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.     

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.     

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.     

Single-image signal-to-noise ratio estimation

A method for estimating the signal-to-noise ratio from a single image is presented in this paper. The autocorrelation-based technique requires that image details be correlated over distances of a few pixels, while the noise is assumed to be uncorrelated from pixel to pixel. The latter is shown to be a good approximation in the case of scanning electron microscope (SEM) images provided that the video signal is not band limited. The noise component is derived from the difference between the image autocorrelation at zero offset and an estimate of the corresponding noise-free autocorrelation. Nonlinear effects introduced by intensity saturation and their implications on the image signal-to-noise ratio are also discussed.     

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.     

Using cross‐correlation for automated stitching of two‐dimensional multi‐tile electron backscatter diffraction data

A method for automatically aligning consecutive data sets of large, two‐dimensional multi‐tile electron backscatter diffraction (EBSD) scans with high accuracy was developed. The method involved first locating grain and phase boundaries within search regions containing overlapping data in adjacent scan tiles, and subsequently using cross‐correlation algorithms to determine the relative position of the individual scan tiles which maximizes the fraction of overlapping boundaries. Savitzky‐Golay filtering in two dimensions was used to estimate the background, which was then subtracted from the cross‐correlation to enhance the peak signal in samples with a high density of interfaces. The technique was demonstrated on data sets with a range of interface densities. The equations were implemented as enhancements to a recently published open source code for stitching of multi‐tile EBSD data sets.     

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.     

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.     

兼顾热轧工艺润滑的工作辊磨损预报模型

通过对某1700热连轧厂润滑轧制中轧辊磨损主要影响因素的研究,提出了包含润滑项的工作辊磨损预报模型,并采用模拟退火遗传算法估算模型主要参数。现场运用结果表明,该工作辊磨损模型结构及其模型参数能够兼顾热轧润滑和非润滑工况,提高了工作辊磨损的预报精度,满足在线运用需求。
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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.     

High‐performance serial block‐face SEM of nonconductive biological samples enabled by focal gas injection‐based charge compensation

A longstanding limitation of imaging with serial block‐face scanning electron microscopy is specimen surface charging. This charging is largely due to the difficulties in making biological specimens and the resins in which they are embedded sufficiently conductive. Local accumulation of charge on the specimen surface can result in poor image quality and distortions. Even minor charging can lead to misalignments between sequential images of the block‐face due to image jitter. Typically, variable‐pressure SEM is used to reduce specimen charging, but this results in a significant reduction to spatial resolution, signal‐to‐noise ratio and overall image quality. Here we show the development and application of a simple system that effectively mitigates specimen charging by using focal gas injection of nitrogen over the sample block‐face during imaging. A standard gas injection valve is paired with a precisely positioned but retractable application nozzle, which is mechanically coupled to the reciprocating action of the serial block‐face ultramicrotome. This system enables the application of nitrogen gas precisely over the block‐face during imaging while allowing the specimen chamber to be maintained under high vacuum to maximise achievable SEM image resolution. The action of the ultramicrotome drives the nozzle retraction, automatically moving it away from the specimen area during the cutting cycle of the knife. The device described was added to a Gatan 3View system with minimal modifications, allowing high‐resolution block‐face imaging of even the most charge prone of epoxy‐embedded biological samples.     

Strain mapping accuracy improvement using super‐resolution techniques

Super‐resolution (SR) software‐based techniques aim at generating a final image by combining several noisy frames with lower resolution from the same scene. A comparative study on high‐resolution high‐angle annular dark field images of InAs/GaAs QDs has been carried out in order to evaluate the performance of the SR technique. The obtained SR images present enhanced resolution and higher signal‐to‐noise (SNR) ratio and sharpness regarding the experimental images. In addition, SR is also applied in the field of strain analysis using digital image processing applications such as geometrical phase analysis and peak pairs analysis. The precision of the strain mappings can be improved when SR methodologies are applied to experimental images.     

