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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data V"

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Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data 37

Application of pattern spectra and convolutional neural netw...

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37th International Cosmic Ray conference, ICRC 2021

作者： Aschersleben, J. Peletier, R.F. vecchi, M. Wilkinson, M.H.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, v. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, v. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, v. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, v. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, v. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, v. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, v. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O'Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d'Informació Científica Edifici D Carrer de l'Albareda Bellaterrra Cerdanyola del Vallès08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS/IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL Department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM/CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue d

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be the major global instrument for very-high-energy astronomy over the next decade, offering 5 − 10 × better flux sensitivity than current generation gamma-ray telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides images that can be used as training data for convolutional neural networks (CNNs) to determine the energy of the initial gamma rays. Compared to other state-of-the-art algorithms, analyses based on CNNs promise to further enhance the performance to be achieved by CTA. Pattern spectra are commonly used tools for image classification and provide the distributions of the shapes and sizes of various objects comprising an image. The use of relatively shallow CNNs on pattern spectra would automatically select relevant combinations of features within an image, taking advantage of the 2D nature of pattern spectra. In this work, we generate pattern spectra from simulated gamma-ray events instead of using the raw images themselves in order to train our CNN for energy reconstruction. This is different from other relevant learning and feature selection methods that have been tried in the past. Thereby, we aim to obtain a significantly faster and less computationally intensive algorithm, with minimal loss of performance. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Gamma rays

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Application of a microstructural characterization uncertainty quantification framework to Widmanstätten α-laths in additive manufactured Ti-6Al-4v 26

Application of a microstructural characterization uncertaint...

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26th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing conference, SFF 2015

作者： Loughnane, Gregory T. Kuntz, Sarah L. Klingbeil, Nathan Sosa, John M. Irwin, Jeff Nassar, Abdalla R. Reutzel, Edward W. Dept. of Mechanical and Materials Engineering Wright State Univ. DaytonOH45435 United States Center for Electron Microscopy and Analysis Ohio State Univ. ColumbusOH43212 United States Dept. of Mech. and Nuclear Engineering Pennsylvania State Univ. Univ. ParkPA16802 United States Applied Research Laboratory Pennsylvania State Univ. Univ. ParkPA16802 United States

This work applies statistical analysis and uncertainty quantification tools developed for characterizing virtual microstructures in three dimensions to a two-dimensional experimental investigation of Ti-6Al-4v Widmanstätten α-lath thicknesses obtained from back-scattered electron (BSE) or electron back-scatter diffraction (EBSD) images on two thin-walled samples manufactured via the LENS® process. The Materials image Processing and Automated reconstruction (MIPAR™) software optimizes unique recipes for conversion of the BSE or EBSD images to binary data, and subsequently computes the inverse of the linear intercept for each α-lath. Mean α-lath thicknesses and discrete probability density functions (PDFs) of inverse intercepts are used to make quantitative comparisons of α-lath structures at different heights throughout the thin walls. Real-time thermal data collected during the LENS® experiment is then compared to quantitative microstructural results in order to determine trends between α-lath structures, thermal gradients, and melt pool areas across experimental process parameters. © SFF *** rights reserved.

关键词： Aluminum alloys

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Analysis of the Cherenkov Telescope Array first Large-Sized Telescope real data using convolutional neural networks 37

Analysis of the Cherenkov Telescope Array first Large-Sized ...

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37th International Cosmic Ray conference, ICRC 2021

作者： vuillaume, Thomas Jacquemont, Mikaël de Bony de Lavergne, Mathieu Sanchez, David A. Poireau, vincent Maurin, Gilles Benoit, Alexandre Lambert, Patrick Lamanna, Giovanni Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. Deleglise, G. Delgado, C. Delgado Mengual, J. della volpe, D. Depaoli, D. Di Pierro, F. Di venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.v. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, v. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Laboratoire d'Annecy de Physique des Particules Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 LAPP 9 chemin de bellevue Annecy74940 France LISTIC Université Savoie Mont-Blanc Polytech Annecy-Chambéry 5 chemin de bellevue Annecy74940 France Institute for Cosmic Ray Research University of Tokyo Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía CSIC Spain INAF - Osservatorio Astronomico di Roma Italy INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France The Barcelona Institute of Science and Technology Spain IRFU CEA Université Paris-Saclay France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan EMFTEL department IPARCOS Universidad Complutense de Madrid Spain INAF - Osservatorio di Astrofisica e Scienza dello spazio di Bologna Italy Centro Brasileiro de Pesquisas Físicas Brazil INFN Sezione di Padova Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Spain Max-Planck-Institut für Physik Germany Department of Physics TU Dortmund University Germany Saha Institute of Nuclear Physics India INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Switzerland Palacky University Olomouc Faculty of Science Czech Republic CIEMAT Spain Port d'Informació Científica Spain INFN Sezione di Torino Italy Dipartimento di Fisica Universitá degli Studi di Torino Italy INFN Sezione di Bari Università di Bari Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan INFN Sezione di Bari Politecnico di Bari

