Scanning electron microscope
Edge detection
ElizabethRani2020401 - Edge Detection in Scanning Electron Microscope (SEM) Images using Various Algorithms
Eh, it's less impressive than I thought
@CONFERENCE{Elizabeth Rani2020401,
author = {Elizabeth Rani, G. and Murugeswari, R. and Rajini, N.},
title = {Edge Detection in Scanning Electron Microscope (SEM) Images using Various Algorithms},
year = {2020},
journal = {Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020},
pages = {401 – 405},
doi = {10.1109/ICICCS48265.2020.9121110},
abstract = {In Image Processing Edge detection is important. It is an essential technique to improve image quality in various fields including medical, space, biological materials etc. Images are generally represented by its intensities either in grey scale or in color scale. Scanning Electron microscope providing information about images but during deep analysis, users are not able to analyze the problems such as voids, cracks and fiber pullout. Edge is an important part of the image. The extraction of image should not change any features in extracted images. In our proposed work three algorithms are chosen for edge detection i.e. Sobel, Laplacian and Hough Transform detection algorithm is used to detect the edges and removed noise at the same time in SEM images for both metal and non-metal images. Finally, the comparison was done for these algorithms by using various parameters like the speed of execution, noise removal and accuracy in edge detection. In this paper it is concluded, Laplacian gives better result compared to other detection algorithms in terms of voids, boundary, lines or curves and orientation detection. © 2020 IEEE.},
author_keywords = {edge detection; Laplacian edge detection; metal and non-metal images; scanning electron microscopic; Sobel edge detection},
keywords = {Biological materials; Control systems; Feature extraction; Hough transforms; Intelligent computing; Laplace transforms; Medical imaging; Scanning electron microscopy; Signal detection; Color scale; Detection algorithm; Fiber pull out; Grey scale; Laplacians; Noise removal; Orientation detections; SEM image; Image enhancement},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
isbn = {978-172814876-2},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 14}
}
Noise removal
Quantifying noise
Kockentiedtab2013 - Poisson shot noise parameter estimation from a single scanning electron microscopy image
@CONFERENCE{Kockentiedtab2013,
author = {Kockentiedtab, Stephen and Tönniesa, Klaus and Gierkeb, Erhardt and Dziurowitzb, Nico and Thimb, Carmen and Plitzkob, Sabine},
title = {Poisson shot noise parameter estimation from a single scanning electron microscopy image},
year = {2013},
journal = {Proceedings of SPIE - The International Society for Optical Engineering},
volume = {8655},
doi = {10.1117/12.2008374},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879528733&doi=10.1117%2f12.2008374&partnerID=40&md5=d00112dbab743f2433f6f845ac73f673},
abstract = {Scanning electron microscopy (SEM) has an extremely low signal-to-noise ratio leading to a high level of shot noise which makes further processing difficult. Unlike often assumed, the noise stems from a Poisson process and is not Gaussian but depends on the signal level. A method to estimate the noise parameters of individual images should be found. Using statistical modeling of SEM noise, a robust optimal noise estimation algorithm is derived. A non-local means noise reduction filter tuned with the estimated noise parameters on average achieves an 18% lower root-mean-square error than the untuned filter on simulated images. The algorithm is stable and can adapt to varying noise levels. © 2013 SPIE-ISandT.},
author_keywords = {noise estimation; noise reduction; noise removal; Poisson process; scanning electron microscopy; shot noise; signal-dependent noise; statistical noise model},
keywords = {Image processing; Noise abatement; Poisson distribution; Scanning electron microscopy; Shot noise; Speech enhancement; Noise estimation; Noise removal; Poisson process; Signal dependent noise; Statistical noise; Algorithms},
issn = {1996756X},
isbn = {978-081949428-3},
coden = {PSISD},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 3}
}
Ouali2012110 - Towards noise characterization in SEM imagery
@CONFERENCE{Ouali2012110,
author = {Ouali, Mohammed},
title = {Towards noise characterization in SEM imagery},
