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.     

Image signal‐to‐noise ratio estimation using adaptive slope nearest‐neighbourhood model

A new technique based on nearest neighbourhood method is proposed. In this paper, considering the noise as Gaussian additive white noise, new technique single‐image‐based estimator is proposed. The performance of this new technique such as adaptive slope nearest neighbourhood is compared with three of the existing method which are original nearest neighbourhood (simple method), first‐order interpolation method and shape‐preserving piecewise cubic hermite autoregressive moving average. In a few cases involving images with different brightness and edges, this adaptive slope nearest neighbourhood is found to deliver an optimum solution for signal‐to‐noise ratio estimation problems. For different values of noise variance, the adaptive slope nearest neighbourhood has highest accuracy and less percentage estimation error. Being more robust with white noise, the new proposed technique estimator has efficiency that is significantly greater than those of the three methods.     

Nonlinear least squares regression for single image scanning electron microscope signal‐to‐noise ratio estimation

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal‐to‐noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first‐order interpolation and the combination of both nearest neighbourhood and first‐order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods.     

Image signal-to-noise ratio estimation using the autoregressive model

In the last two decades, a variety of techniques for signal-to-noise ratio (SNR) estimation in scanning electron microscope (SEM) images have been proposed. However, these techniques can be divided into two groups: first, SNR estimators of good accuracy, but based on impractical assumptions; second, estimators based on realistic assumptions but of poor accuracy. In this paper we propose the implementation of autoregressive (AR)-model interpolation as a solution to the problem. Unlike others, the proposed technique is based on a single SEM image and offers the required accuracy and robustness in estimating SNR values.     

Image signal-to-noise ratio estimation using Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average model

We propose to cascade the Shape-Preserving Piecewise Cubic Hermite model with the Autoregressive Moving Average (ARMA) interpolator; we call this technique the Shape-Preserving Piecewise Cubic Hermite Autoregressive Moving Average (SP2CHARMA) model. In a few test cases involving different images, this model is found to deliver an optimum solution for signal to noise ratio (SNR) estimation problems under different noise environments. The performance of the proposed estimator is compared with two existing methods: the autoregressive-based and autoregressive moving average estimators. Being more robust with noise, the SP2CHARMA estimator has efficiency that is significantly greater than those of the two methods.     

Image signal-to-noise ratio and noise variance estimation using autoregressive model

During the last three decades, several techniques have been proposed for signal-to-noise ratio (SNR) and noise variance estimation in images, with different degrees of success. Recently, a novel technique based on the statistical autoregressive model (AR) was developed and proposed as a solution to SNR estimation in scanning electron microscope (SEM) image. In this paper, the efficiency of the developed technique with different imaging systems is proven and presented as an optimum solution to image noise variance and SNR estimation problems. Simulation results are carried out with images like Lena, remote sensing, and SEM. The two image parameters, SNR and noise variance, are estimated using different techniques and are compared with the AR-based estimator.     

Performance of signal‐to‐noise ratio estimation for scanning electron microscope using autocorrelation Levinson–Durbin recursion model

A new technique to quantify signal‐to‐noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson–Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first‐order linear interpolation and nearest neighbourhood combined with first‐order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation.     

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. SCANNING 35: 205‐212, 2013. © 2012 Wiley Periodicals, Inc.     

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two‐dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub‐band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub‐band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.     

Robust image signal-to-noise ratio estimation using mixed Lagrange time delay estimation autoregressive model

A new technique based on the statistical autoregressive (AR) model has recently been developed as a solution to signal-to-noise (SNR) estimation in scanning electron microscope (SEM) images. In the present study, we propose to cascade the Lagrange time delay (LTD) estimator with the AR model. We call this technique the mixed Lagrange time delay estimation autoregressive (MLTDEAR) model. In a few test cases involving different images, this model is found to present an optimum solution for SNR estimation problems under different noise environments. In addition, it requires only a small filter order and has no noticeable estimation bias. The performance of the proposed estimator is compared with three existing methods: simple method, first-order linear interpolator, and AR-based estimator over several images. The efficiency of the MLTDEAR estimator, being more robust with noise, is significantly greater than that of 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.     

公共场所异常声源定位中时延估计方法研究

HB(Hassab-Boucher)加权广义互相关(generalized cross correlation based on HB weighted function,GCC-HB)是常用的时延估计方法,在环境为弱高斯噪声情况下,可获得较为精确的时延估计值用于声源定位。通过分析认为,通常公共场所异常声音是一种短时信号,背景噪声主要为粉红噪声与脉冲噪声,符合分数低阶α稳定分布(fractional lower order alpha-stable,FLOA)。在此背景噪声的低信噪比环境下,GCC-HB方法的时延估计性能急剧下降。为此,提出基于反正切变换的改进GCC-HB的时延估计方法(improved GCC-HB method based on arc tangent transform,ATAN-IHB)。该方法首先对加噪信号采用反正切变换抑制噪声中尖峰脉冲的影响,然后结合每帧的信噪比对HB加权函数进行改变,并由多帧HB加权后的峰值确定出时延估计值。理论分析和计算机仿真结果表明,所提出的方法即使在低信噪比的环境下,也可以获得比较满意的时延估计值,具有一定的实用性价值。     

基于Blackman-Harris窗三谱线插值测试ADC信噪比的方法

利用快速傅里叶变换(FFT)测试模数转换器(ADC)的信噪比(SNR)必然存在非相干采样,导致测量结果会受频谱泄漏的影响.针对此问题,提出了一种利用Blackman-Harris窗三谱线插值测试高速ADC SNR的方法.基于MATLAB构建了验证平台,采用美国模拟器件公司(ADI)的AD9627等高速ADC产品的行为级动态模型进行了仿真验证.结果表明,在非相干程度最大的情况下,基于Blackman-Harris窗三谱线插值测试得到的SNR误差低于0.23 dB,达到了ADI公司提供的测试标准,证明该法能很好地抑制频谱泄漏的影响,提高SNR测试精度.     

