Improving dimensional measurement from noisy atomic force microscopy images by non‐local means filtering

Chen, Yuhang

doi:10.1002/sca.21246

Cited by 3 publications

(2 citation statements)

References 24 publications

(31 reference statements)

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“…Algorithms for double tip identification and correction in AFM data were tested using synthetic data with deposited particles of different coverage factors [ 24 ]. Simple patterns combined with Gaussian roughness were used for development of non-local means denoising for more accurate dimensional measurements [ 25 ]. Combined with real measurements, synthetic data were used to establish methodology for spectral properties evaluation on rough surfaces [ 26 ] and spectral properties determination from irregular regions [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Data in Quantitative Scanning Probe Microscopy

Nečas

Klapetek

2021

Nanomaterials

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Synthetic data are of increasing importance in nanometrology. They can be used for development of data processing methods, analysis of uncertainties and estimation of various measurement artefacts. In this paper we review methods used for their generation and the applications of synthetic data in scanning probe microscopy, focusing on their principles, performance, and applicability. We illustrate the benefits of using synthetic data on different tasks related to development of better scanning approaches and related to estimation of reliability of data processing methods. We demonstrate how the synthetic data can be used to analyse systematic errors that are common to scanning probe microscopy methods, either related to the measurement principle or to the typical data processing paths.

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Section: Introductionmentioning

confidence: 99%

Synthetic Data in Quantitative Scanning Probe Microscopy

Nečas

Klapetek

2021

Nanomaterials

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“…There is no method that can filter out all types of noise at the same time. Therefore, different filtering methods, according to the type of the noise, need to be applied to acquire high-quality images with removed noise [3–4]. Chen [5] has proposed “unsupervised destripe” to remove the non-uniform stripe noises from AFM images.…”

Section: Introductionmentioning

confidence: 99%

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

Zhang¹,

Li²,

Song³

et al. 2019

Beilstein J. Nanotechnol.

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A novel method based on Bayesian compressed sensing is proposed to remove impulse noise from atomic force microscopy (AFM) images. The image denoising problem is transformed into a compressed sensing imaging problem of the AFM. First, two different ways, including interval approach and self-comparison approach, are applied to identify the noisy pixels. An undersampled AFM image is generated by removing the noisy pixels from the image. Second, a series of measurement matrices, all of which are identity matrices with some rows removed, are constructed by recording the position of the noise-free pixels. Third, the Bayesian compressed sensing reconstruction algorithm is applied to recover the image. Different from traditional compressed sensing reconstruction methods in AFM, each row of the AFM image is reconstructed separately in the proposed method, which will not reduce the quality of the reconstructed image. The denoising experiments are conducted to demonstrate that the proposed method can remove the impulse noise from AFM images while preserving the details of the image. Compared with other methods, the proposed method is robust and its performance is not influenced by the noise density in a certain range.

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Signal Detection Theory as a Novel Tool to Understand Cognitive Fatigue in Individuals With Multiple Sclerosis

Román

DeLuca

Yao

et al. 2022

Front. Behav. Neurosci.

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Multiple Sclerosis (MS) affects 2.8 million persons worldwide. One of the most persistent, pervasive, and debilitating symptoms of MS is cognitive fatigue. While this has been known for over a century, cognitive fatigue has been difficult to study because patients’ subjective (self-reported) cognitive fatigue has consistently failed to correlate with more objective measures, such as reaction time (RT) and accuracy. Here, we investigated whether more nuanced metrics of performance, specifically the metrics of Signal Detection Theory (SDT), would show a relationship to cognitive fatigue even if RT and accuracy did not. We also measured brain activation to see whether SDT metrics were related to activation in brain areas that have been shown to be sensitive to cognitive fatigue. Fifty participants (30 MS, 20 controls) took part in this study and cognitive fatigue was induced using four blocks of a demanding working memory paradigm. Participants reported their fatigue before and after each block, and their performance was used to calculate SDT metrics (Perceptual Certainty and Criterion) and RT and accuracy. The results showed that the SDT metric of Criterion (i.e., response bias) was positively correlated with subjective cognitive fatigue. Moreover, the activation in brain areas previously shown to be related to cognitive fatigue, such as the striatum, was also related to Criterion. These results suggest that the metrics of SDT may represent a novel tool with which to study cognitive fatigue in MS and other neurological populations. These results hold promise for characterizing cognitive fatigue in MS and developing effective interventions in the future.

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Improving dimensional measurement from noisy atomic force microscopy images by non‐local means filtering

Cited by 3 publications

References 24 publications

Synthetic Data in Quantitative Scanning Probe Microscopy

Synthetic Data in Quantitative Scanning Probe Microscopy

A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

Signal Detection Theory as a Novel Tool to Understand Cognitive Fatigue in Individuals With Multiple Sclerosis

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