year = {2012},
journal = {Proceedings of the IASTED International Conference on Visualization, Imaging and Image Processing, VIIP 2012},
pages = {110 – 115},
doi = {10.2316/P.2012.782-059},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866676323&doi=10.2316%2fP.2012.782-059&partnerID=40&md5=ff7086fea0c6abbd96b7ba95e38379bb},
abstract = { few noise removal methods have been developed for scanning electron microscopy (SEM), and most of the noise removal algorithms come from standard image processing. These algorithms are designed for a certain type of image formation process, often optical. Moreover, these con- ventional algorithms are not always easy to use by SEM op- erators because of their number of parameters (size, shape, weight, and the number of iterations to name a few). Fur- thermore, the setting of these parameters requires a sound understanding of the underlying algorithm. It is then important to devise an adequate noise removal filter for such an image formation process (SEM). However, it is still unclear how noise affects SEM images. In this con- tribution, we tackle the characterization of noise in SEM imagery. A noise identification taxonomy is suggested as well as a method for off-line SNR estimation.},
author_keywords = {Autocorrelation; Noise contamination; Noise model; PSNR; SEM; SNR},
keywords = {Algorithms; Autocorrelation; Image processing; Optical data processing; Scanning electron microscopy; Signal to noise ratio; Visualization; Image formation process; Noise characterization; Noise contamination; Noise identification; Noise models; Noise removal; Noise removal algorithm; Number of iterations; PSNR; SEM image; SNR; SNR estimation; Standard images; Parameter estimation},
isbn = {978-088986926-4},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 0}
}
Adaptive noisy Wiener filter
Sim2016148 - Adaptive noise Wiener filter for scanning electron microscope imaging system
@ARTICLE{Sim2016148,
author = {Sim, K.S. and Teh, V. and Nia, M.E.},
title = {Adaptive noise Wiener filter for scanning electron microscope imaging system},
year = {2016},
journal = {Scanning},
volume = {38},
number = {2},
pages = {148 – 163},
doi = {10.1002/sca.21250},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938262727&doi=10.1002%2fsca.21250&partnerID=40&md5=04ed29413311ea203bfe05d3cf05d345},
abstract = {Summary 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. SCANNING 38:148-163, 2016. © 2015 Wiley Periodicals, Inc.},
author_keywords = {electron microscope; noise; signal-to-noise ratio; Wiener filter},
keywords = {Electron microscopes; Gaussian noise (electronic); Scanning electron microscopy; Signal to noise ratio; Spurious signal noise; Adaptive noise; Average filter; Better performance; Gaussian smoothing filter; Noise; Noise reduction filters; Noise variance; WIENER filters; Article; filter; imaging system; noise reduction; normal distribution; priority journal; scanning electron microscope; signal noise ratio; Median filters},
publisher = {John Wiley and Sons Inc.},
issn = {01610457},
coden = {SCNND},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 19; All Open Access, Bronze Open Access}
}
NL-means filter
Qin20111819 - An automatic additive and multiplicative noise removal scheme with sharpness preservation
@CONFERENCE{Qin20111819,
author = {Qin, Jing and Guo, Weihong},
title = {An automatic additive and multiplicative noise removal scheme with sharpness preservation},
year = {2011},
journal = {Proceedings - International Symposium on Biomedical Imaging},
pages = {1819 – 1822},
doi = {10.1109/ISBI.2011.5872760},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80055037371&doi=10.1109%2fISBI.2011.5872760&partnerID=40&md5=e4e5ec225cebde7418add6000750c7a3},
abstract = {To remove noise from biomedical images polluted by excessive and inhomogeneous additive or multiplicative noise, most of the denoising algorithms cannot keep a desirable balance between denoising and preservation of fine features; only work for one specific noise; and involve heuristic parameter tuning. We present a fully automatic approach to preserve sharp edges and fine details while removing noise. Explained in nonlocal means scheme, we propose a segmentation boosted NL-means filter (SNL) based on the concept of mutual position function to ensure averaging is only taken over pixels in the same phase. To address unreliable segmentation due to excessive noise, we apply SNL filtering in an iterative way. Comparison with ROF, BM3D, K-SVD and the original NL-means on simulated data, MRI and SEM images indicates potentials of our method. © 2011 IEEE.},
