Single Image Estimation Techniques for SEM Imaging System - Sim, Kok Swee; Lew, Kai Liang; Tan, Shing Chiang (JOIV : International Journal on Informatics Visualization, 2025)
Estimating a single image's signal-to-noise ratio (SNR) is a critical challenge in Scanning Electron Microscopy (SEM), impacting image quality and analysis reliability. SEM images are essential for revealing structural details at the micro- or nanoscale, but noise often obscures these details, complicating interpretation. Traditional SNR estimation methods required two images to compare and assess the noise levels. SEM images are usually corrupted by noise through several operating conditions, such as dwell time, probe current, and specimen composition. This paper introduces a novel single-image SNR estimation technique, Quarsig SNR Estimation (QSE), for estimating SNR value in SEM images. This method differs from the traditional methods because it only uses a single image to obtain the SNR value without a reference image. This approach involves a single image with Gaussian noise and using the autocorrelation function (ACF) to calculate the peak value for both the original and noisy images. The peak value is the SNR value for the noisy image. QSE has outperformed the existing methods, such as Nearest Neighborhood (NN), Linear Interpolation (LI), and the combination of NN and LI by archiving the nearest SNR value to the reference measurements. This shows that QSE has significant potential for single-image SNR estimation under Gaussian noise. However, its performance under non-Gaussian noise remains a limitation. Despite this, QSE has showcased its reliability in the SEM imaging field by improving the analysis of structural details in noisy imaging conditions.