K. S. Sim

Performance of a mixed Lagrange time delay estimation autoregressive (MLTDEAR) model for single-image signal- to-noise ratio estimation in scanning electron microscopy

A novel technique based on the statistical autoregressive (AR) model has recently been developed as a solution to estimate the signal‐to‐noise ratio (SNR) in scanning electron microscope (SEM) images. In another research study, the authors also developed an algorithm by cascading the AR model with the Lagrange time delay (LTD) estimator. This technique is named the mixed Lagrange time delay estimation autoregressive (MLTDEAR) model. In this paper, the fundamental performance limits for the problem of single‐image SNR estimation as derived from the Cramer–Rao inequality is presented. We compared the experimental performances of several existing methods – the simple method, the first‐order linear interpolator, the AR‐based estimator as well as the MLTDEAR method – with respect to this performance bound. In a few test cases involving different images, the efficiency of the MLTDEAR single‐image estimation technique proved to be significantly better than that of the other three methods. Study of the effect of different SEM setting conditions that affect the autocorrelation function curve is also discussed.

Image noise cross-correlation for signal-to-noise ratio estimation in scanning electron microscope images.

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.

New technique for in-situ measurement of backscattered and secondary electron yields for the calculation of signal-to-noise ratio in a SEM

The quality of an image generated by a scanning electron microscope is dependent on secondary emission, which is a strong function of surface condition. Thus, empirical formulae and available databases are unable to take into account actual metrology conditions. This paper introduces a simple and reliable measurement technique to measure secondary electron yield (δ) and backscattered electron yield (η) that is suitable for in‐situ measurements on a specimen immediately prior to imaging. The reliability of this technique is validated on a number of homogenous surfaces. The measured electron yields are shown to be within the range of published data and the calculated signal‐to‐noise ratio compares favourably with that estimated from the image.

Single Image Signal-to-Noise Ratio Estimation for Magnetic Resonance Images

A novel technique to quantify the signal-to-noise ratio (SNR) of magnetic resonance images is developed. The image SNR is quantified by estimating the amplitude of the signal spectrum using the autocorrelation function of just one single magnetic resonance image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. It is shown that the technique can be implemented in a highly efficient way for the magnetic resonance imaging system.

Contrast enhancement dynamic histogram equalization for medical image processing application

Image processing requires an excellent image contrast‐enhancement technique to extract useful information invisible to the human or machine vision. Because of the histogram flattening, the widely used conventional histogram equalization image‐enhancing technique suffers from severe brightness changes, rendering it undesirable. Hence, we introduce a contrast‐enhancement dynamic histogram‐equalization algorithm method that generates better output image by preserving the input mean brightness without introducing the unfavorable side effects of checkerboard effect, artefacts, and washed‐out appearance. The first procedure of this technique is; normalizing input histogram and followed by smoothing process. Then, the break point detection process is done to divide the histogram into subhistograms before we can remap the gray level allocation. Lastly, the transformation function of each subhistogram is constructed independently.

Signal-to-noise ratio estimation on SEM images using cubic spline interpolation with Savitzky–Golay smoothing

A 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.

Enhancement of optical images using hybrid edge detection technique

Edge structures which are boundaries of object surfaces are essential image characteristic in computer vision and image processing. As a result, edge detection becomes part of the core feature extraction in many object recognition and digital image applications. This paper presents a new hybrid edge detector that combines the advantages of Prewitt, Sobel and optimized Canny edge detectors to perform edge detection while eliminating their limitations. The optimum Canny edges are obtained through varying the Gaussian filter standard deviation and the threshold value. Simulation results show that the proposed hybrid edge detection method is able to consistently and effectively produce better edge features even in noisy images. Compared to the other three edge detection techniques, the hybrid edge detector has demonstrated its superiority by returning specific edges with less noise.

Noise Variance Estimation Using Image Noise Cross-Correlation Model on SEM Images

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.

Performance of new signal-to-noise ratio estimation for SEM images based on single image noise cross-correlation.

A new technique for estimation of signal‐to‐noise ratio in scanning electron microscope images is reported. The method is based on the image noise cross‐correlation estimation model recently developed. We derive the basic performance limits on a single image signal‐to‐noise ratio estimation using the Cramer–Rao inequality. The results are compared with those from existing estimation methods including the nearest neighbourhood (the simple method), the first order linear interpolator, and the autoregressive based estimator. The comparisons were made using several tests involving different images within the performance bounds. From the results obtained, the efficiency and accuracy of image noise cross‐correlation estimation technique is considerably better than the other three methods.

Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error.

A new technique based on cubic spline interpolation with Savitzky–Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real‐time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky–Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal‐to‐noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation‐based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real‐time SEM images, without generating corruption or increasing scanning time.