Single Image Estimation Techniques for SEM Imaging System

Kai Liang Lew - Multimedia University, 75450 Malacca, Malaysia
Kok Swee Sim - Multimedia University, 75450 Malacca, Malaysia
Shing Chiang Tan - Multimedia University, 75450 Malacca, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.9.1.3505

Abstract


Estimating a single image's signal-to-noise ratio (SNR) is a critical challenge in Scanning Electron Microscopy (SEM), impacting image quality and analysis reliability. SEM images are essential for revealing structural details at the micro- or nanoscale, but noise often obscures these details, complicating interpretation. Traditional SNR estimation methods required two images to compare and assess the noise levels. SEM images are usually corrupted by noise through several operating conditions, such as dwell time, probe current, and specimen composition. This paper introduces a novel single-image SNR estimation technique, Quarsig SNR Estimation (QSE), for estimating SNR value in SEM images. This method differs from the traditional methods because it only uses a single image to obtain the SNR value without a reference image. This approach involves a single image with Gaussian noise and using the autocorrelation function (ACF) to calculate the peak value for both the original and noisy images. The peak value is the SNR value for the noisy image. QSE has outperformed the existing methods, such as Nearest Neighborhood (NN), Linear Interpolation (LI), and the combination of NN and LI by archiving the nearest SNR value to the reference measurements. This shows that QSE has significant potential for single-image SNR estimation under Gaussian noise. However, its performance under non-Gaussian noise remains a limitation. Despite this, QSE has showcased its reliability in the SEM imaging field by improving the analysis of structural details in noisy imaging conditions.


Keywords


Scanning Electron Microscopy (SEM); Autocorrelation Function (ACF); Signal-to-Noise Ratio (SNR); Nearest Neighbourhood (NN); Linear Interpolation (LI)

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References


D. Roldán, C. Redenbach, K. Schladitz, C. Kübel, and S. Schlabach, “Image quality evaluation for FIB-SEM images,” J. Microsc., vol. 293, no. 2, pp. 98–117, 2024, doi: 10.1111/jmi.13254.

M. S. Prasad and D. C. Joy, “Is SEM noise Gaussian?,” Microsc. Microanal., vol. 9, no. SUPPL. 2, pp. 982–983, 2003, doi:10.1017/s1431927603444917.

D. C. Joy, “Noise and Its Effects on the Low-Voltage SEM,” Biol. Low-Voltage Scanning Electron Microsc., pp. 129–144, 2007, doi:10.1007/978-0-387-72972-5_4.

F. Timischl, M. Date, and S. Nemoto, “A statistical model of signal–noise in scanning electron microscopy,” Scanning, vol. 34, no. 3, pp. 137–144, May 2012, doi: https://doi.org/10.1002/sca.20282.

A. E. Ilesanmi and T. O. Ilesanmi, “Methods for image denoising using convolutional neural network: a review,” Complex Intell. Syst., vol. 7, no. 5, pp. 2179–2198, 2021, doi: 10.1007/s40747-021-00428-4.

J. T. L. Thong, K. S. Sim, and J. C. H. Phang, “Single-Image Signal-to-Noise Ratio Estimation,” Scanning, vol. 23, pp. 328–336, 2001, doi:10.1002/sca.4950230506.

D. R. I. M. Setiadi, “PSNR vs SSIM: imperceptibility quality assessment for image steganography,” Multimed. Tools Appl., vol. 80, no. 6, pp. 8423–8444, 2021, doi: 10.1007/s11042-020-10035-z.

B. T. Bosworth, W. R. Bernecky, J. D. Nickila, B. Adal, and G. C. Carter, “Estimating Signal-to-Noise Ratio (SNR),” IEEE J. Ocean. Eng., vol. 33, no. 4, pp. 414–418, 2008, doi:10.1109/JOE.2008.2001780.

M. Nadipally, “Chapter 2 - Optimization of Methods for Image-Texture Segmentation Using Ant Colony Optimization,” in Intelligent Data-Centric Systems, D. J. Hemanth, D. Gupta, and V. B. T.-I. D. A. for B. A. Emilia Balas, Eds., Academic Press, 2019, pp. 21–47. doi:10.1016/B978-0-12-815553-0.00002-1.

