Michael Zibulevsky

发表

A Multiscale Framework For Blind Separation of Linearly Mixed Signals

Yehoshua Y. Zeevi, Pavel Kisilev, Michael Zibulevsky, 2003, J. Mach. Learn. Res..

Approximate Fourier phase information in the phase retrieval problem: what it gives and how to use it.

Irad Yavneh, Michael Zibulevsky, Eliyahu Osherovich, 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

Underdetermined blind source separation using sparse representations

Michael Zibulevsky, Pau Bofill, M. Zibulevsky, 2001, Signal Process..

Classical Scaling Revisited

Ron Kimmel, Michael Zibulevsky, Gil Shamai, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Blind Decomposition of Transmission Light Microscopic Hyperspectral Cube Using Sparse Representation

Ehud Rivlin, Grigory Begelman, Michael Zibulevsky, 2009, IEEE Transactions on Medical Imaging.

End to End Deep Neural Network Frequency Demodulation of Speech Signals

Michael Zibulevsky, Dan Elbaz, M. Zibulevsky, 2017, Advances in Intelligent Systems and Computing.

Independent Component Analysis: Blind source separation by sparse decomposition in a signal dictionary

Barak A. Pearlmutter, Pavel Kisilev, Michael Zibulevsky, 2001 .

Blind Source Separation via Multinode Sparse Representation

Barak A. Pearlmutter, Yehoshua Y. Zeevi, Pavel Kisilev, 2001, NIPS.

A wide-angle view at iterated shrinkage algorithms

Michael Elad, Michael Zibulevsky, Joseph Shtok, 2007, SPIE Optical Engineering + Applications.

A Probabilistic Study of the Average Performance of the Basis Pursuit

Michael Elad, Michael Zibulevsky, Michael Elad, 2004 .

Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

Michael Elad, Michael Zibulevsky, Ron Rubinstein, 2010, IEEE Transactions on Signal Processing.

Blind Source Separation Using the Block-Coordinate Relative Newton Method

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2004, ICA.

Sparse Component Analysis

Michael Zibulevsky, Rémi Gribonval, R. Gribonval, 2010 .

Separation of reflections via sparse ICA

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

ML reconstruction of dynamic PET images from projections and Clist

Michael Zibulevsky, M. Zibulevsky, 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

BLOCK-COORDINATE RELATIVE NEWTON METHOD FOR BLIND SOURCE SEPARATION

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2003 .

Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning pdf

Michael Elad, Michael Zibulevsky, Joseph Shtok, 2010, ArXiv.

Quasi-maximum likelihood blind deconvolution of images acquired through scattering media

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

High quality ultrasonic multi-line transmission through deep learning pdf

Alexander M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky, 2018, MLMIR@MICCAI.

Learning subject-specific spatial and temporal filters for single-trial EEG classification

Michael Zibulevsky, Dmitri Model, M. Zibulevsky, 2006, NeuroImage.

Learning Sparse Dictionaries for Sparse Signal Approximation

Michael Elad, Michael Zibulevsky, Ron Rubinstein, 2009 .

E-cient Implementation of the K-SVD Algorithm and the Batch-OMP Method

Michael Elad, Michael Zibulevsky, Ron Rubinstein, 2008 .

Blind source separation using block-coordinate relative Newton method

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2004, Signal Process..

Sparse ICA for blind separation of transmitted and reflected images

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2005, Int. J. Imaging Syst. Technol..

Signal reconstruction in sensor arrays using sparse representations

Michael Zibulevsky, Dmitri Model, M. Zibulevsky, 2006, Signal Process..

Compressed Sensing and Computed Tomography with Deep Neural Networks

Michael Elad, Michael Zibulevsky, David Boublil, 2015 .

Direct adaptive algorithms for CT reconstruction

Michael Elad, Michael Zibulevsky, Joseph Shtok, 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

Reconstruction in diffraction ultrasound tomography using nonuniform FFT

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2002, IEEE Transactions on Medical Imaging.

Trainlets: Dictionary Learning in High Dimensions

Michael Elad, Boaz Ophir, Michael Zibulevsky, 2016, IEEE Transactions on Signal Processing.

