Charged Particle Image Quality Normalization for Stable ANN Analysis
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Solution Overview
Problem
Charged particle microscopy images are sensitive to imaging conditions, leading to degradation in analysis and manipulation results if the data is noisy, out of focus, or affected by inappropriate instrument conditions, making it challenging for ANNs and CNNs to maintain consistent performance.
Innovation Solution
A method implemented by a data processing apparatus that receives an image, sets a desired image quality parameter, analyzes the image using an image analysis technique, compares the current image quality to the set-point, and applies an image modification technique using ANNs or CNNs to either improve or deteriorate the image quality to match moderate, training-condition-like quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ANNs and CNNs are used for image processing in charged particle microscopy, then image analysis and manipulation can be performed, but the performance degrades when imaging conditions vary (noise, focus, instrument conditions)
Solution Approach 1:
An image quality assessment system acts as an intermediary between the variable-quality microscope images and the ANN/CNN analysis system. This intermediary evaluates image quality parameters (noise, focus, illumination) and applies appropriate preprocessing or postprocessing operations to transform images into a standardized format suitable for consistent neural network analysis, thereby mediating the mismatch between variable input conditions and requirements for stable analysis performance
2Reliability
If network retraining is performed to adapt to new imaging conditions, then analysis performance can be maintained, but computation time and expert input requirements increase significantly
Solution Approach 1:
The system performs preliminary image quality assessment and preprocessing operations before images are fed to the ANN/CNN for analysis. By evaluating image quality parameters in advance and applying appropriate corrections or transformations beforehand, the system prepares images in a format that maintains consistent analysis performance without requiring retraining of the neural networks, thus avoiding the time-consuming retraining process
3Measurement precision
If image quality is improved for all images, then analysis performance increases, but processing time and computational resources increase
Solution Approach 1:
Instead of uniformly improving all images, the system applies quality assessment and selective preprocessing only where needed. Images that already meet quality thresholds are processed directly, while only those with specific deficiencies (excessive noise, poor focus, inadequate illumination) receive targeted preprocessing operations. This localized approach maintains high analysis performance for critical images while preserving overall processing throughput
Data Source
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AI summary
The invention relates to a method implemented by a data processing apparatus, comprising the steps of receiving an image; providing a set-point for a desired image quality parameter of said image; and processing said image using an image analysis technique for determining a current image quality parameter of said image. In the method, the current image quality parameter is compared with said desired set-point. Based on said comparison, a modified image is generated by using an image modification technique. The generating comprises the steps of improving said image in terms of said image quality parameter in case said current image quality parameter is lower than said set-point; and deteriorating said image in terms of said image quality parameter in case said current image quality parameter exceeds said set-point. The modified image is then output.