用自适应高斯过程回归提升SEM图像去噪效果
Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images
- 结合线性最小二乘法与可优化高斯过程回归估算噪声方差
- 在真实SEM图像上实现更低的均方误差（MSE）
- 适合需要高精度图像处理的材料科学与显微成像研究者
扫描电子显微镜（SEM）图像常受噪声干扰，影响图像质量和后续分析。本文提出一种完整的噪声估计与增强方法：先评估信噪比（SNR）和噪声方差（NV），再通过NV引导的维纳滤波器实现图像去噪。研究对比了五种SNR估计算法，发现线性最小二乘回归（LSR）表现最佳；随后将LSR与支持向量机（SVM）和高斯过程回归（GPR）结合，结果显示可优化的GPR模型在噪声方差估计中精度最高。最终构建自适应优化高斯过程回归-线性最小二乘回归（AO-GPRLLSR）滤波流程，生成的噪声方差作为输入用于维纳滤波，显著提升了图像质量，且在过滤后实现了更低的均方误差（MSE）。该方法在真实SEM图像上验证了其有效性。
原文摘要 · Abstract (English)
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.
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