End to End Deep Neural Networks Radio Receiver for Speech Signals pdf

Michael Zibulevsky, Dan Elbaz, M. Zibulevsky, 2017, ArXiv.

"Unmixing" tissues: sparse component analysis in multi-contrast MRI

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2005, IEEE International Conference on Image Processing 2005.

Blind Deconvolution Using the Relative Newton Method

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2004, ICA.

Multiscale blind source separation

Barak A. Pearlmutter, Yehoshua Y. Zeevi, Pavel Kisilev, 2001, SPIE Defense + Commercial Sensing.

Sampling and Noise in Compressive Sensing

Michael Zibulevsky, Yehuda Pfeffer, 2010 .

SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques

Boris Ginsburg, Michael Zibulevsky, Elad Richardson, 2016, NIPS.

Multiscale Framework For Blind Source Separation

Pavel Kisilev, Michael Zibulevsky, M. Zibulevsky, 2003 .

Blind separation of mixed images using multiscale transforms

Yehoshua Y. Zeevi, Pavel Kisilev, Michael Zibulevsky, 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

Perceptual audio loss function for deep learning pdf

Michael Zibulevsky, Dan Elbaz, 2017, ArXiv.

Quasi maximum likelihood MIMO blind deconvolution: Super- and sub-Gaussianity versus consistency

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2005, IEEE Transactions on Signal Processing.

Relative optimization for blind deconvolution

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2005, IEEE Transactions on Signal Processing.

Optimal nonlinear estimation of photon coordinates in PET

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

An MEG Study of Response Latency and Variability in the Human Visual System During a Visual-Motor Integration Task

Barak A. Pearlmutter, Michael Zibulevsky, Akaysha C. Tang, 1999, NIPS.

On Separation of Semitransparent Dynamic Images from Static Background

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2006, ICA.

High frame-rate cardiac ultrasound imaging with deep learning pdf

Alexander M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky, 2018, MICCAI.

On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations

Michael Elad, Alfred M. Bruckstein, Michael Zibulevsky, 2008, IEEE Transactions on Information Theory.

Block-based compressed sensing of images via deep learning

Michael Zibulevsky, Amir Adler, David Boublil, 2017, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP).

Sparsity-based Sinogram Denoising for low-dose Computed Tomography

Michael Elad, Michael Zibulevsky, Joseph Shtok, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Speeding-Up Convergence via Sequential Subspace Optimization: Current State and Future Directions pdf

Michael Zibulevsky, M. Zibulevsky, 2013, ArXiv.

Submitted for Review to Ieee Transactions on Image Processing Blind Deconvolution of Images Using Optimal Sparse Representations

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2022 .

Compressed Learning: A Deep Neural Network Approach pdf

Michael Elad, Michael Zibulevsky, Amir Adler, 2016, ArXiv.

Towards CT-quality Ultrasound Imaging using Deep Learning pdf

Alexander M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky, 2017, ArXiv.

Fast Reconstruction Method for Diffraction Imaging

Irad Yavneh, Michael Zibulevsky, Eliyahu Osherovich, 2009, ISVC.

Signal Reconstruction in Sensor Arrays Using Temporal-Spatial Sparsity Regularization

Michael Zibulevsky, Dmitri Model, M. Zibulevsky, 2004, ICA.

Blind separation of sparse sources with relative Newton method

Michael Zibulevsky, 2003, SPIE Optics + Photonics.

Learned Shrinkage Approach for Low-Dose Reconstruction in Computed Tomography

Michael Elad, Michael Zibulevsky, Joseph Shtok, 2013, Int. J. Biomed. Imaging.

Blind source separation by sparse decomposition

Barak A. Pearlmutter, Michael Zibulevsky, M. Zibulevsky, 2000, SPIE Defense + Commercial Sensing.

MEG/EEG Source Localization Using Spatio-temporal Sparse Representations

Michael Zibulevsky, Alexey Polonsky, 2004, ICA.

Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit

Michael Elad, Michael Zibulevsky, Ron Rubinstein, 2008 .

PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated MRI pdf

Michael Zibulevsky, Tomer Weiss, Oleg Michailovich, 2019, ArXiv.

