Review of single image signal-to-noise ratio estimation for SEM image

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

doi:10.1063/1.4803626

Cited by 5 publications

(12 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique requires the perfect alignment of two images which is difficult to achieve. In 2002, Sim proposed a single image to calculate the SNR (Sim, ), using Equation .

SNR = \frac{{\overset{false¯}{r}}_{11} (() 0, 0) - μ_{1}^{2}}{r_{11} (() 0, 0) - {\overset{false¯}{r}}_{11} (() 0, 0)},

where

{\overset{false¯}{r}}_{11} (() 0, 0)

is the estimated noise‐free peak, μ is the image mean and r 11 (0, 0) is the noisy peak.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the estimation technique to predict the

{\overset{false¯}{r}}_{11} (() 0, 0)

is needed. The simplest existing method is the nearest neighborhood where we assume the amplitude of the nearest point r 11 (−1, 0) next to the noisy‐peak is equal to the amplitude of the noise‐free peak (Sim, ). The second method is the first order interpolation (Sim, ).…”

Section: Methodsmentioning

confidence: 99%

“…The simplest existing method is the nearest neighborhood where we assume the amplitude of the nearest point r 11 (−1, 0) next to the noisy‐peak is equal to the amplitude of the noise‐free peak (Sim, ). The second method is the first order interpolation (Sim, ). This takes the difference between the nearest two points r 11 (−1, 0), r 11 (−2, 0) and add them into the nearest neighborhood point r 11 (−1, 0).…”

Section: Methodsmentioning

confidence: 99%

“…The electrons are reflected and detected by a secondary electron (SE) detector and generate a grayscale image. The images captured are SE images, therefore, the main factor that deteriorates the image quality is the presence of corrupting Gaussian noise (Sim, ; Sim et al, 2015a, Sim et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Yeap

Sim

Tso

2018

Microscopy Res & Technique

View full text Add to dashboard Cite

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two‐dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub‐band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub‐band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.

show abstract

“…This technique requires the perfect alignment of two images which is difficult to achieve. In 2002, Sim proposed a single image to calculate the SNR (Sim, ), using Equation .

SNR = \frac{{\overset{false¯}{r}}_{11} (() 0, 0) - μ_{1}^{2}}{r_{11} (() 0, 0) - {\overset{false¯}{r}}_{11} (() 0, 0)},

where

{\overset{false¯}{r}}_{11} (() 0, 0)

is the estimated noise‐free peak, μ is the image mean and r 11 (0, 0) is the noisy peak.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the estimation technique to predict the

{\overset{false¯}{r}}_{11} (() 0, 0)

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Yeap

Sim

Tso

2018

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

“…Besides, the cross‐correlation of two images technique cannot be applied on the existing images such as images stored in the data base. With these limitations in mind, Sim and his co‐workers proposed a technique of using only one image, by using the auto‐correlation function of the image (Sim, ; Sim and Kamel, ; Sim et al ., ). The technique was not constrained by image registration requirement.…”

Section: Introductionmentioning

confidence: 99%

Signal‐to‐noise ratio estimation using adaptive tuning on the piecewise cubic Hermite interpolation model for images

2015

Self Cite

View full text Add to dashboard Cite

An improvement to the existing technique of quantifying signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images using piecewise cubic Hermite interpolation (PCHIP) technique is proposed. The new technique uses an adaptive tuning onto the PCHIP, and is thus named as ATPCHIP. To test its accuracy, 70 images are corrupted with noise and their autocorrelation functions are then plotted. The ATPCHIP technique is applied to estimate the uncorrupted noise-free zero offset point from a corrupted image. Three existing methods, the nearest neighborhood, first order interpolation and original PCHIP, are used to compare with the performance of the proposed ATPCHIP method, with respect to their calculated SNR values. Results show that ATPCHIP is an accurate and reliable method to estimate SNR values from SEM images. SCANNING 38:502-514, 2016. © 2015 Wiley Periodicals, Inc.

show abstract

Autoregressive linear least square single scanning electron microscope image signal‐to‐noise ratio estimation

Sim

NorHisham

2016

Scanning

View full text Add to dashboard Cite

A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc.

show abstract

Review of single image signal-to-noise ratio estimation for SEM image

Cited by 5 publications

References 7 publications

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Signal‐to‐noise ratio estimation using adaptive tuning on the piecewise cubic Hermite interpolation model for images

Autoregressive linear least square single scanning electron microscope image signal‐to‐noise ratio estimation

Contact Info

Product

Resources

About