Autoregressive linear least square single scanning electron microscope image signal‐to‐noise ratio estimation. Issue 6 (2nd June 2016)

• Record Type: Journal Article
• Authors: Sim, Kok Swee; NorHisham, Syafiq
• Is Part Of: Scanning. Volume 38:Issue 6(2016:Nov./Dec.)
• Page Start: 771
• Page End: 782
• Publication Date: 2016-06-02
• Subjects: SNR estimation -- least squares regression -- autocorrelation function -- scanning electron microscope -- image analysis; Scanning electron microscopy -- Periodicals; 502.825
• DOI: 10.1002/sca.21327
• Languages: English
• ISSNs: 0161-0457

Abstract

Summary: 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.