Iterative reconstruction in diffraction tomography using nonuniform fast Fourier transform

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

Optimal sparse representations for blind source separation and blind deconvolution: a learning approach

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

Texture and Structure Two-view Classification of Images pdf

Yehoshua Y. Zeevi, Samah Khawaled, Michael Zibulevsky, 2019, ArXiv.

The Design and Implementation of COSEM, an Iterative Algorithm for Fully 3D Listmode Data

Michael Zibulevsky, Arkadi Nemirovski, Aharon Ben-Tal, 2001, IEEE Trans. Medical Imaging.

Relative Newton method for signal separation

Michael Zibulevsky, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

BLIND SEPARATION OF REFLECTIONS USING SPARSE ICA

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2003 .

Separation of semireflective layers using sparse ICA

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

Accelerating Multigrid Optimization via SESOP pdf

Irad Yavneh, Tao Hong, Michael Zibulevsky, 2018, ArXiv.

Relative Newton and Smoothing Multiplier Optimization Methods for Blind Source Separation

Michael Zibulevsky, M. Zibulevsky, 2007, Blind Speech Separation.

Accelerating the Computation of Canonical Forms for 3D Nonrigid Objects using Multidimensional Scaling

Ron Kimmel, Michael Zibulevsky, Gil Shamai, 2015, 3DOR@Eurographics.

Simultaneous deconvolution and phase retrieval from noisy data

Irad Yavneh, Michael Zibulevsky, Eliyahu Osherovich, 2010 .

Phase retrieval combined with digital holography

Irad Yavneh, Michael Zibulevsky, Eliyahu Osherovich, 2012 .

Fast relative Newton algorithm for blind deconvolution of images

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

Penalty/Barrier Multiplier Methods for Convex Programming Problems

Michael Zibulevsky, Aharon Ben-Tal, A. Ben-Tal, 1997, SIAM J. Optim..

ICA Using Kernel Entropy Estimation with NlogN Complexity

Yoav Y. Schechner, Michael Zibulevsky, Sarit Shwartz, 2004, ICA.

Blind separation of mixed images using multiscale transforms

Yehoshua Y. Zeevi, Pavel Kisilev, Michael Zibulevsky, 2003, Visual Communications and Image Processing.

Solving RED via Weighted Proximal Methods

Irad Yavneh, Tao Hong, Michael Zibulevsky, 2019 .

Self-supervised learning of inverse problem solvers in medical imaging

Michael Zibulevsky, Tomer Weiss, Oleg Michailovich, 2019, DART/MIL3ID@MICCAI.

Blind source separation using multinode sparse representation

Yehoshua Y. Zeevi, Pavel Kisilev, Michael Zibulevsky, 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

Efficient Separation of Convolutive Image Mixtures

Yoav Y. Schechner, Michael Zibulevsky, Sarit Shwartz, 2006, ICA.

Blind source separation of multichannel neuromagnetic responses

Barak A. Pearlmutter, Michael Zibulevsky, Akaysha C. Tang, 2000, Neurocomputing.

Patch Ordering as a Regularization for Inverse Problems in Image Processing

Michael Elad, Michael Zibulevsky, Gregory Vaksman, 2016, SIAM J. Imaging Sci..

Blind separation of convolutive image mixtures

Yoav Y. Schechner, Michael Zibulevsky, Sarit Shwartz, 2008, Neurocomputing.

Application of wavelets in blind source separation

Yehoshua Y. Zeevi, Pavel Kisilev, Michael Zibulevsky, 2003, SPIE Optics + Photonics.

Fast Classical Scaling pdf

Ron Kimmel, Michael Zibulevsky, Gil Shamai, 2016, ArXiv.

Fast kernel entropy estimation and optimization

Yoav Y. Schechner, Michael Zibulevsky, Sarit Shwartz, 2005, Signal Process..

Designing and using prior knowledge for phase retrieval

Irad Yavneh, Michael Zibulevsky, Eliyahu Osherovich, 2012 .

Wavelet representation and total variation regularization in emission tomography

Yehoshua Y. Zeevi, Pavel Kisilev, Michael Zibulevsky, 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

L1-L2 Optimization in Signal and Image Processing

Michael Elad, Michael Zibulevsky, Michael Elad, 2010, IEEE Signal Processing Magazine.