S. Yu, G. Dai, Z. Wang, L. Li, X. Wei, and Y. Xie, “A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images,” BMC Med. Imaging, vol. 18, no. 1, pp. 1–9, 2018, doi: 10.1186/s12880-018-0256-6.

E. Palovcak, D. Asarnow, M. G. Campbell, Z. Yu, and Y. Cheng, “Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks,” IUCrJ, vol. 7, pp. 1142–1150, 2020, doi: 10.1107/S2052252520013184.

C. Eichner et al., “Increased sensitivity and signal-to-noise ratio in diffusion-weighted MRI using multi-echo acquisitions,” Neuroimage, vol. 221, no. July, 2020, doi: 10.1016/j.neuroimage.2020.117172.

A. Gnanasambandam and S. H. Chan, “Exposure-Referred Signal-To-Noise Ratio for Digital Image Sensors,” IEEE Trans. Comput. Imaging, vol. 8, pp. 561–575, 2022, doi: 10.1109/TCI.2022.3187657.

Q. Zhang, C. Liu, and G. He, “An Improved Wiener Filter Based on Adaptive SNR MRI Image Denoising Algorithm,” Proc. - 2022 Int. Conf. Comput. Commun. Percept. Quantum Technol. CCPQT 2022, pp. 164–168, 2022, doi: 10.1109/CCPQT56151.2022.00036.

K. L. Lew, C. Y. Kew, K. S. Sim, and S. C. Tan, “Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance,” J. Informatics Web Eng., vol. 3, no. 1, pp. 169–181, 2024, doi:10.33093/jiwe.2024.3.1.11.

W. T. Chan, “Conditional Noise Filter for MRI Images with Revised Theory on Second-order Histograms,” Int. J. Robot. Autom. Sci., vol. 3, pp. 25–32, 2021, doi:10.33093/ijoras.2021.3.5.

W. T. Chan, “Noise Estimation for MRI Images with Revised Theory on Histograms of Second-order Derivatives,” Int. J. Robot. Autom. Sci., vol. 5, no. 1, pp. 6–12, 2023.

K. S. Sim and N. S. Kamel, “Image signal-to-noise ratio estimation using the autoregressive model,” Scanning, vol. 26, no. 3, pp. 135–139, 2004, doi: 10.1002/sca.4950260306.

K. S. Sim, M. A. Lai, C. P. Tso, and C. C. Teo, “Single image signal-to-noise ratio estimation for magnetic resonance images,” J. Med. Syst., vol. 35, no. 1, pp. 39–48, 2011, doi: 10.1007/s10916-009-9339-9.

K. S. Sim, M. Y. Wee, and W. K. Lim, “Image signal-to-noise ratio estimation using shape-preserving piecewise cubic hermite autoregressive moving average model,” Microsc. Res. Tech., vol. 71, no. 10, pp. 710–720, 2008, doi: 10.1002/jemt.20610.

K. S. Sim, Z. X. Yeap, F. F. Ting, and C. P. Tso, “The performance of adaptive tuning piecewise cubic hermite interpolation model for signal-to-noise ratio estimation,” Int. J. Innov. Comput. Inf. Control, vol. 14, no. 5, pp. 1787–1804, 2018, doi: 10.24507/ijicic.14.05.1787.

A. B. Yesilyurt, A. Erol, F. Kamisli, and A. A. Alatan, “Single Image Noise Level Estimation Using Dark Channel Prior,” 2019 IEEE Int. Conf. Image Process., pp. 2065–2069, 2019, doi:10.1109/ICIP.2019.8803150.

W. Tian, Q. Zhao, Z. Kan, X. Long, H. Liu, and J. Cheng, “A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral Images Based on Pure Pixel Extraction,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, pp. 399–408, 2023, doi:10.1109/JSTARS.2022.3225964.

D. C. Y. Ong and K. S. Sim, “Single Image Signal-to-Noise Ratio (SNR) Estimation Techniques for Scanning Electron Microscope - A Review,” IEEE Access, vol. 12, no. September, pp. 155747–155772, 2024, doi: 10.1109/access.2024.3482118.

