Charged Particle Beam Image Restoration With Adaptive PSF Deconvolution
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Solution Overview
Problem
Existing charged particle beam apparatuses face challenges in automatically setting optimal parameters for image restoration, leading to variable image quality based on user experience and prolonged processing times, especially when dealing with noisy images, and conventional filters like the Wiener filter degrade restoration performance.
Innovation Solution
An image processing apparatus that automatically sets optimal parameters for image restoration using a constrained least square filter (CLSF) and optimization algorithms, such as gradient descent or robust regression, to enhance image quality and reduce noise amplification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual parameter adjustment by user is used for image restoration, then restoration quality can be improved through user experience, but processing time increases and results vary depending on user skill
Solution Approach 1:
The system performs self-adjustment of restoration parameters through automated algorithms (Richardson-Lucy, Wiener filter, total variation minimization) that independently optimize image quality without requiring manual user intervention. The processor automatically selects and adjusts parameters based on image characteristics, eliminating dependency on user experience while reducing processing time.
Solution Approach 2:
The system automatically changes restoration parameters (regularization parameters, filter settings, iteration counts) based on image noise levels and characteristics. Multiple parameter sets are prepared for different image conditions, and the system selects optimal parameters dynamically, resolving the contradiction between quality and time by automating parameter optimization.
2Productivity
If conventional filters like Wiener filter are used for image restoration, then processing can be performed, but restoration performance degrades significantly for images with high noise levels
Solution Approach 1:
The system employs a composite approach by combining multiple restoration methods (Richardson-Luc deconvolution, Wiener filter, total variation minimization) into a unified framework. Each method has complementary strengths, and their combination allows the system to maintain high restoration quality for noisy images while preserving processing efficiency, overcoming the limitations of individual conventional filters.
Solution Approach 2:
The system dynamically adapts restoration parameters and method selection based on image noise characteristics. For high-noise images, it adjusts regularization parameters and selects appropriate algorithms automatically, enabling the restoration process to maintain high quality across varying noise conditions rather than using fixed conventional filter settings.
3Productivity
If automated parameter setting is implemented, then processing speed increases and user dependency decreases, but system complexity increases
Solution Approach 1:
The system uses feedback mechanisms where the processor evaluates image characteristics (noise levels, blur程度) and automatically adjusts restoration parameters accordingly. This closed-loop approach enables automated parameter setting that adapts to different images, achieving high processing speed and quality without requiring complex manual configuration interfaces.
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.