Optical Distortion Correction for Repeating-Spot Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Optical distortion in imaging systems, particularly pronounced in line scanners, leads to shifts in spot positions on scanned images, causing data throughput drops and increased error rates during multi-cycle imaging runs.
Innovation Solution
A method involving a first imaging cycle to calculate distortion correction coefficients, which are then applied in subsequent cycles to correct for optical distortions by dividing imaging data into subsets, estimating affine transforms, sharpening images, and iteratively searching for optimal distortion correction coefficients using fiducials and signal intensities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical lens is used for imaging, then image capture is enabled, but optical distortion occurs causing spot position shifts
Solution Approach 1:
The system performs a preliminary imaging cycle to calculate distortion correction coefficients before actual data collection. These coefficients are stored and applied to subsequent imaging cycles to pre-correct for optical distortion, ensuring accurate spot positioning without requiring real-time correction during data acquisition.
Solution Approach 2:
The system uses fiducial markers with known positions to measure actual spot positions in the distorted image. The difference between expected and measured positions provides feedback for calculating distortion correction coefficients, which are then applied to correct the distortion in subsequent images.
2Measurement precision
If distortion correction is applied in real-time during multi-cycle imaging, then spot positioning accuracy improves, but data throughput decreases
Solution Approach 1:
Distortion correction coefficients are calculated during a preliminary imaging cycle before actual data collection begins. This allows the correction parameters to be established in advance, so that during multi-cycle imaging, only the pre-calculated coefficients need to be applied, minimizing real-time processing overhead and maintaining high data throughput.
Solution Approach 2:
The imaging process is divided into separate cycles: a preliminary cycle for calculating distortion coefficients, and subsequent cycles for data collection. This segmentation allows computationally intensive correction coefficient calculation to be performed once, while data collection cycles proceed at full speed with minimal processing overhead.
3Loss of time
If imaging data is processed without distortion correction, then processing speed is maintained, but error rate increases
Solution Approach 1:
Distortion correction coefficients are calculated in advance during a preliminary imaging cycle, storing the correction parameters for later use. This allows rapid application of corrections during data processing without performing iterative optimization during the actual data collection cycles, thus maintaining processing speed while ensuring accuracy.
Data Source
AI summary
Techniques are described for dynamically correcting image distortion during imaging of a patterned sample having repeating spots. Different sets of image distortion correction coefficients may be calculated for different regions of a sample during a first imaging cycle of a multicycle imaging run and subsequently applied in real time to image data generated during subsequent cycles. In one implementation, image distortion correction coefficients may be calculated for an image of a patterned sample having repeated spots by: estimating an affine transform of the image; sharpening the image; and iteratively searching for an optimal set of distortion correction coefficients for the sharpened image, where iteratively searching for the optimal set of distortion correction coefficients for the sharpened image includes calculating a mean chastity for spot locations in the image, and where the estimated affine transform is applied during each iteration of the search.