B. Sharma, “sem-images_299x299,” Kaggle. [Online]. Available: https://www.kaggle.com/datasets/sharumaan/semimages-299x299?resource=download

K. S. Sim, H. T. Chuah, and C. Zheng, “Performance of a mixed Lagrange time delay estimation autoregressive (MLTDEAR) model for single-image signal- to-noise ratio estimation in scanning electron microscopy,” J. Microsc., vol. 219, no. 1, pp. 1–17, Jul. 2005, doi:10.1111/j.1365-2818.2005.01488.x.

Z. X. Yeap, K. S. Sim, and C. P. Tso, “Signal-to-noise ratio estimation technique for SEM image using linear regression,” Proc. 2016 Int. Conf. Robot. Autom. Sci. ICORAS 2016, pp. 1–5, 2017, doi:10.1109/ICORAS.2016.7872602.

M. A. Kiani, K. S. Sim, M. E. Nia, and C. P. Tso, “Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky–Golay filters and weighted least squares error,” J. Microsc., vol. 258, no. 2, pp. 140–150, May 2015, doi:10.1111/jmi.12227.

Z. X. Yeap, K. S. Sim, and C. P. Tso, “Signal-to-noise ratio estimation technique for SEM image using B-spline,” Proc. 2016 Int. Conf. Robot. Autom. Sci. ICORAS 2016, no. 3, pp. 1–5, 2017, doi:10.1109/icoras.2016.7872617.

J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-Excitation Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 8, pp. 2011–2023, 2020, doi: 10.1109/TPAMI.2019.2913372.

Z. X. Yeap, K. S. Sim, and C. P. Tso, “Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images,” Microsc. Res. Tech., vol. 82, no. 4, pp. 402–414, Apr. 2019, doi: 10.1002/jemt.23181.

K. S. Sim and S. NorHisham, “Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation.,” Scanning, vol. 38, no. 6, pp. 771–782, Nov. 2016, doi:10.1002/sca.21327.

K. S. Sim and S. Norhisham, “Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation,” J. Microsc., vol. 264, no. 2, pp. 159–174, 2016, doi:10.1111/jmi.12425.

K. S. Sim, M. S. Lim, and Z. X. Yeap, “Performance of signal-to-noise ratio estimation for scanning electron microscope using autocorrelation Levinson-Durbin recursion model.,” J. Microsc., vol. 263, no. 1, pp. 64–77, Jul. 2016, doi: 10.1111/jmi.12376.

L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.

S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, “1D convolutional neural networks and applications: A survey,” Mech. Syst. Signal Process., vol. 151, pp. 1–20, 2021, doi:10.1016/j.ymssp.2020.107398.

H. Kirmizitas and N. Besli, “Image and Texture Independent Deep Learning Noise Estimation Using Multiple Frames,” Elektron. ir Elektrotechnika, vol. 28, no. 6, pp. 42–47, 2022, doi:10.5755/j02.eie.30586.

X. Yang, K. Xu, S. Xu, and P. X. Liu, “Image Noise Level Estimation for Rice Noise Based on Extended ELM Neural Network Training Algorithm,” IEEE Access, vol. 7, pp. 1943–1951, 2019, doi:10.1109/access.2018.2886294.

N. R. Huber, J. Kim, S. Leng, C. H. McCollough, and L. Yu, “Deep Learning–Based Image Noise Quantification Framework for Computed Tomography,” J. Comput. Assist. Tomogr., vol. 47, no. 4, 2023, doi: 10.1097/RCT.0000000000001469.

L. Deng, B. Zhou, J. Ying, and R. Zhao, “A Noise Estimation Method for Hyperspectral Image Based on Stacked Autoencoder,” IEEE Access, vol. 11, pp. 89835–89843, 2023, doi:10.1109/access.2023.3307200.

K.S. Sim, “Single Image SEM Signal-to-Noise ratio Estimation using adaptive piecewise cubic Hermite interpolation(ATPCHIP),” PI 2016702293, 2016

K. S. Sim, “Signal-To-Noise Ratio Estimation Of Sem Images Using Single?Image Approach And Adaptive Tuning On Piecewise Hermite Interpolation,” MY-195563-A, 2023