author_keywords = {Image denoising; Nonlocal means; segmentation; sharp},
keywords = {Medical imaging; Noise pollution control; Phase noise; Additive and multiplicative noise; Biomedical images; De-noising; De-noising algorithm; Fine Feature; Heuristic parameters; Image de-noising; Multiplicative noise; NL-means; Nonlocal; Nonlocal means; Position functions; REmove noise; SEM image; sharp; Sharp edges; Simulated data; Magnetic resonance imaging},
issn = {19458452},
isbn = {978-142444128-0},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 0}
}
Simple filters
Oho1984331 - Application of the Laplacian filter to highâ€resolution enhancement of SEM images
@ARTICLE{Oho1984331,
author = {Oho, Eisaku and Baba, Norio and Katoh, Masaru and Nagatani, Takashi and Osumi, Masako and Amako, Kazunobu and Kanaya, Koichi},
title = {Application of the Laplacian filter to highâ€resolution enhancement of SEM images},
year = {1984},
journal = {Journal of Electron Microscopy Technique},
volume = {1},
number = {4},
pages = {331 – 340},
doi = {10.1002/jemt.1060010403},
abstract = {Certain digital imageâ€processing methods, which are useful for nonperiodic structural images, have been applied to highâ€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 goldâ€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 imageâ€processing tool was the Laplacian filter, which subtracts the Laplacian from the original image. Noise, which is a serious problem in digital processing of highâ€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. Copyright © 1984 Wileyâ€Liss, Inc.},
author_keywords = {Digital image processing; Highâ€resolution; Laplacin filter; Scanning electron microscopy},
keywords = {computer analysis; diagnosis; electron microscopy; filter; methodology; morphometrics; nonbiological model; nonhuman; scanning electron microscopy},
issn = {07410581},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 22}
}Sim2005219 - The Scanning Electron Microscope (SEM) assessment system using the autoregressive (AR) technique
@CONFERENCE{Sim2005219,
author = {Sim, K.S. and Kamel, N.S. and Chuah, H.T.},
title = {The Scanning Electron Microscope (SEM) assessment system using the autoregressive (AR) technique},
year = {2005},
journal = {Proceedings of the 2005 International Conference on Computer Vision, VISION'05},
pages = {219 – 225},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-60749123426&partnerID=40&md5=31490fbd06ab50ba966968f5efd86e00},
abstract = {In this paper, a novel real-time SNR estimator is proposed. The technique is based on the Autoregressive model and can be embedded onto SEM imaging system for image assessment. The AR based estimator is robust and effective in estimating the noise second order statistics which is required for Wiener filtering process. The AR wiener filter is then embedded onto the frame grabber card to do real-time SEM noise removal. The AR-based method is compared with the Simple method-wiener filter as well as First-order interpolation-wiener filter. In the most testing cases, the AR-wiener filter outperforms the rest of the systems.},
keywords = {Argon; Computer vision; Estimation; Military photography; Optoelectronic devices; Scanning electron microscopy; Assessment systems; Auto-regressive; Auto-regressive models; First orders; Frame grabbers; Image assessments; Noise removals; Scanning electron microscopes; Second-order statistics; Sem; Simple methods; Testing case; Wiener filters; Wiener-filtering; Adaptive filtering},
isbn = {978-193241565-0},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 0}
}
Shetty2021585 - Microscopic image noise reduction using mathematical morphology
@ARTICLE{Shetty2021585,
author = {Shetty, Mangala and Balasubramani, R.},
title = {Microscopic image noise reduction using mathematical morphology},
year = {2021},
journal = {Advances in Intelligent Systems and Computing},
volume = {1166},
pages = {585 – 594},
doi = {10.1007/978-981-15-5148-2_52},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089621117&doi=10.1007%2f978-981-15-5148-2_52&partnerID=40&md5=751c8ca1753f591a64b5256d505dd7a8},