Efficient Inter-Geodesic Distance Computation and Fast Classical Scaling

Ron Kimmel, Michael Zibulevsky, Gil Shamai, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Conformal Mapping with as Uniform as Possible Conformal Factor

Ron Kimmel, Michael Zibulevsky, Yonathan Aflalo, 2013, SIAM J. Imaging Sci..

QML Blind Deconvolution: Asymptotic Analysis

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2004, ICA.

Solving RED With Weighted Proximal Methods

Irad Yavneh, Tao Hong, Michael Zibulevsky, 2019, IEEE Signal Processing Letters.

Separation of reflections via sparse ICA

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2003, SPIE Optics + Photonics.

Learning beamforming in ultrasound imaging

Alexander M. Bronstein, Oleg V. Michailovich, Michael Zibulevsky, 2019, MIDL.

Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation

Arie Yeredor, Fabian J. Theis, Andrzej Cichocki, 2012 .

Improved denoising of images using modelling of a redundant contourlet transform

Michael Elad, Michael Zibulevsky, Boaz Matalon, 2005, SPIE Optics + Photonics.

Image Denoising with Shrinkage and Redundant Representations

Michael Elad, Michael Zibulevsky, Boaz Matalon, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

Blind separation of tissues in multi-modal MRI using Sparse Component Analysis

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2005, 2005 13th European Signal Processing Conference.

IMAGE RECONSTRUCTION FROM NOISY FOURIER MAGNITUDE WITH PARTIAL PHASE INFORMATION

Irad Yavneh, Michael Zibulevsky, Eliyahu Osherovich, 2009 .

A Deep Learning Approach to Block-based Compressed Sensing of Images pdf

Michael Elad, Michael Zibulevsky, Amir Adler, 2016, ArXiv.

Extraction of a source from multichannel data using sparse decomposition

Yehoshua Y. Zeevi, Michael Zibulevsky, M. Zibulevsky, 2002, Neurocomputing.

Quasi Maximum Likelihood Blind Deconvolution of Images Using Optimal Sparse Representations

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2003 .

Learning Fast Magnetic Resonance Imaging pdf

Michael Zibulevsky, Tomer Weiss, Oleg Michailovich, 2019, ArXiv.

Multiresolution framework for blind source separation

Barak A. Pearlmutter, Yehoshua Y. Zeevi, Pavel Kisilev, 2000 .

On the uniqueness of non-negative sparse & redundant representations

Michael Elad, Alfred M. Bruckstein, Michael Zibulevsky, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Underdetermined blind source separation using sparse representations Pau Bo # lla

Michael Zibulevsky, M. Zibulevsky, 2001 .

Algorithms for phase retrieval with a (rough) phase estimate available: a comparison

Irad Yavneh, Michael Zibulevsky, Eliyahu Osherovich, 2010 .

Optimal non-linear line-of-flight estimation in positron emission tomography

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2002, The 22nd Convention on Electrical and Electronics Engineers in Israel, 2002..

Pattern Recognition via Support Vector Machine with Computationally Efficient Nonlinear Transform

Michael Zibulevsky, 1998 .

Efficient Blind Separation of Convolutive Image Mixtures

Yoav Y. Schechner, Michael Zibulevsky, Sarit Shwartz, 2005 .

Second Order Blind Source Separation By Recursive Splitting Of Signal Subspaces

Barak A. Pearlmutter, Michael Zibulevsky, M. Zibulevsky, 2000 .

Mechanical design problems with unilateral contact

Michal Kočvara, Michael Zibulevsky, Jochem Zowe, 1998 .

Penalty/Barrier Multiplier Methods for Large-Scale Nonlinear and Semidefinite Programming

Michael Zibulevsky, M. Zibulevsky, 1996 .

Smoothing Method of Multipliers for Sum-Max Problems

Michael Zibulevsky, 2005 .

DENOISING WITH THE CONTOURLET TRANSFORM

Michael Elad, Michael Zibulevsky, Boaz Matalon, 2005 .

Optimal nonlinear line-of-flight estimation in positron emission tomography

Yehoshua Y. Zeevi, Alexander M. Bronstein, Michael M. Bronstein, 2003 .