SLM彩色显微图像的亚像素边缘检测方法

微操作技术的迅速崛起迫切要求显微图像处理技术的发展。在HSI彩色模型的基础上,根据SLM视觉系统的彩色显微图像特点,基于小波变换提出了一种适用于SLM彩色显微图像的边缘检测方法。根据三次样条拟合曲线的一阶导数零点,获取精确的亚像素边缘。试验证明,该方法适用于各放大倍率下的SLM彩色显微图像,能较好地滤除噪声,检测出完整的亚像素边缘。给出了利用该算法处理噪声仿真图像和真实SLM彩色显微图像的试验结果。     

Performance of a gradient‐based shift estimator in a spatially sparse data environment: tracking the sub‐pixel motion of fluorescent particles

Through a series of numerical simulations, we investigate the suitability of a relatively new gradient‐based particle‐tracking algorithm for efficiently quantifying sub‐pixel shifts of fluorescently labelled cells or particles from a sequence of video microscopy images. The algorithm excels at estimating sub‐0.5 pixel per frame shifts in both data‐dense (e.g. laser speckle imaging) and data‐sparse (e.g. fluorescence imaging) applications. No upsampling (i.e. interpolation) is required to achieve the sub‐pixel shift resolution, and thus the approach avoids the complexity and potential errors associated with the interpolation process. An efficient matlab sub‐routine is provided for implementing the algorithm.     

Adaptive-weighted cubic B-spline using lookup tables for fast and efficient axial resampling of 3D confocal microscopy images

Confocal laser scanning microscopy has become a most powerful tool to visualize and analyze the dynamic behavior of cellular molecules. Photobleaching of fluorochromes is a major problem with confocal image acquisition that will lead to intensity attenuation. Photobleaching effect can be reduced by optimizing the collection efficiency of the confocal image by fast z-scanning. However, such images suffer from distortions, particularly in the z dimension, which causes disparities in the x, y, and z directions of the voxels with the original image stacks. As a result, reliable segmentation and feature extraction of these images may be difficult or even impossible. Image interpolation is especially needed for the correction of undersampling artifact in the axial plane of three-dimensional images generated by a confocal microscope to obtain cubic voxels. In this work, we present an adaptive cubic B-spline-based interpolation with the aid of lookup tables by deriving adaptive weights based on local gradients for the sampling nodes in the interpolation formulae. Thus, the proposed method enhances the axial resolution of confocal images by improving the accuracy of the interpolated value simultaneously with great reduction in computational cost. Numerical experimental results confirm the effectiveness of the proposed interpolation approach and demonstrate its superiority both in terms of accuracy and speed compared to other interpolation algorithms.     

Entropy‐assisted image segmentation for nano‐ and micro‐sized networks

Image analysis is an important tool for characterizing nano/micro network structures. To understand the connection, organization and proper alignment of network structures, the knowledge of the segments that represent the materials inside the image is very necessary. Image segmentation is generally carried out using statistical methods. In this study, we developed a simple and reliable masking method that improves the performance of the indicator kriging method by using entropy. This method selectively chooses important pixels in an image (optical or electron microscopy image) depending on the degree of information required to assist the thresholding step. Reasonable threshold values can be obtained by selectively choosing important pixels in a complex network image composed of extremely large numbers of thin and narrow objects. Thus, the overall image segmentation can be improved as the number of disconnected objects in the network is minimized. Moreover, we also proposed a new method for analyzing high‐pixel resolution images on a large scale and optimized the time‐consuming steps such as covariance estimation of low‐pixel resolution image, which is rescaled by performing the affine transformation on high‐pixel resolution images. Herein, image segmentation is executed in the original high‐pixel resolution image. This entropy‐based masking method of low‐pixel resolution significantly decreases the analysis time without sacrificing accuracy.     