abstract = {In image processing to enhance the region of an image, mathematical morphological (MM) operations are taken an important role. Mainly application of basic morphological techniques are useful in improving the quality of an image. In the collection and delivery process, the image will be polluted by salt-and-pepper noise, which would lead directly to image quality reduction throughout subsequent processes of image analysis. Thus obtaining the actual image from the image that is distorted by noise is therefore of great importance [1]. This paper deals with an approach using morphological functions to reduce the salt-and-pepper noise from scanning electron microscopic(SEM) images of bacteria cell. The noise removal has a wide effect in getting the accurate segmentation and classification of bacteria cells thereby cell identification accuracy increases to identify the bacteria cells within a short period of time automatically, the noise has to remove from the SEM image of bacteria. Various quality assessment operations are used to measure the quality of enhanced images. The results of the experiment indicate that without blurring edges, this experiment can reduce noise effectively from the input image. The validation outcomes of denoised images with a higher peak signal-to-noise ratio (PSNR) and mean squared error (MSE) show their reliable application potential. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.},
author_keywords = {Mathematical morphology; SEM Bacteria; Structuring element},
keywords = {Bacteria; Cells; Cytology; Image enhancement; Image quality; Image segmentation; Mathematical morphology; Mean square error; Morphology; Signal to noise ratio; Cell identification; Mean squared error; Microscopic image; Peak signal to noise ratio; Quality assessment; Quality reduction; Salt-and-pepper noise; Scanning electron microscopic; Image denoising},
editor = {Gupta D. and Khanna A. and Bhattacharyya S. and Hassanien A.E. and Anand S. and Jaiswal A.},
publisher = {Springer},
issn = {21945357},
isbn = {978-981155147-5},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 0}
}
Sparsity
Lazar2015 - Sparsity based noise removal from low dose scanning electron microscopy images
@CONFERENCE{Lazar2015,
author = {Lazar, A. and Fodor, P.S.},
title = {Sparsity based noise removal from low dose scanning electron microscopy images},
year = {2015},
journal = {Proceedings of SPIE - The International Society for Optical Engineering},
volume = {9401},
doi = {10.1117/12.2078438},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926644056&doi=10.1117%2f12.2078438&partnerID=40&md5=1fe13b1acdf8dea61aaa4dde56ab52b2},
abstract = {Scanning electron microscopes are some of the most versatile tools for imaging materials with nanometer resolution. However, images collected at high scan rates to increase throughput and avoid sample damage, suffer from low signalto- noise ratio (SNR) as a result of the Poisson distributed shot noise associated with the electron production and interaction with the surface imaged. The signal is further degraded by additive white Gaussian noise (AWGN) from the detection electronics. In this work, denoising frameworks are applied to this type of images, taking advantage of their sparsity character, along with a methodology for determining the AWGN. A variance stabilization technique is applied to the raw data followed by a patch-based denoising algorithm. Results are presented both for images with known levels of mixed Poisson-Gaussian noise, and for raw images. The quality of the image reconstruction is assessed based both on the PSNR as well as on measures specific to the application of the data collected. These include accurate identification of objects of interest and structural similarity. High-quality results are recovered from noisy observations collected at short dwell times that avoid sample damage. © 2015 SPIE-IS & T.},
author_keywords = {additive white Gaussian noise; image denoising; scanning electron microscope; SEM images; shot noise; sparse reconstruction},
keywords = {Additive noise; Algorithms; Electrons; Gaussian distribution; Gaussian noise (electronic); Image processing; Image reconstruction; Scanning electron microscopy; Shot noise; Signal to noise ratio; Throughput; White noise; Additive White Gaussian noise; Low signal-to-noise ratio; Patch-based denoising; Poisson-Gaussian noise; Scanning electron microscopy image; SEM image; Sparse reconstruction; Variance stabilizations; Image denoising},
