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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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11015481/ |
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| author | Dominic Chee Yong Ong Iksan Bukhori Kok Swee Sim Kok Beng Gan |
| author_facet | Dominic Chee Yong Ong Iksan Bukhori Kok Swee Sim Kok Beng Gan |
| author_sort | Dominic Chee Yong Ong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b72f8e294b764ee2bbffb9e671389336 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b72f8e294b764ee2bbffb9e6713893362025-08-20T02:03:47ZengIEEEIEEE Access2169-35362025-01-0113935749359210.1109/ACCESS.2025.357338911015481Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM ImagesDominic Chee Yong Ong0https://orcid.org/0009-0002-9373-5657Iksan Bukhori1https://orcid.org/0000-0001-8216-5607Kok Swee Sim2https://orcid.org/0000-0003-2976-8825Kok Beng Gan3https://orcid.org/0000-0002-8776-5502Faculty of Engineering and Technology, Multimedia University, Melaka, MalaysiaDepartment of Electrical Engineering, Faculty of Engineering, President University, Bekasi, IndonesiaFaculty of Engineering and Technology, Multimedia University, Melaka, MalaysiaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiaScanning 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.https://ieeexplore.ieee.org/document/11015481/Image processingscanning electron microscope (SEM)noise variance estimationsignal-to-noise ratio (SNR)SNR estimationmachine learning |
| spellingShingle | Dominic Chee Yong Ong Iksan Bukhori Kok Swee Sim Kok Beng Gan Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images IEEE Access Image processing scanning electron microscope (SEM) noise variance estimation signal-to-noise ratio (SNR) SNR estimation machine learning |
| title | Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images |
| title_full | Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images |
| title_fullStr | Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images |
| title_full_unstemmed | Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images |
| title_short | Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images |
| title_sort | adaptive optimizable gaussian process regression linear least squares regression filtering method for sem images |
| topic | Image processing scanning electron microscope (SEM) noise variance estimation signal-to-noise ratio (SNR) SNR estimation machine learning |
| url | https://ieeexplore.ieee.org/document/11015481/ |
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