Citation

Chee Yong Ong, Dominic and Bukhori, Iksan and Sim, Kok Swee and Beng Gan, Kok (2025) Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images. IEEE Access, 13. pp. 93574-93592. ISSN 2169-3536

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Abstract

Scanning Electron Microscopy (SEM) images often suffer from noise contamination, which degrades image quality and affects further analysis. This research presents a complete approach to estimate their Signal-to-Noise Ratio (SNR) and noise variance (NV), and enhance image quality using NV-guided Wiener filter. The main idea of this study is to use a good SNR estimation technique and infuse a machine learning model to estimate NV of the SEM image, which then guides the wiener filter to remove the noise, providing a more robust and accurate SEM image filtering pipeline. First, we investigate five different SNR estimation techniques, namely Nearest Neighbourhood (NN) method, First-Order Linear Interpolation (FOL) method, Nearest Neighbourhood with First-Order Linear Interpolation (NN+FOL) method, Non-Linear Least Squares Regression (NLLSR) method, and Linear Least Squares Regression (LSR) method. It is shown that LSR method to perform better than the rest. Then, Support Vector Machines (SVM) and Gaussian Process Regression (GPR) are tested by pairing it with LSR. In this test, the Optimizable GPR model shows the highest accuracy and it stands as the most effective solution for NV estimation. Combining these results lead to the proposed Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression (AO-GPRLLSR) Filtering pipeline. The AO-GPRLLSR method generated an estimated noise variance which served as input to NV-guided Wiener filter for improving the quality of SEM images. The proposed method is shown to achieve notable success in estimating SNR and NV of SEM images and leads to lower Mean Squared Error (MSE) after the filtering process.

Item Type:	Article
Uncontrolled Keywords:	Image processing, scanning electron microscope (SEM), noise variance estimation, signalto-noise ratio (SNR), SNR estimation, machine learning, Gaussian process regression (GPR), support vector machine (SVM)
Subjects:	Q Science > QC Physics > QC350-467 Optics. Light
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	26 Jun 2025 06:45
Last Modified:	26 Jun 2025 06:45
URII:	http://shdl.mmu.edu.my/id/eprint/14096

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