Performance of signal‐to‐noise ratio estimation for scanning electron microscope using autocorrelation Levinson–Durbin recursion model

Summary A new technique to quantify signal‐to‐noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson–Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. Th...

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Bibliographic Details
Published in:Journal of microscopy (Oxford) 2016-07, Vol.263 (1), p.64-77
Main Authors: SIM, K.S., LIM, M.S., YEAP, Z.X.
Format: Article
Language:English
Subjects:
Autocorrelation function
electron microscope
Levinson‐order update
Noise
peak estimation
scanning electron microscope
SNR
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Summary:Summary A new technique to quantify signal‐to‐noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson–Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first‐order linear interpolation and nearest neighbourhood combined with first‐order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation. Lay description High resolution microscopy images are commonly used for studying anode layers and other components used in fuel cells. The performance of fuel cell materials is largely governed by their transport properties. Fluid, ions and electronic conductivity in particular depend on the microstructure. Understanding and modeling their morphology at the microscopic scale is therefore critical to develop new devices with improved properties. In this study, several methods are employed to model these media, based on three types of anode layers of different origins and aspect. A general methodology is used to represent materials made of three different phases, based on two random 3D sets which are independently chosen. The independent models are computed according to statistical measurement carried out on the experimental images. The best model, which reproduces the correlation function of the experimental images and other statistical features, is shown to model accurately the three types of anode layers investigated. The correlation function of the anode layers is also modeled, which can serves as the basis for generic models of these materials.
ISSN:0022-2720
1365-2818
DOI:10.1111/jmi.12376