Denoising Particle Beam Micrographs with Plug-and-Play Methods

In a particle beam microscope, a raster-scanned focused beam of particles interacts with a sample to generate a secondary electron (SE) signal pixel by pixel. Conventionally formed micrographs are noisy because of limitations on acquisition time and dose. Recent work has shown that estimation methods applicable to a time-resolved measurement paradigm can greatly reduce noise, but these methods apply pixel by pixel without exploiting image structure. The raw SE data can be modeled with a compound Poisson (Neyman Type A) likelihood, which implies data variance that is signal-dependent and greater than the variation in the underlying particle-sample interaction. These statistical properties make methods that assume additive white Gaussian noise ineffective. This paper introduces methods for particle beam micrograph denoising that use the plug-and-play framework to exploit image structure while being applicable to the unusual data likelihoods of this modality. Approximations of the data likelihood that vary in accuracy and computational complexity are combined with denoising by total variation regularization, BM3D, and DnCNN. Methods are provided for both conventional and time-resolved measurements. In simulations representative of helium ion microscopy and scanning electron microscopy, significant improvements in root mean-squared error (RMSE), structural similarity index measure (SSIM), and qualitative appearance are obtained. Average reductions in RMSE are by factors ranging from 2.24 to 4.11.

[1]  Sheila W. Seidel,et al.  Addressing Neon Gas Field Ion Source Instability Through Online Beam Current Estimation , 2022, Microscopy and Microanalysis.

[2]  Charles A. Bouman.  Foundations of Computational Imaging: A Model-Based Approach , 2022 .

[3]  Akshay Agarwal,et al.  Online Beam Current Estimation in Particle Beam Microscopy , 2021, IEEE Transactions on Computational Imaging.

[4]  Janusz Konrad,et al.  Convolutional Neural Network Denoising of Focused Ion Beam Micrographs , 2021, 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP).

[5]  Christopher C. Yu,et al.  Robustness of Time-Resolved Measurement to Unknown and Variable Beam Current in Particle Beam Microscopy , 2021, 2021 IEEE International Conference on Image Processing (ICIP).

[6]  Sheila W. Seidel,et al.  Prevention Beats Removal: Avoiding Stripe Artifacts from Current Variation in Particle Beam Microscopy Through Time-Resolved Sensing , 2021, Microscopy and Microanalysis.

[7]  Vivek K Goyal,et al.  Time-Resolved Focused Ion Beam Microscopy: Modeling, Estimation Methods, and Analyses , 2020, IEEE Transactions on Computational Imaging.

[8]  Brendt Wohlberg,et al.  Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling , 2020, 2020 54th Asilomar Conference on Signals, Systems, and Computers.

[9]  Vivek K Goyal,et al.  Source shot noise mitigation in focused ion beam microscopy by time-resolved measurement. , 2019, Ultramicroscopy.

[10]  Charles A. Bouman,et al.  Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery , 2019, IEEE Signal Processing Magazine.

[11]  J. Audinot,et al.  Imaging and Analytics on the Helium Ion Microscope. , 2019, Annual review of analytical chemistry.

[12]  Xiaohan Chen,et al.  Plug-and-Play Methods Provably Converge with Properly Trained Denoisers , 2019, ICML.

[13]  Charles A. Bouman,et al.  Physics-Based Regularizer for Joint Soft Segmentation and Reconstruction of Electron Microscopy Images of Polycrystalline Microstructures , 2019, IEEE Transactions on Computational Imaging.

[14]  Zhaoying Bian,et al.  Optimizing a Parameterized Plug-and-Play ADMM for Iterative Low-Dose CT Reconstruction , 2019, IEEE Transactions on Medical Imaging.

[15]  Hassan Mansour,et al.  A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems , 2017, IEEE Signal Processing Letters.

[16]  Shunsuke Ono,et al.  Primal-Dual Plug-and-Play Image Restoration , 2017, IEEE Signal Processing Letters.

[17]  Wangmeng Zuo,et al.  Learning Deep CNN Denoiser Prior for Image Restoration , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[19]  Stanley H. Chan,et al.  Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications , 2016, IEEE Transactions on Computational Imaging.

[20]  J. Meyer,et al.  Nanopore fabrication and characterization by helium ion microscopy , 2016, 1805.00292.

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Michael Elad,et al.  Poisson Inverse Problems by the Plug-and-Play scheme , 2015, J. Vis. Commun. Image Represent..

[23]  Avraham Adler,et al.  Lambert-W Function , 2015 .

[24]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[25]  Sreekanth H. Chalasani,et al.  Helium Ion Microscopy (HIM) for the imaging of biological samples at sub-nanometer resolution , 2013, Scientific Reports.

[26]  Brendt Wohlberg,et al.  Plug-and-Play priors for model based reconstruction , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[27]  R. Livengood,et al.  Observation of synchronized atomic motions in the field ion microscope. , 2013, Ultramicroscopy.

[28]  K. Leung,et al.  Electronic Transport in Tin(IV) Oxide Nanocrystalline Films: Two-Medium Transport with Three-Dimensional Variable-Range Hopping Mechanism for the Ultrasmall Nanocrystallite Size Regime , 2012 .

[29]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[30]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  José M. Bioucas-Dias,et al.  Restoration of Poissonian Images Using Alternating Direction Optimization , 2010, IEEE Transactions on Image Processing.

[32]  Richard H. Livengood,et al.  Subsurface damage from helium ions as a function of dose, beam energy, and dose rate , 2009 .

[33]  C. W. Hagen,et al.  On the influence of the sputtering in determining the resolution of a scanning ion microscope , 2009 .

[34]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[35]  B. Griffin,et al.  HELIUM ION MICROSCOPE , 2010 .

[36]  Raymond Hill,et al.  An Introduction to the Helium Ion Microscope. , 2007 .

[37]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[38]  D. Mcmullan.  Scanning electron microscopy 1928–1965† , 2006 .

[39]  Nicholas P. Economou,et al.  Helium ion microscope: A new tool for nanoscale microscopy and metrology , 2006 .

[40]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[41]  M. Utlaut,et al.  Fundamental limits to imaging resolution for focused ion beams , 1996 .

[42]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[43]  Y. Uchikawa,et al.  Electron-count imaging in SEM , 1991 .

[44]  S. K. Katti,et al.  Approximations to the Neyman Type A Distribution for Practical Problems , 1962 .