Displaying publications 21 - 23 of 23 in total

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  1. Fulltext In Situ SEM Nanomanipulation and Nanomechanical/Electrical Characterization
    Lu Y, Shen Y, Liu X, Ahmad MRB, Chen Y
    Scanning, 2017;2017:8016571.
    PMID: 29238439 DOI: 10.1155/2017/8016571
  2. Signal-to-noise ratio estimation using adaptive tuning on the piecewise cubic Hermite interpolation model for images
    Sim KS, Yeap ZX, Tso CP
    Scanning, 2016 Nov;38(6):502-514.
    PMID: 26618491 DOI: 10.1002/sca.21286
    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.
  3. Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation
    Sim KS, NorHisham S
    Scanning, 2016 Nov;38(6):771-782.
    PMID: 27253888 DOI: 10.1002/sca.21327
    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.
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