A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc.

中文翻译：

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自回归线性最小二乘单次扫描电子显微镜图像信噪比估计

应用基于线性最小二乘回归 (LSR) 模型的技术来估计扫描电子显微镜 (SEM) 图像的信噪比 (SNR)。为了测试这种技术对 SNR 估计的准确性，许多 SEM 图像最初被白噪声破坏。形成原始和损坏的 SEM 图像的自相关函数 (ACF) 作为参考点来估计损坏图像的 SNR 值。然后将 LSR 技术与前三种已知的最近邻域、一阶插值以及最近邻域和一阶插值相结合的现有技术进行比较。然后计算所有这些技术的实际和估计 SNR 值以进行比较。结果表明，与其他三种现有技术相比，LSR 技术能够获得最高的精度，因为实际 SNR 值和估计 SNR 值之间的绝对差异相对较小。扫描 38:771-782, 2016. © 2016 Wiley Periodicals, Inc.