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

A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.

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“…Rather it can be found by extrapolation from the values

r (- N)

r (+ N)

whereas noise does not seem in the correlation [i.e.

r_{i} (x) - r_{s} (x) \equiv r (x)

, for

x \neq 0

; Sim et al ., ]. In this paper we estimate

r_{s} (0)

from ACF of noisy image by removing the power of noise using a cubic spline interpolation with Savitzky–Golay filters and WLSE smoothing technique.…”

Section: Signal and Noise Illuminationmentioning

confidence: 99%

Signal‐to‐noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error

Kiani

Sim

Nia

et al. 2015

Journal of Microscopy

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“…This technique is simple and easy to implement. It works by reducing the amount of intensity variation between one pixel and the adjacent pixel (Sim et al ., ). However, there are two drawbacks from the average filter.…”

Section: Introductionmentioning

confidence: 97%

Adaptive noise Wiener filter for scanning electron microscope imaging system

2015

Self Cite

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Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments.

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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: 99%

“…In order to generate virtual SEM images, Cizmar et al (2008) have considered that the final image noise is an addition of a Poisson distribution representing primary emission and a Gaussian distribution representing the others types of noise in the SEM (secondary emission and electronics). Sim et al (2004aSim et al ( , 2011Sim et al ( , 2013 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: 99%

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

Cited by 13 publications

References 10 publications

Signal‐to‐noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error

Signal‐to‐noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error

Adaptive noise Wiener filter for scanning electron microscope imaging system

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

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