Breast histopathology image segmentation using spatio‐colour‐texture based graph partition method

This paper proposes a novel integrated spatio‐colour‐texture based graph partitioning method for segmentation of nuclear arrangement in tubules with a lumen or in solid islands without a lumen from digitized Hematoxylin–Eosin stained breast histology images, in order to automate the process of histology breast image analysis to assist the pathologists. We propose a new similarity based super pixel generation method and integrate it with texton representation to form spatio‐colour‐texture map of Breast Histology Image. Then a new weighted distance based similarity measure is used for generation of graph and final segmentation using normalized cuts method is obtained. The extensive experiments carried shows that the proposed algorithm can segment nuclear arrangement in normal as well as malignant duct in breast histology tissue image. For evaluation of the proposed method the ground‐truth image database of 100 malignant and nonmalignant breast histology images is created with the help of two expert pathologists and the quantitative evaluation of proposed breast histology image segmentation has been performed. It shows that the proposed method outperforms over other methods.     

Sequential processing of quantitative phase images for the study of cell behaviour in real‐time digital holographic microscopy

Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The study of the cell is based on extraction of the dynamic data on cell behaviour from the time‐lapse sequence of the phase images. However, the phase images are affected by the phase aberrations that make the analysis particularly difficult. This is because the phase deformation is prone to change during long‐term experiments. Here, we present a novel algorithm for sequential processing of living cells phase images in a time‐lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least‐squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. All these procedures are performed automatically and applied immediately after obtaining every single phase image. This property of the algorithm is important for real‐time cell quantitative phase imaging and instantaneous control of the course of the experiment by playback of the recorded sequence up to actual time. Such operator's intervention is a forerunner of process automation derived from image analysis. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off‐axis holographic microscope.     

Background intensity correction for terabyte‐sized time‐lapse images

Several computational challenges associated with large‐scale background image correction of terabyte‐sized fluorescent images are discussed and analysed in this paper. Dark current, flat‐field and background correction models are applied over a mosaic of hundreds of spatially overlapping fields of view (FOVs) taken over the course of several days, during which the background diminishes as cell colonies grow. The motivation of our work comes from the need to quantify the dynamics of OCT‐4 gene expression via a fluorescent reporter in human stem cell colonies. Our approach to background correction is formulated as an optimization problem over two image partitioning schemes and four analytical correction models. The optimization objective function is evaluated in terms of (1) the minimum root mean square (RMS) error remaining after image correction, (2) the maximum signal‐to‐noise ratio (SNR) reached after downsampling and (3) the minimum execution time. Based on the analyses with measured dark current noise and flat‐field images, the most optimal GFP background correction is obtained by using a data partition based on forming a set of submosaic images with a polynomial surface background model. The resulting image after correction is characterized by an RMS of about 8, and an SNR value of a 4 × 4 downsampling above 5 by Rose criterion. The new technique generates an image with half RMS value and double SNR value when compared to an approach that assumes constant background throughout the mosaic. We show that the background noise in terabyte‐sized fluorescent image mosaics can be corrected computationally with the optimized triplet (data partition, model, SNR driven downsampling) such that the total RMS value from background noise does not exceed the magnitude of the measured dark current noise. In this case, the dark current noise serves as a benchmark for the lowest noise level that an imaging system can achieve. In comparison to previous work, the past fluorescent image background correction methods have been designed for single FOV and have not been applied to terabyte‐sized images with large mosaic FOVs, low SNR and diminishing access to background information over time as cell colonies span entirely multiple FOVs. The code is available as open‐source from the following link https://isg.nist.gov/ .     

A real‐time image dynamic range compensation for scanning electron microscope imaging system

This paper presents the development and implementation of a real‐time dynamic range compensation system for scanning electron microscope (SEM) imaging applications. Compared with conventional automatic brightness contrast compensators that are based on the average image or pixel intensity level, the proposed system utilizes histogram‐profiling techniques to compensate continuously the dynamic range of the processed video signal. The algorithms are implemented in software with a frame grabber card forming the front‐end video capture element. The proposed technique yields better image compensation compared with conventional methods.