Image noise cross‐correlation for signal‐to‐noise ratio estimation in scanning electron microscope images

Sim, K. S.; Nia, M. E.; Tso, C.P.

doi:10.1002/sca.20223

Cited by 15 publications

(22 citation statements)

References 5 publications

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“…Two sample images of power IC, captured at beam diameters 151 and 89 nm and beam energy of 5 keV, are shown in Figures 3(c) and (d). The simple method, the first order interpolation method, the AR‐based technique (Sim & Nidal, 2004) and the INCCE technique (Sim et al, 2011) are used to estimate the SNR values.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, we give a summary on the image noise cross‐correlation estimation method. Sim et al (2011) estimated the zero‐offset pick denoted as by estimating the zero‐offset pick of the noise itself as a separate value with respect to the noise variance. The zero‐offset pick of the autocorrelation of the original image, , is then obtained with which is the fundamental of image noise cross‐correlation SNR estimation method as described below: …”

Section: The Snr Estimatorsmentioning

confidence: 99%

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Performance of new signal‐to‐noise ratio estimation for SEM images based on single image noise cross‐correlation

Sim

Nia

Tso

et al. 2012

Journal of Microscopy

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SummaryA new technique for estimation of signal-to-noise ratio in scanning electron microscope images is reported. The method is based on the image noise cross-correlation estimation model recently developed. We derive the basic performance limits on a single image signal-to-noise ratio estimation using the Cramer-Rao inequality. The results are compared with those from existing estimation methods including the nearest neighbourhood (the simple method), the first order linear interpolator, and the autoregressive based estimator. The comparisons were made using several tests involving different images within the performance bounds. From the results obtained, the efficiency and accuracy of image noise crosscorrelation estimation technique is considerably better than the other three methods.

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Section: Resultsmentioning

confidence: 99%

Section: The Snr Estimatorsmentioning

confidence: 99%

Performance of new signal‐to‐noise ratio estimation for SEM images based on single image noise cross‐correlation

Sim

Nia

Tso

et al. 2012

Journal of Microscopy

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“…In this INCCE method proposed by Sim et al . (), the noise power spectrum and the zero‐offset pick of the noise ACF itself, denoted as

r_{11}^{N o i s e} (0, 0)

, is estimated as a separate value with respect to the noise variance. The result is then used to estimate the noise‐free ACF as below:

r_{11}^{N F} (0, 0) = r_{11}^{N o i s y i m a g e} (0, 0) - r_{11}^{N o i s e} (0, 0)

where the zero‐offset pick of the noise‐free image ACF is denoted as

r_{11}^{N F} (0, 0)

.…”

Section: Acf Estimatorsmentioning

confidence: 99%

Noise Variance Estimation Using Image Noise Cross‐Correlation Model on SEM Images

2012

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A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.

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“…Sim et al . (a, , ) have studied the final noise in SEM images. They assumed this noise is additive white noise and have developed many methods for estimating image signal‐to‐noise ratio (SNR) based on image cross‐correlation.…”

Section: Introductionmentioning

confidence: 97%

Scanning electron microscope image signal‐to‐noise ratio monitoring for micro‐nanomanipulation

2014

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As an imaging system, scanning electron microscope (SEM) performs an important role in autonomous micro-nanomanipulation applications. When it comes to the sub micrometer range and at high scanning speeds, the images produced by the SEM are noisy and need to be evaluated or corrected beforehand. In this article, the quality of images produced by a tungsten gun SEM has been evaluated by quantifying the level of image signal-to-noise ratio (SNR). In order to determine the SNR, an efficient and online monitoring method is developed based on the nonlinear filtering using a single image. Using this method, the quality of images produced by a tungsten gun SEM is monitored at different experimental conditions. The derived results demonstrate the developed method's efficiency in SNR quantification and illustrate the imaging quality evolution in SEM.

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Image noise cross‐correlation for signal‐to‐noise ratio estimation in scanning electron microscope images

Cited by 15 publications

References 5 publications

Performance of new signal‐to‐noise ratio estimation for SEM images based on single image noise cross‐correlation

Performance of new signal‐to‐noise ratio estimation for SEM images based on single image noise cross‐correlation

Noise Variance Estimation Using Image Noise Cross‐Correlation Model on SEM Images

Scanning electron microscope image signal‐to‐noise ratio monitoring for micro‐nanomanipulation

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