Charged Particle Image Deconvolution With Automatic PSF Tuning
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Solution Overview
Problem
Existing image restoration methods in charged particle beam apparatuses rely heavily on user-adjusted parameters, leading to variable image quality and prolonged processing times, and degrade significantly when dealing with noisy images.
Innovation Solution
An image processing apparatus automatically sets optimal parameters for image restoration, using filters like the constrained least square filter (CLSF) to maintain high quality even in noisy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user-adjusted parameters are used in image restoration, then flexibility and adaptability are improved, but image quality consistency deteriorates and processing time increases
Solution Approach 1:
The system automatically determines restoration parameters and performs image restoration without requiring manual user adjustment. The image processing apparatus self-evaluates restoration quality and iteratively optimizes parameters, eliminating dependency on user experience while maintaining consistent high-quality results.
Solution Approach 2:
The system incorporates a feedback mechanism where restoration quality is automatically evaluated and used to adjust parameters for subsequent restoration iterations. This closed-loop approach ensures continuous optimization and consistent image quality without manual intervention.
2Adaptability or versatility
If user-adjusted parameters are used in image restoration, then flexibility is improved, but processing time deteriorates
Solution Approach 1:
The automated parameter determination and quality evaluation system eliminates the need for manual parameter adjustment by users, thereby removing the time cost associated with expert intervention while maintaining restoration flexibility through algorithmic adaptation.
Solution Approach 2:
The system performs preliminary automatic parameter optimization and quality evaluation before final image restoration, preparing optimal parameters in advance to accelerate the overall processing workflow without requiring time-consuming manual adjustments.
3Reliability
If Wiener filter is applied to restore noisy images, then restoration capability is improved, but restoration performance deteriorates due to noise
Solution Approach 1:
The system automatically adjusts restoration parameters based on noise characteristics and image content, dynamically optimizing the restoration process for different noise levels and types. This adaptive parameter adjustment maintains high restoration performance even in noisy conditions where fixed-parameter filters like Wiener filter fail.
Data Source
AI summary
Provided is a method of deconvoluting and restoring an image observed in a charged particle beam apparatus. The method includes receiving, by an image processing apparatus, an observed image acquired by a detector of the charged particle beam apparatus, calculating, by the image processing apparatus, a point spread function (PSF), deconvoluting and restoring, by the image processing apparatus, the observed image using the observed image and the PSF, calculating, by the image processing apparatus, an evaluation function of the parameter applied to a process of the deconvoluting, and adjusting, by the image processing apparatus, the parameter on the basis of a result of the evaluation function, and restoring the image after deconvoluting the observed image and the PSF again using an optimal parameter. Furthermore, a charged particle beam apparatus using the above-described method of deconvoluting and restoring the image is provided.