editor = {Bouman C.A. and Sauer K.D.},
publisher = {SPIE},
issn = {0277786X},
isbn = {978-162841491-2},
coden = {PSISD},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 6}
}
Complex hysteresis smoothing
Oho2004140 - Reduction in acquisition time of scanning electron microscopy image using complex hysteresis smoothing
@ARTICLE{Oho2004140,
author = {Oho, Eisaku},
title = {Reduction in acquisition time of scanning electron microscopy image using complex hysteresis smoothing},
year = {2004},
journal = {Scanning},
volume = {26},
number = {3},
pages = {140 – 146},
doi = {10.1002/sca.4950260307},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-3142674837&doi=10.1002%2fsca.4950260307&partnerID=40&md5=6f6280ea4dc82c7284fc99ae25732231},
abstract = {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.},
author_keywords = {Digital image processing; Image enhancement; Noise reduction; Scanning electron microscopy; Signal-to-noise ratio},
keywords = {Scanning electron microscopy; Signal to noise ratio; Spurious signal noise; Complex hysteresis smoothing (CHS); Processing parameters; amplitude modulation; article; complex hysteresis smoothing; digital filtering; hysteresis; image analysis; image processing; optical resolution; parameter; priority journal; scanning electron microscopy; signal noise ratio; time; Scanning},
publisher = {Foundation for Advances in Medicine and Science Inc.},
issn = {01610457},
coden = {SCNND},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 7; All Open Access, Bronze Open Access}
}
Oho199650 - Practical method for noise removal in scanning electron microscopy
@ARTICLE{Oho199650,
author = {Oho, Eisaku and Ichise, Norihiko and Martin, William H. and Peters, Klaus-Ruediger},
title = {Practical method for noise removal in scanning electron microscopy},
year = {1996},
journal = {Scanning},
volume = {18},
number = {1},
pages = {50 – 54},
doi = {10.1002/sca.1996.4950180108},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029984616&doi=10.1002%2fsca.1996.4950180108&partnerID=40&md5=6e04a1dadf9c01dd62af82c5bde137bf},
abstract = {A new smoothing filter has been developed for noise removal of scanning electronmicroscopy (SEM) images. We call this the complex hysteresis smoothing (CHS) filter. Itis much easier to use for SEM operators than any other conventional smoothing filter, andit rarely produces processing artifacts because it does not use a definite mask (whichusually has processing parameters of size, shape, weight, and the number of iterations) likea common averaging filter or a complicated filter shape in the Fourier domain. Its criterionfor distinguishing noise depends simply on the amplitude of the SEM signal. When appliedto several images with different characteristics, it is shown that the present method has a highperformance with some original advantages.},
author_keywords = {digital image processing; image enhancement; noise removal; scanning electronmicroscopy},
keywords = {article; artifact reduction; digital filtering; image enhancement; image processing; image quality; imaging system; priority journal; scanning electron microscopy},
publisher = {John Wiley and Sons Inc.},
issn = {01610457},
coden = {SCNND},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 25; All Open Access, Bronze Open Access}
}
Suzuki2013292 - Feature evaluation of complex hysteresis smoothing and its practical applications to noisy SEM images
@ARTICLE{Suzuki2013292,
author = {Suzuki, Kazuhiko and Oho, Eisaku},
title = {Feature evaluation of complex hysteresis smoothing and its practical applications to noisy SEM images},
year = {2013},
journal = {Scanning},
volume = {35},
number = {5},
pages = {292 – 301},
doi = {10.1002/sca.21066},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886092708&doi=10.1002%2fsca.21066&partnerID=40&md5=2fc9656296ff6ba53e861f7323876f7f},
