A new and robust parameter estimation technique, named image noise cross-correlation, is proposed to predict the signal-to-noise ratio (SNR) of scanning electron microscope images. The results of SNR and variance estimation values are tested and compared with nearest neighborhood and first-order interpolation. Overall, the proposed method is best as its estimations for the noise-free peak and SNR are most consistent and accurate to within a certain acceptable degree, compared with the others.

Scanning electron microscope (SEM) image signal-to-noise ratio (SNR) depends on the beam current, the materials present in the specimen, and the specimen topography. It is desirable to quantify the SNR in SEM images, as it is a parameter, along with spatial resolution, that quantifies the image quality. SNR measurement usually requires at least two images, to avoid this requirement, a method of SNR estimation with only a single image is described here. The SNR could be quantified as the ratio of signal variance to noise variance. The autocorrelation of image at its peak (zero offset) is used to estimate the noise variance and the signal component in accordance to the corresponding original autocorrelation and mean of the image while assuming the signal and the noise are uncorrelated.

SummaryA new technique based on cubic spline interpolation with Savitzky-Golay noise reduction filtering is designed to estimate signal-to-noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first-order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.

Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments.

A number of techniques have been proposed during the last three decades for noise variance and signal-to-noise ratio (SNR) estimation in digital images. While some methods have shown reliability and accuracy in SNR and noise variance estimations, other methods are dependent on the nature of the images and perform well on a limited number of image types. In this article, we prove the accuracy and the efficiency of the image noise cross-correlation estimation model, vs. other existing estimators, when applied to different types of scanning electron microscope images.