研究目的
To restore electron beam measurements degraded by linear motion blur using a fuzzy inference system and Wiener inverse filter, enabling unsupervised deblurring without exact knowledge of the probe size, improving reliability, and facilitating probe-independent characterization.
研究成果
The proposed FIS-based method effectively restores EB measurements degraded by linear motion blur, providing unsupervised deblurring without exact probe size knowledge. It improves reliability and facilitates probe-independent characterization, with validation through cross-correlation and FWHM analysis showing accurate restoration. Future work could involve automated tuning and handling more complex cases.
研究不足
The FIS tuning was performed heuristically by an expert, which may not be optimal; clustering algorithms or adaptive FISs could improve automation and handling of more complex scenarios. The method assumes linear motion blur and may not generalize to other types of degradation.
1:Experimental Design and Method Selection:
The method combines a fuzzy inference system (FIS) with a Wiener inverse filter for deblurring. The FIS has three inputs (PSF length deviation, null frequency magnitude, deconvolution residue) and one output (deconvolution grade), using fuzzy rules and membership functions to grade restorations.
2:Sample Selection and Data Sources:
Ground truth signals were obtained using an 18 μm wire probe for benchmarking. Degraded EB measurements were used as samples.
3:List of Experimental Equipment and Materials:
An 18 μm wire probe was used for ground truth measurements. Specific equipment brands or models are not mentioned.
4:Experimental Procedures and Operational Workflow:
Null frequencies are extracted from the degraded signal's spectrum; deconvolutions are performed using Wiener filters with estimated PSF lengths; FIS evaluates the restorations based on inputs and selects the best one.
5:Data Analysis Methods:
Cross-correlation and full width at half maximum (FWHM) analysis were used to validate restorations against ground truth.
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