abstract = {Quality of a scanning electron microscopy (SEM) image is strongly influenced by noise. This is a fundamental drawback of the SEM instrument. Complex hysteresis smoothing (CHS) has been previously developed for noise removal of SEM images. This noise removal is performed by monitoring and processing properly the amplitude of the SEM signal. As it stands now, CHS may not be so utilized, though it has several advantages for SEM. For example, the resolution of image processed by CHS is basically equal to that of the original image. In order to find wide application of the CHS method in microscopy, the feature of CHS, which has not been so clarified until now is evaluated correctly. As the application of the result obtained by the feature evaluation, cursor width (CW), which is the sole processing parameter of CHS, is determined more properly using standard deviation of noise Nσ. In addition, disadvantage that CHS cannot remove the noise with excessively large amplitude is improved by a certain postprocessing. CHS is successfully applicable to SEM images with various noise amplitudes. © Wiley Periodicals, Inc.},
author_keywords = {covariance; digital image processing; image quality; noise removal; scanning electron microscope},
keywords = {Algorithms; Animals; Image Processing, Computer-Assisted; Kidney; Microscopy, Electron, Scanning; Rats; Image processing; Image quality; Scanning electron microscopy; Complex hysteresis; covariance; Feature evaluation; Noise amplitude; Noise removal; Processing parameters; Scanning electron microscopy image; Standard deviation; amplitude modulation; article; complex hysteresis smoothing; hysteresis; noise reduction; priority journal; scanning electron microscopy; signal noise ratio; simulation; algorithm; animal; image processing; kidney; procedures; rat; scanning electron microscopy; ultrastructure; Hysteresis},
issn = {19328745},
coden = {SCNND},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 6}
}
Convolutional networks
Chang2022 - Denoising of scanning electron microscope images for biological ultrastructure enhancement
@ARTICLE{Chang2022,
author = {Chang, Sheng and Shen, Lijun and Li, Linlin and Chen, Xi and Han, Hua},
title = {Denoising of scanning electron microscope images for biological ultrastructure enhancement},
year = {2022},
journal = {Journal of Bioinformatics and Computational Biology},
volume = {20},
number = {3},
doi = {10.1142/S021972002250007X},
abstract = {Scanning electron microscopy (SEM) is of great significance for analyzing the ultrastructure. However, due to the requirements of data throughput and electron dose of biological samples in the imaging process, the SEM image of biological samples is often occupied by noise which severely affects the observation of ultrastructure. Therefore, it is necessary to analyze and establish a noise model of SEM and propose an effective denoising algorithm that can preserve the ultrastructure. We first investigated the noise source of SEM images and introduced a signal-related SEM noise model. Then, we validated the effectiveness of the noise model through experiments, which are designed with standard samples to reflect the relation between real signal intensity and noise. Based on the SEM noise model and traditional variance stabilization denoising strategy, we proposed a novel, two-stage denoising method. In the first stage variance stabilization, our VS-Net realizes the separation of signal-dependent noise and signal in the SEM image. In the second stage denoising, our D-Net employs the structure of U-Net and combines the attention mechanism to achieve efficient noise removal. Compared with other existing denoising methods for SEM images, our proposed method is more competitive in objective evaluation and visual effects. © 2022 The Author(s).},
author_keywords = {deep learning; denoising; noise model; SEM; two-stage multi-loss; variance stabilization transformation},
keywords = {Algorithms; Image Processing, Computer-Assisted; Microscopy, Electron, Scanning; Signal-To-Noise Ratio; Software; algorithm; image processing; procedures; scanning electron microscopy; signal noise ratio; software},
publisher = {World Scientific},
issn = {02197200},
coden = {JBCBB},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 1; All Open Access, Hybrid Gold Open Access}
}
Curvelet analysis
Shirazi2014 - Curvelet based offline analysis of SEM images
@ARTICLE{Shirazi2014,
author = {Shirazi, Syed Hamad and Ul Haq, Nuhman and Hayat, Khizar and Naz, Saeeda and Ul Haque, Ihsan},
title = {Curvelet based offline analysis of SEM images},
year = {2014},
journal = {PLoS ONE},
volume = {9},
number = {8},
doi = {10.1371/journal.pone.0103942},
abstract = {Manual offline analysis, of a scanning electron microscopy (SEM) image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM). The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD) calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm. © 2014 Shirazi et al.},
