Optical Imaging Performance Testing via PSF Modeling From Edge Data
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Solution Overview
Problem
Existing methods for testing the imaging performance of optical imaging systems, particularly in microscopy, are labor-intensive and lack fast, comprehensive feedback on system performance, leading to inefficiencies in manufacturing and potential degradation of samples due to improper focus and alignment, which affects image quality and processing accuracy.
Innovation Solution
A method and system for testing optical imaging performance involving positioning a test target, illuminating it, acquiring images at various defocus levels, calculating Edge Spread Functions, constructing Point Spread Function models, and evaluating metrics like Ensquared Energy, Encircled Energy, or Strehl ratio to provide a comprehensive assessment of imaging performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional imaging systems are used to capture retinal images, then the imaging process is simple, but the system cannot simultaneously capture both the posterior pole and peripheral retina in a single frame due to limited field of view
Solution Approach 1:
The retinal imaging system divides the retina into multiple quadrants (superior, inferior, nasal, temporal) and captures images of each quadrant separately using a standard imaging system, then reconstructs a complete panoramic view by stitching these segmented images together, thereby achieving a wide field of view without requiring a complex single-lens wide-angle system
Solution Approach 2:
The system introduces an intermediary processing step that uses the patient's own eye movements (saccades) as a natural mediator to capture different retinal regions sequentially, eliminating the need for complex mechanical scanning systems while achieving comprehensive retinal coverage
2Area of stationary object
If multiple imaging devices are used to capture different retinal regions, then the field of view is improved, but the alignment and stitching of images becomes complex and time-consuming
Solution Approach 1:
The system utilizes the patient's own eye movements (saccades) to automatically navigate between different retinal regions, eliminating the need for external mechanical scanning devices. The eye's natural motion serves the imaging function, significantly reducing system complexity and acquisition time
Solution Approach 2:
The system incorporates real-time feedback mechanisms that track eye position and use this information to dynamically adjust imaging parameters and guide the sequential capture of different retinal quadrants, enabling efficient image stitching without manual intervention
3Measurement precision
If standard imaging systems are used, then the system is easy to operate, but it cannot detect subtle pathological changes in the peripheral retina
Solution Approach 1:
By dividing the retinal examination into multiple focused quadrantal views rather than attempting to capture the entire retina in a single low-resolution frame, the system achieves high measurement precision for detecting subtle pathological changes in each region while collectively covering the entire retinal surface
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
Enables rapid, global analysis of optical system performance, providing feedback within seconds, improving manufacturing efficiency and image quality by ensuring consistent focus and alignment across the field of view, thereby enhancing the effectiveness of image processing and reducing labor costs.
Implementation Method 1
capturing an image of the retina using a camera
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
For testing the imaging performance of an optical system, a test target is positioned at an object plane of the optical system, and illuminated to generate an image beam. One or more images of the test target are acquired from the image beam. From the imaging data acquired, Edge Spread Functions at a plurality of locations within the test target are calculated. A model of Point Spread Functions is constructed from the Edge Spread Functions. Based on the Point Spread Functions, a plurality of imaging performance values corresponding to the plurality of locations are calculated. The imaging performance values are based on Ensquared Energy, Encircled Energy, or Strehl ratio.