Optical Boundary Positioning via Frequency Domain Analysis
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Solution Overview
Problem
Existing methods for determining the position of an optical boundary surface in microscopy are sensitive to noise and illumination differences, leading to unstable results and requiring empirical parameter adjustments, lacking a robust solution.
Innovation Solution
A method involving imaging a periodic pattern, averaging and transforming the data to determine the frequency of the greatest amplitude, which allows for a robust determination of the optical boundary surface position without relying on derivative-based contrast functions, and includes a device with an illuminating and imaging module for implementing this method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If derivative-based contrast functions are used to determine axial position, then the position can be calculated, but the results become sensitive to noise and illumination differences
Solution Approach 1:
Instead of using derivative-based contrast functions that amplify noise, the patent inverts the approach by using integral-based contrast functions that integrate signal information. This inversion transforms the mathematically sensitive derivative operation into a more stable integral operation, reducing sensitivity to noise and illumination variations while maintaining axial position determination capability
Solution Approach 2:
The patent converts the harmful effect of noise and illumination variations into a benefit by using integration operations. The integral-based contrast function accumulates signal information over the pattern period, which naturally filters out high-frequency noise components and compensates for illumination variations, turning what would be detrimental factors into advantages for measurement stability
2Reliability
If additional filters and segmentations with empirical parameters are applied, then the instability is remedied, but the device complexity and parameter adjustment burden increase
Solution Approach 1:
The patent extracts and removes the problematic derivative operation from the contrast function calculation, replacing it with a simpler integral-based approach. This extraction eliminates the need for multiple filtering stages and complex segmentation parameters, achieving stable position determination with a more straightforward computational method that requires fewer empirical adjustments
Solution Approach 2:
The patent changes the mathematical parameter from derivative-based to integral-based contrast function. This fundamental parameter change transforms the computational approach, making the system inherently more stable without requiring additional filters or segmentation parameters, thereby reducing device complexity and empirical parameter adjustment burden
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a robust and precise method for determining the position of optical boundary surfaces, reducing sensitivity to noise and illumination variations, and eliminating the need for empirical parameter adjustments, thereby improving the accuracy and reliability of the measurement.
Implementation Method 1
transforming each data set from step c) into a frequency domain
Implementation Method 2
the contrast of the grating pattern imaged on the boundary surface and detected with the camera depends on the axial position
Data Source
AI summary
A method for determining the position of an optical boundary surface along a first direction includes a) imaging a pattern in a plane transverse to the first direction, and recording a two-dimensional image of the pattern imaged in the plane; b) repeating step a) for different positions in the first direction, wherein the different positions cover an area in the first direction in which the optical boundary surface lies; c) averaging each image from step a) along a direction transverse to the second direction such that in each case a one-dimensional data set is produced; d) transforming each data set from step c) into a frequency domain; e) determining the frequency of the greatest amplitude of all data sets transformed in step d); and f) determining the position along the first direction by evaluating the data sets transformed in step d) at the frequency determined in step e).