A simple method for the acquisition of high-quality digital images from analog scanning electron microscopes

A method is described for converting video signals of analog scanning electron microscopes (SEMs) into digital images of high quality. A plug-in card commercially available for personal computers is used for the on-line analog/digital conversion. A Windows application program written by the authors, together with low-level software drivers supplied with the plug-in card, allow digital images to be recorded, to be displayed simultaneously on the computer monitor and to be saved as a file in a standardized format. Compared to conventional photographic images obtained from the SEM camera system, the digital images possess superior sharpness of outline, excellent image definition, diminished noise and well-defined grey-scale tones. This method provides SEM images of high quality for less than $1000 from most older analog SEMs. In addition, the advantages of digital image processing can be applied to analog SEMs, including contrast enhancement, digital filtering and multichannel recording.     

Mixed lagrange time delay estimation autoregressive Wiener filter application for real-time SEM image enhancement

A new filter is developed for the enhancement of scanning electron microscope (SEM) images. A mixed Lagrange time delay estimation auto-regression (MLTDEAR)-based interpolator is used to provide an estimate of noise variance to a standard Wiener filter. A variety of images are captured and the performance of the filter is shown to surpass the conventional noise filters. As all the information required for processing is extracted from a single image, this method is not constrained by image registration requirements and thus can be applied in real-time in cases where specimen drift is presented in the SEM image.     

基于模糊神经网络的图像滤波算法

针对传统的线性和非线性图像滤波算法对含有丰富细节的图像滤波处理的不足,提出一种基于模糊神经网络的图像滤波算法.该算法利用神经网络的自学习功能训练标准样本以确定模糊贴近度阈值,在基本不改变原有图像的灰度信息的前提下,找出图像受到噪声污染的像素点,采用迭代中值滤波算法得到的中值替换噪声点.仿真实验表明该方法有很好的滤波效果,优于传统的图像滤波算法.     

Collection deficiencies of scanning electron microscopy signal contrasts measured and corrected by differential hysteresis image processing

Limitations of scanning electron microscopy (SEM) image resolution and quality were measured in digital image data and their effect on image contrasts was analyzed and corrected by differential hysteresis (DH) processing. DH processing is a mathematical procedure that utilizes hysteresis properties of intensity variations in the image for a segmentation of differential contrast patterns. These patterns display contrast properties of the data as coherent full-frame images. The contrast segmentation is revertible so that the original image can be restored from the sum of the sequentially extracted DH contrast patterns. DH imaging enhances weak contrast components so that they are more easily recognizable and displays SEM image data free of signal collection efficiency contrasts. Example image data include environmental SEM (ESEM) and SEM images of low and mediumhigh magnifications where collection deficiencies included charging of the specimen surface, obstructions from specimen topography, and uneven signal collection properties of the detector. ESEM low-vacuum image data, which appear to be of high quality, contained local areas of reduced contrasts due to residual surface charging. In such areas, signal contrasts were reduced up to 80%, which suppressed most of the weak short-range contrasts. In low-magnification SEM images, up to 93% of the local high precision contrast was lost from the various adverse effects which diminished the pixel-related contrast resolution of the microscope and resulted in images with low detail. Also, at medium magnification, surface charging effects dramatically reduced the image quality because contrasts resulting from local electron beam/specimen interactions were reduced by as much as 71%. DH imaging restored the local contrast losses by elimination of the collected distorted fraction of signal contrasts and reconstitution of the collected maintained fraction. Restored DH images are of superior quality and enhance the imaging capability of the conventional SEM. DH contrast segmentation provides an improved basis for the measurement of various signal contrast components and detector performances. The DH analysis will ultimately facilitate a precise deduction of specimen properties from extracted contrast patterns.     

A method using an on-line digital computer for the improvement of resolution of backscattered electron images

In principle, the resolution of backscattered electron (BSE) images can be little improved, even though an infinitely small beam size is achieved by various improvements in the intrinsic instrument. In order to circumvent this problem, a method is proposed which utilizes an on-line digital computer for the image recording and processing. The major image-processing tools are reduction, expansion, super-imposition with matching of the images, and high-emphasis filtering in the Fourier domain. By using various combinations of these techniques, the resolution of BSE images has been significantly improved. The validity of these improved images has been confirmed. In the case of a BSE image with too wide a dynamic range, both the present method and digital homomorphic filtering provide successful results.     

