Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation

K S Sim; S Norhisham

doi:10.1111/jmi.12425

Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation

J Microsc. 2016 Nov;264(2):159-174. doi: 10.1111/jmi.12425. Epub 2016 May 30.

Authors

K S Sim¹, S Norhisham²

Affiliations

¹ Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia. kssim@mmu.edu.my.
² Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.

PMID: 27238911
DOI: 10.1111/jmi.12425

Abstract

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods.

Keywords: Autocorrelation function; Gaussian noise; SNR estimation; image analysis; nonlinear least squares regression; scanning electron microscope.