The Cherenkov Telescope Array (CTA) is the future ground-based gamma-ray observatory and will be composed of two arrays of imaging atmospheric Cherenkov telescopes (IACTs) located in the Northern and Southern hemispheres respectively. The first CTA prototype telescope built on-site, the Large-Sized Telescope (LST-1), is under commissioning in La Palma and has already taken data on numerous known sources. IACTs detect the faint flash of Cherenkov light indirectly produced after a very energetic gamma-ray photon has interacted with the atmosphere and generated an atmospheric shower. reconstruction of the characteristics of the primary photons is usually done using a parameterization up to the third order of the light distribution of the images. In order to go beyond this classical method, new approaches are being developed using state-of-the-art methods based on convolutional neural networks (CNN) to reconstruct the properties of each event (incoming direction, energy and particle type) directly from the telescope images. While promising, these methods are notoriously difficult to apply to real data due to differences (such as different levels of night sky background) between Monte Carlo (MC) data used to train the network and real data. The GammaLearn project, based on these CNN approaches, has already shown an increase in sensitivity on MC simulations for LST-1 as well as a lower energy threshold. This work applies the GammaLearn network to real data acquired by LST-1 and compares the results to the classical approach that uses random forests trained on extracted image parameters. The improvements on the background rejection, event direction, and energy reconstruction are discussed in this contribution. © 2022 Sissa Medialab Srl. All rights reserved.

关键词： Telescopes

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Compression and Completion of Animated Point Clouds using Topological Properties of the Manifold

Compression and Completion of Animated Point Clouds using To...

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International conference on 3D Imaging, Modeling, Processing, visualization and Transmission (3DIMPvT)

作者： Linfei Pan Ľubor Ladický Marc Pollefeys ETH Zurich ETH Zurich/Microsoft

ISBN: (数字)9781728181288

ISBN: (纸本)9781728181295

Recent progress in consumer hardware allowed for the collection of a large amount of animated point cloud data, which is on the one hand highly redundant and on the other hand incomplete. Our goal is to bridge this gap and find a low dimensional representation capable of approximation to a desired precision and completion of missing data. Model-less non-rigid 3D reconstruction algorithms, formulated as a linear factorization of observed point tracks into static shape component and dynamic pose, have been found insufficient to create suitable generative models, capable of generating new unobserved poses. This is due to the non-locality of the linear models, over-fitting to the non-causal correlations present in the data, which manifests in the reconstruction containing rigidly behaving not directly connected parts. In this paper, we propose a new method that can distinguish body parts and factorize the data into shape and pose purely using topological properties of the manifold-local deformations and neighborhoods. To obtain localized factorization, we formulate the deformation distance between two point tracks as the smallest deformation along the path between them. After embedding such distance in low dimensional space, a clustering of embedded data leads to close to rigid components, suitable as initialization for fitting a model-a skinned rigged mesh, used extensively in computer graphics. As both local deformations and neighborhoods of a point are local and can be estimated only from the part of the animation, the method can be used to recover unobserved data in each frame.

关键词： Strain Three-dimensional displays data models Computational modeling Shape Animation image reconstruction

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image Error Concealment using Robust data Hiding Technique

Image Error Concealment using Robust Data Hiding Technique

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International conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Z Ghouse Ahamed v. K. Sharma Anuj Jain Bhagwanth University Ajmer Rajasthan India Lovely Professional University Punjab India

ISBN: (数字)9781728141671

ISBN: (纸本)9781728141688

Some of the packets of the image or image frame may be lost during transmission. Many methods are proposed to deal with this problem. Some of them are retransmission, forward error correction, error resilient, and error concealment. image error concealment is a method of correcting an image at the receiving end by minimizing the error. in the proposed method copy of image is embedded in such a way that the embedded information can be recovered from the image even though if some embedded information is lost due to packet loss. In turn image can be corrected using the resultant embedded information derived from received image.

关键词： Matrix converters image reconstruction conferences Industry applications image edge detection Interpolation Neural networks

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Implicit Functions in Feature Space for 3D Shape reconstruction and Completion

Implicit Functions in Feature Space for 3D Shape Reconstruct...

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conference on Computer vision and Pattern Recognition (CvPR)

作者： Julian Chibane Thiemo Alldieck Gerard Pons-Moll Max Planck Institute for Informatics Saarland Informatics Campus Germany University of Würzburg Germany Computer Graphics Lab TU Braunschweig Germany

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the output of 3D scanners, which are becoming more accessible, and are the intermediate output of 3D computer vision algorithms. Recently, learned implicit functions have shown great promise as they produce continuous reconstructions. However, we identified two limitations in reconstruction from 3D inputs: 1) details present in the input data are not retained, and 2) poor reconstruction of articulated humans. To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans. Our work differs from prior work in two crucial aspects. First, instead of using a single vector to encode a 3D shape, we extract a learnable 3-dimensional multi-scale tensor of deep features, which is aligned with the original Euclidean space embedding the shape. Second, instead of classifying x-y-z point coordinates directly, we classify deep features extracted from the tensor at a continuous query point. We show that this forces our model to make decisions based on global and local shape structure, as opposed to point coordinates, which are arbitrary under Euclidean transformations. Experiments demonstrate that IF-Nets outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions. Code and project website is available at https://***/ifnets/.

