Dynamic Quality Threshold for Displacement Estimation
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Solution Overview
Problem
Conventional displacement estimation methods using optical displacement estimation devices face inaccuracies due to fixed quality thresholds, leading to potential error displacements caused by low image quality, which is often a result of high noise levels, and cannot dynamically adjust based on sampling parameters or noise levels.
Innovation Solution
A method and device that dynamically adjust the quality threshold based on the sampling parameter and noise level of an image frame by determining a predetermined noise level, calculating a quality parameter, and comparing it to a quality threshold to determine whether to post-process the image frame for accurate displacement estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed quality threshold is used for image validation, then the identification process is simple, but error displacements occur due to invalid images with high noise levels
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed quality threshold to a dynamic quality threshold that adapts to current imaging conditions. The processing unit determines a dynamic quality threshold based on real-time parameters such as exposure time, gain settings, and measured noise levels, allowing the system to maintain reliable displacement estimation across varying operational conditions while keeping the identification process manageable.
2Manufacturing precision
If the sampling parameter is changed to adjust image quality, then image quality can be improved, but the noise level changes and may still result in error displacements
Solution Approach 1:
The patent implements feedback by measuring the actual noise level in acquired images and using this measurement to adjust the quality threshold dynamically. The processing unit calculates a quality parameter from the image data, compares it against the dynamically determined threshold, and makes informed decisions about whether to use the image for displacement estimation. This closed-loop approach ensures that sampling parameter changes lead to improved reliability rather than just raw image quality.
3Measurement precision
If a dynamic quality threshold is implemented based on sampling parameters and noise levels, then displacement estimation accuracy increases, but the processing complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the quality threshold based on measurable imaging parameters such as exposure time, gain settings, and noise levels. Rather than implementing a completely complex adaptive system, the patent changes the threshold parameter in response to changes in other imaging parameters, achieving improved measurement precision through a manageable increase in processing complexity that focuses on threshold determination rather than overhauled image processing.
Data Source
AI summary
The present invention provides a displacement estimation method including the steps of: acquiring an image frame and determining a quality threshold according to a sampling parameter; calculating a quality parameter of the image frame; and comparing the quality parameter and the quality threshold to determine whether to post-process the image frame. In the displacement estimation method of the present invention, the quality threshold can be adjusted dynamically so as to reduce the possibility of outputting error displacement. The present invention further provides a displacement estimation device.