preliminary determination of blur correction parameters before actual image analysis. By pre-calculating optimal parameters through automated testing and statistical analysis, the system prepares correction values in advance, making the actual correction process faster and more efficient
2Measurement precision
If blur is not corrected, then the processing is simpler and faster, but range measurements become inaccurate due to object enlargement
Solution Approach 1:
The system changes parameters (multiplier and constant values) to optimize blur correction for different conditions. By adjusting these parameters based on statistical analysis of test images, the system achieves accurate range measurements while managing complexity through systematic parameter optimization rather than complex algorithms
Solution Approach 2:
The system replaces manual mechanical adjustment of blur parameters with automated computer-based calculation and selection. This substitution eliminates the need for human operators to manually tweak parameters, reducing operational complexity while improving measurement precision through consistent automated processing
3Measurement precision
If a single blur correction parameter is used for all pixel segments, then the process is simpler, but accuracy decreases for segments with different blur characteristics
Solution Approach 1:
The system divides the image into different pixel segments and determines separate blur correction parameters for each segment. This segmentation allows each region to have optimized parameters based on its specific blur characteristics, improving overall measurement accuracy while managing complexity through systematic segment-based processing
Solution Approach 2:
The system applies different blur correction parameters to different pixel segments based on their local characteristics. By allowing parameters to vary locally across different segments rather than using a single global parameter, the system achieves higher precision for segments with different blur properties while maintaining manageable complexity through localized optimization
Data Source
AI summary
A method for blur correction determines a first constant for a first pixel segment and a second constant for a second pixel segment for each of the multipliers that results in a first mean error for the first pixel segment being and a second mean error for the second pixel segment being within a mean error threshold. A first multiplier is selected from the multipliers with the corresponding first constant for the first pixel segment and second multiplier is selected from the multipliers with the corresponding second constant for the second segment that has the probability of the frames with the measured ranges being within the selected error threshold. A data file comprising the first multiplier, the corresponding first constant for the first multiplier, the second multiplier, and the corresponding second constant for the second multiplier is created for a platform computer system for a platform.