keywords = {Algorithms; Entropy; Fractals; Humans; Image Enhancement; Imaging, Three-Dimensional; Microscopy, Electron, Scanning; Pattern Recognition, Automated; Software; algorithm; article; automation; controlled study; curvelet transform; fractal analysis; image analysis; image enhancement; image processing; image quality; intermethod comparison; mathematical analysis; measurement accuracy; noise reduction; perimeter; quantitative analysis; scanning electron microscopy; surface property; algorithm; automated pattern recognition; computer program; entropy; human; procedures; scanning electron microscopy; statistics and numerical data; three dimensional imaging},
publisher = {Public Library of Science},
issn = {19326203},
coden = {POLNC},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 12; All Open Access, Gold Open Access}
}
Roughness measurement
Katz2006 - Bias reduction in roughness measurement through SEM noise removal
@CONFERENCE{Katz2006,
author = {Katz, R. and Chase, C.D. and Kris, R. and Peltinov and Villarrubia, J. and Bunday, B.},
title = {Bias reduction in roughness measurement through SEM noise removal},
year = {2006},
journal = {Proceedings of SPIE - The International Society for Optical Engineering},
volume = {6152 II},
doi = {10.1117/12.661135},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745612054&doi=10.1117%2f12.661135&partnerID=40&md5=57597a1785f8b6048b36f434a16babdd},
abstract = {The importance of Critical Dimension (CD) roughness metrics such as Line and Contact edge roughness (LER, CER) and their associated width metrics (LWR, CWR) have been dealt with widely in the literature and are becoming semiconductor industry standards. With the downscaling of semiconductor fabrication technology, the accuracy of these metrics is of increasing importance. One important challenge is to separate the image noise (present in any SEM image) from the physically present roughness. An approach for the removal of the non-systematic image noise was proposed by J.Villarrubia and B.Bunday [Proc. SPIE 5752, 480 (2005)]. In the presented work this approach is tested and extended to deal with the challenge of noise removal in the presence of various types of systematic phenomena present in the imaging process such as CD variation. The study was carried out by means of simulated LWR and using real measurements.},
author_keywords = {Line edge roughness (LER); Linewidth roughness (LWR); Noise removal; Roughness Measurements; SEM Metrology; Unbiased Estimation},
keywords = {Computer simulation; Imaging techniques; Noise abatement; Scanning electron microscopy; Semiconductor devices; Standards; Line edge roughness (LER); Linewidth roughness (LWR); Noise removal; Roughness Measurements; SEM Metrology; Unbiased Estimation; Roughness measurement},
issn = {0277786X},
isbn = {0819461954; 978-081946195-7},
coden = {PSISD},
type = {Conference paper},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 19}
}
To be sorted
Oho199877 - Digital Image Processing Technology for Scanning Electron Microscopy
@ARTICLE{Oho199877,
author = {Oho, Eisaku},
title = {Digital Image Processing Technology for Scanning Electron Microscopy},
year = {1998},
journal = {Advances in Imaging and Electron Physics},
volume = {105},
number = {C},
pages = {77–112,112a,112b,112c,112d,113–140},
doi = {10.1016/S1076-5670(08)70176-9},
abstract = {Digital image processing technology for scanning electron microscopy (SEM) is discussed. The validity of structures in digitized images can be confirmed by observing an identical image recorded at a much higher magnification. SEM images taken with underscanning are contaminated by the aliasing error, while SEM noise with all frequency components is always undersampled. The common lowpass filter is equivalent to the bilinear interpolation in the space domain, and has little effect in emphasizing the noise while maintaining high fidelity for expansion of the oversampled signal. Based on the principle of hysteresis smoothing, optimal cursor width (CW) can be found, while provides an insight of noise removal. Image enhancement limitation often occurs in contrast enhancement by histogram equalization of the reduced image including the brightness information.},
publisher = {Academic Press Inc.},
issn = {10765670},
coden = {AOEMA},
type = {Article},
publication_stage = {Final},
source = {Scopus},
note = {Cited by: 4}
}