关键词： Three-dimensional displays Shape image reconstruction Topology Feature extraction Computer vision Tensile stress

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Direct MRI Segmentation from k-Space data by Iterative Potts Minimization 7th

Direct MRI Segmentation from k-Space Data by Iterative Potts...

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7th International conference on Scale Space and variational Methods in Computer vision (SSvM)

作者： Kiefer, Lukas Petra, Stefania Storath, Martin Weinmann, Andreas Heidelberg Univ Math Imaging Grp Heidelberg Germany Hsch Darmstadt Dept Math & Nat Sci Darmstadt Germany Hsch Wurzburg Schweinfurt Dept Appl Nat Sci & Humanities Wurzburg Germany Helmholtz Zentrum Munchen Inst Computat Biol Munich Germany

ISBN: (纸本)9783030223687;9783030223670

We consider the problem of image segmentation in magnetic resonance imaging (MRI) directly from k-space measurements as opposed to more standard approaches that first reconstruct the image before segmenting it. The model we employ is the piecewise-constant Mumford-Shah model (the Potts model) in connection with the MRI reconstruction problem. The output of our proposed scheme is a piecewise-constant function which yields a segmentation of the image domain. To solve the involved non-convex and non-smooth optimization problem, we adopt an iterative minimization strategy based on surrogate functionals. Numerical experiments illustrate the potential of the approach when applied on undersampled MRI data.

关键词： MRI segmentation Joint reconstruction and segmentation Potts model Piecewise-constant Mumford-Shah model Regularization of inverse problems

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SharinGAN: Combining Synthetic and Real data for Unsupervised Geometry Estimation

SharinGAN: Combining Synthetic and Real Data for Unsupervise...

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conference on Computer vision and Pattern Recognition (CvPR)

作者： P. N. v. R. Koutilya Hao Zhou David Jacobs University of Maryland College Park MD USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.

关键词： Task analysis Estimation Geometry Training Semantics image reconstruction Computer vision

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Semi-Supervised Learning for Low-Dose CT image Restoration with Hierarchical Deep Generative Adversarial Network (HD-GAN) 41

Semi-Supervised Learning for Low-Dose CT Image Restoration w...

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41st Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Choi, Kihwan vania, Malinda Kim, Sungwon Korea Inst Sci & Technol Ctr Bion Seoul 02792 South Korea Yonsei Univ Dept Radiol Coll Med Seoul 03722 South Korea

ISBN: (纸本)9781538613115

In the absence of duplicate high-dose CT data, it is challenging to restore high-quality images based on deep learning with only low-dose CT (LDCT) data. When different reconstruction algorithms and settings are adopted to prepare high-quality images, LDCT datasets for deep learning can be unpaired. To address this problem, we propose hierarchical deep generative adversarial networks (HD-GANs) for semi-supervised learning with the unpaired datasets. We first cluster each patient's CT images into multiple categories, and then collect the images in the same categories across different patients to build an imageset for denoising. Each imageset is fed into a generative adversarial network that consists of a denoising network and a following classification network. The denoising network efficiently reuses feature maps from the lower layers for end-to-end learning with full-size images. The classifier is trained to distinguish between the denoised images and the high-quality images. Evaluated with a clinical LDCT dataset, the proposed semi-supervised learning approach efficiently reduces the noise level of LDCT images without loss of information, thereby addressing the major shortcomings of IR such as computation time and anatomical inaccuracy.

关键词： Computed tomography Noise reduction image reconstruction Training Generative adversarial networks Noise measurement Gallium nitride

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Fast image reconstruction in Ultrasound Transmission Tomography by U-net

Fast Image Reconstruction in Ultrasound Transmission Tomogra...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Hongjian Wang Xueze Qian Hartmut Gemmeke Torsten Hopp Nicole v. Ruiter Jürgen Hesser School of Computer Science and Technology Donghua University Shanghai China Institute for Data Processing and Electronics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Medical Faculty Mannheim Heidelberg University Mannheim Germany

Conventional iterative methods for image reconstruction in ultrasound transmission tomography need to perform many iterations where at each iteration one has to compute the complex forward model of ultrasound wave propagation, and hence they are time-consuming. We use a U-net neural network to accelerate the reconstruction, by training the network to map from an initial reconstruction obtained via a few iterations of L-BFGS method to the target ground truth image. Since the computation of a forward pass of the neural network is very fast, we can expect a significant acceleration using the trained network for image reconstruction. Experiments show that our trained network can replace 40 L-BFGS iterations to generate equivalent reconstructions with slightly better quantitative quality in terms of normalized root mean square error and better visual quality due to the network's denoising effect. It can achieve up to 283× speedup compared with L-BFGS method for reconstructing small-size sound speed images with 80×80 pixels. This implies that we can expect even greater acceleration effects when applying such approach to reconstruct large-size 3D images.

关键词： Training visualization Ultrasonic imaging Three-dimensional displays Propagation Neural networks Noise reduction

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