Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images

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 Wien...

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Vydáno v:	IEEE access Ročník 13; s. 93574 - 93592
Hlavní autoři:	Chee Yong Ong, Dominic, Bukhori, Iksan, Sim, Kok Swee, Beng Gan, Kok
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Electrons Estimation Gaussian process Gaussian process regression (GPR) Gaussian processes Image degradation Image filters Image processing Image quality Interpolation Least squares method Machine learning Noise noise variance estimation Numerical analysis Regression scanning electron microscope (SEM) Scanning electron microscopy Signal to noise ratio signal-to-noise ratio (SNR) SNR estimation support vector machine (SVM) Support vector machines Wiener filtering Wiener filters
ISSN:	2169-3536, 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.
AbstractList	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.
Author	Chee Yong Ong, Dominic Sim, Kok Swee Bukhori, Iksan Beng Gan, Kok
Author_xml	– sequence: 1 givenname: Dominic orcidid: 0009-0002-9373-5657 surname: Chee Yong Ong fullname: Chee Yong Ong, Dominic organization: Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia – sequence: 2 givenname: Iksan orcidid: 0000-0001-8216-5607 surname: Bukhori fullname: Bukhori, Iksan organization: Department of Electrical Engineering, Faculty of Engineering, President University, Bekasi, Indonesia – sequence: 3 givenname: Kok Swee orcidid: 0000-0003-2976-8825 surname: Sim fullname: Sim, Kok Swee email: sksbg2022@gmail.com organization: Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia – sequence: 4 givenname: Kok orcidid: 0000-0002-8776-5502 surname: Beng Gan fullname: Beng Gan, Kok organization: Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
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SubjectTerms	Electrons Estimation Gaussian process Gaussian process regression (GPR) Gaussian processes Image degradation Image filters Image processing Image quality Interpolation Least squares method Machine learning Noise noise variance estimation Numerical analysis Regression scanning electron microscope (SEM) Scanning electron microscopy Signal to noise ratio signal-to-noise ratio (SNR) SNR estimation support vector machine (SVM) Support vector machines Wiener filtering Wiener filters
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Title	Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images
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