归一化循环相关超声回波时延估计

传统超声回波时延估计算法是在高斯噪声背景下展开研究的,而实际工况中超声回波不仅含有高斯噪声,还含有脉冲冲击噪声(α稳定分布噪声)等,这导致传统算法失效。为了解决上述问题,本文提出了一种针对混合噪声特别是包含噪声背景下的超声回波时延估计算法:归一化循环相关时延估计算法。首先,对归一化循环相关算法理论进行了简要的介绍。接着,对归一化循环相关时延估计算法进行了理论推导分析。然后,结合仿真分析,在相同α混合噪声情况下对传统循环相关和归一化循环相关时延估计进行比较。最后,在不同信噪比下,对归一化循环相关时延估计算法的估计性能进行了分析。通过对比实验发现,在噪声特征指数趋于1时,循环相关算法已不能估计出时延,而归一化循环相关算法的误差仍能保持在0.4μs;且在-10dB信噪比下,归一化循环相关算法时延估计也能保持在10μs误差范围内。本文所提归一化循环相关算法在混合噪声特别是包含α噪声情况下能够对超声回波时延进行精确估计,具有传统算法所不能比拟的优势。     

CCD的Binning技术在光信号测量中的应用研究

为了降低CCD噪声,提高信噪比,根据噪声的特性,提出用CCD的Binning技术用于噪声的抑制。理论上推导了水平Binning和垂直Binning两种情况下信噪比提高的公式。利用MATLAB语言编程对垂直Binning的几种情况进行仿真,仿真结果表明,Binning可以有效提高信噪比,信噪比提高的理论值和计算值基本相符,对Binning可能带来的电荷饱和也进行了讨论。     

一种星空背景的运动补偿方法

运动背景补偿技术是提高运动星空背景下弱小目标检测精度的关键技术之一。本文提出了一种基于块匹配与质心提取法相结合的星空背景运动补偿技术。该方法在样本运动参数估计阶段直接剔除异常点;为了减少块匹配过程中每个待匹配位置的运算次数,保持块匹配的精度,又引入了连续排除算法,降低了计算复杂度。实验验证了该方法的有效性和优越性,证明了该方法能有效地提高目标的信噪比并且降低差分残余图像灰度均值。     

多子阵高分辨实时波达估计算法研究

常用的高分辨波达估计算法一般都面临计算量巨大的高维协方差矩阵求逆或需要已知目标数目的先验信息等难题,难以在实际中应用。本文从多子阵处理策略出发,结合最小无失真响应算法(MVDR)理论,提出了一种自适应高分辨实时波达估计算法,并给出了详细的理论推导及相应的性能分析。研究表明,本文算法波达估计误差优于频域常规波束形成算法,同时,相较于常用算法实现高分辨波达估计所需的10 dB以上的阵元信噪比门限,本文算法所需的信噪比门限为3.5 dB,更适用于低信噪比条件下目标的探测,并且对舷侧阵阵元信噪比分布不一致性有较强的宽容性。更重要的是,由于协方差矩阵维数的大大降低,使得本文算法的处理速度大大提升,从而使得高分辨的实时波达估计成为可能。仿真及海试数据处理验证了本文算法的可行性。     

基于MAP估计的复小波域局部自适应绝缘子红外热像去噪方法

为了从强白噪声干扰的红外热像中提取真实的绝缘子盘面温度场信息,提出一种基于MAP估计的复小波域局部自适应去噪方法.首次证实了绝缘子红外热像双树复小波变换(DT-CWT)系数服从拉普拉斯分布,并对不同滤波器组采用各自最精细分解层子带系数估计噪声方差,利用待估计点圆形邻域系数估计信号方差,且随分辨率变化调整圆形邻域半径,使得MAP估计的无噪声系数更为准确,提高了去噪图像质量.实验结果表明,该方法比传统的Wiener滤波法、基于离散小波变换和DT-CWT的贝叶斯阈值去噪方法具有更高的信噪比,在有效去除图像噪声的同时,图像细节信息保留更完好.     

一种适合于超精密加工的特殊精密曲线插补算法

针对超精密车床提出一种新型的插补计算方法,将特殊精密曲线的插补功能集成到数控系统内部,并提出双圆弧逼近的误差的通用估计准则和生成程序段数目的估计算法。     

Carrier fringe analysis algorithms for three degree of freedom optical probing

In this work, we present a fiber-delivered and fiber-detected, 3-DOF optical probe concept for measuring optical components to be used in conjunction with an optical coordinate measuring machine (OCMM). The optical probe uses a Michelson interferometer to produce carrier fringes and a high density fiber bundle to transmit interferograms that are recorded away from the probe head in a remote imaging system. We compare several different signal FFT processing techniques (parabolic interpolation, windowing, and zero padding) and a single-bin DFT technique to compute and enhance the resolution of the displacement, tip, and tilt of a moving mirror. We simulated varying signal-to-noise ratios and interference fringe contrast ranges to determine the algorithms’ sensitivity to those parameters and compare our simulated values to measured SNR and fringe values. Based on this work, it should be possible to use a carrier fringe algorithm for fiber probing applications if the interferogram can be transmitted through the fiber bundle with sufficient contrast (40%) and SNR (30 dB).