Application of the Laplacian filter to high-resolution enhancement of SEM images

Certain digital image-processing methods, which are useful for nonperiodic structural images, have been applied to high-resolution SEM images for the improvement of resolution. Samples utilized in the present study consisted of magnetic tape coated with gold, T4 phage coated with gold-palladium, and uncoated specimens of Prolamellar body (PLB) in Cucurbita moschata. These images were blurred and otherwise disturbed by electronic noise, though the images were taken at the limit of efficiency of intrinsic instrument. The major image-processing tool was the Laplacian filter, which subtracts the Laplacian from the original image. Noise, which is a serious problem in digital processing of high-resolution SEM images, was suppressed by the nonlinear type smoothing method. Also, the noise was evaluated by an autocorrelation function and a power spectrum of the image. By using these methods of “deblurring” and noise removal, we achieved better resolution, and structural details of our biological specimens were revealed.     

Digital image processing for nonperiodic objects and an on-line image processing system for high resolution electron microscopy

A digital image processing method for noise removal and image enhancement in nonperiodic structural images is described. The method for noise removal uses a reversible transform between an image and image autocorrelation function. The Laplacian filter is then employed for image enhancement. Furthermore, an on-line image processing system for highresolution TEM is presented.     

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.     

Image restoration for TV‐scan moving images acquired through a semiconductor backscattered electron detector

A semiconductor backscattered electron (BSE) detector has become popular in scanning electron microscopy session. However, detectors of semiconductor type have a serious disadvantage on the frequency characteristics. As a result, fast scan (e.g. TV‐scan) BSE image should be blurred remarkably. It is the purpose of this study to restore this degradation by using digital image processing technology. In order to improve it practically, we have to settle several problems, such as noise, undesirable processing artifacts, and ease of use. Image processing techniques in an impromptu manner like a conventional mask processing are unhelpful for this study, because a complicated degradation of output signal affects severely the phase response as well as the amplitude response of our SEM system. Hence, based on the characteristics of an SEM signal obtained from the semiconductor BSE detector, a proper inverse filter in Fourier domain is designed successfully. Finally, the inverse filter is converted to a special convolution mask, which is skillfully designed, and applied for TV‐scan moving BSE images. The improved BSE image is very effective in the work for finding important objects. SCANNING 31: 229–235, 2009. © 2010 Wiley Periodicals, Inc.     

光纤端面参数自动化测量系统的研究

为快速、准确、方便地进行光纤端面几何参数的测量,在数字图像采集和处理的基础上,研究和设计了光纤端面参数自动化测量系统。介绍了采用图像处理方式进行光纤端面几何参数自动测量的基本原理。对比了Canny边缘检测法和高通滤波法对光纤图像进行边缘检测的效果,体现出高通滤波在光纤端面图像处理中的优势。针对普通光纤、保偏光纤和光子晶体光纤不同的端面形状,设计出利用图像处理方法自动测量各种光纤端面几何参数的软件系统。结果表明该系统可有效地测量出各类光纤端面的几何参数。     

Reduction in acquisition time of scanning electron microscopy image using complex hysteresis smoothing

Complex hysteresis smoothing (CHS), which was developed for noise removal of scanning electron microscopy (SEM) images some years ago, is utilized in acquisition of an SEM image. When using CHS together, recording time can be reduced without problems by about one-third under the condition of SEM signal with a comparatively high signal-to-noise ratio (SNR). We do not recognize artificiality in a CHS-filtered image, because it has some advantages, that is, no degradation of resolution, only one easily chosen processing parameter (this parameter can be fixed and used in this study), and no processing artifacts. This originates in the fact that its criterion for distinguishing noise depends simply on the amplitude of the SEM signal. The automation of reduction in acquisition time is not difficult, because CHS successfully works for almost all varieties of SEM images with a fairly high SNR.     

基子CT图像快速成型数据的构建方法

本文针对医学CT图像数据,提出了由CT图像构建快速成型数据的建模方法,确定了CT图像处理方案,即通过对断层图像的预处理、滤波处理、数据提取,生成断层图像轮廓,继而通过对轮廓优化和轮廓冗余数据去除,得到用于三维重建的二维轮廓数据,并在此基础上构造出可直接用于快速原型制造的三维STL数字模型.本文所提出的方法能够大大提高快速医学模型的构建速度.     

Proper acquisition and handling of scanning electron microscopy images using a high-performance personal computer

The modern high-performance personal computer (PC) has very recently expanded the range of utilization of digital scanning electron microscopy (SEM) images, and the PC will be used increasingly with SEMs. However, the image quality of digital SEM images may be considerably influenced by scanning and digitization conditions. In particular, the effects of the aliasing error peculiar to digital data are often serious in the low-magnification acquisition (undersampling) of SEM images, and moreover even a high-magnification image (oversampling) is disturbed by the undersampled noise (a sort of aliasing error). Furthermore, the signal-to-noise ratio of a digitized SEM image is closely related to the performance of the analog-to-digital converter. To prevent a flood of low-quality digital images with artifacts by the aliasing and additional noise, we propose a method using very high-density sampling (scanning). In addition, we will discuss how to handle digital SEM images from the point of view of the sampling and quantization.     

Practical method for noise removal in scanning electron microscopy

A new smoothing filter has been developed for noise removal of scanning electron microscopy (SEM) images. We call this the complex hysteresis smoothing (CHS) filter. It is much easier to use for SEM operators than any other conventional smoothing filter, and it rarely produces processing artifacts because it does not utilize a definite mask (which usually has processing parameters of size, shape, weight, and the number of iterations) like a common averaging filter or a complicated filter shape in the Fourier domain. Its criterion for distinguishing noise depends simply on the amplitude of the SEM signal. When applied to several images with different characteristics, it is shown that the present method has a high performance with some original advantages.     

Autoregressive Wiener filtering in a scanning electron microscopy imaging system

In this paper, we propose to use the autoregressive (AR)-based interpolator with Wiener filter and apply the idea to scanning electron microscope (SEM) images. The concept for combining the AR-based interpolator with Wiener filtering comes from the essential requirement of Wiener filtering for accurate and consistent estimation of the power of the noise in images prior to filter implementation. The resultant filter is called AR-Wiener filter. The proposed filter is embedded onto the frame grabber card of the scanning electron microscope (SEM) for real-time image processing. Different images are captured using SEM and used to compare the performances of the conventional Wiener and the proposed AR-Wiener technique.     

Image noise reduction using Wiener filtering with pseudo-inverse

We present a preliminary design and experimental results of a Gaussian noise reduction method for ultrasound images. Our method utilizes a Wiener filtering algorithm with pseudo-inverse technique. The method is capable of solving the Gaussian noise problem in ultrasound image by setup a constant dB of noise function. The key idea of the Wiener filtering algorithm is to process the given ultrasound signal by making the filtering less sensitive to slight changes in input conditions. In this paper, we investigate the possibility of employing this approach for pre-processing ultrasound image application. The application of the proposed method for reducing Gaussian noise is demonstrated by four examples. Meanwhile, we also made the comparisons with median filter, mean filter and adaptive filter; the results reveal that the proposed method has the best noise filtering capability than other three methods. The results also show that the proposed method produces recovery images with quiet high peak-signal-to-noise ratio.     

Digital image acquisition in scanning electron microscopy

Digital image acquisition is of great interest in SEM, as can be proven by the number of ways in which images can be improved by processing with this technique (Desai and Reimer 1985, Hawkes 1981). In the case of the system described here, this interest is enlarged by the facility of storage and retrieval of images. In addition, a digital image becomes the only quantitative result when the “MEBIS” (Microscope Electronique à Balayage In Situ)is used as an automatic control tool whithin an industrial production line (Franceschi and Le Floch 1985). This paper describes a system for acquisition, storage and processing of the images given by “MEBIS”, or other SEM if the scanning system includes a remote command. Software for preliminary processing for local representation is presented. The main advantages of this system are its ease of use and its compactness; it is developed around the familiar and low-cost Apple IIe microcomputer, presented in a portable version including a 5 video screen and a single disk drive in a ruggedized housing.     

Examples of image processing using a computer controlled SEM

Several examples of the use of digital image processing on SEM images are presented. The emphasis is on image enhancement as opposed to pattern recognition. Examples given include non-linear histogram modification, noise filtering, and frame by frame subtraction. Image processing is done on digitally stored images obtained from a modified SEM. The SEM has been modified to allow minicomputer control of the electron/CRT beam position and blanking. Digitization of the sample signals, such as secondary electron, backscattered electron, and absorbed current is done using a 5 MHz voltage to frequency converter and a 100 KHz timer/sealer combination. Software for image storage and manipulation is also described.     

空蚀初生期破坏程度的表征方法

空蚀破坏实验中,传统表征方法不能准确表征空蚀初生期的破坏程度.提出一种蚀坑法和面积法相结合,利用SEM空蚀图像和数字图像处理来表征空蚀初生期破坏程度的方法.采用抽样方法获取空蚀试样表面SEM图像,对图像进行蚀坑轮廓识别、蚀坑填充和分割等处理,最后进行蚀坑信息统计,获得样本总体的单位面积蚀坑数量、蚀坑面积百分、蚀坑平均直径3个参数,从而实现了对试样整体空蚀初生期破坏程度的准确衡量.