102 citations · 261 across the 23 of their papers we have counts for
24 papers
Radiation design in computed tomography via convex optimization
Anatoli Juditsky, Arkadi Nemirovski, Michael Zibulevsky
Proper X-ray radiation design (via dynamic fluence field modulation, FFM) allows to reduce effective radiation dose in computed tomography without compromising image quality. It ta…
Primal-Dual Sequential Subspace Optimization for Saddle-point Problems
Yoni Choukroun, Michael Zibulevsky, Pavel Kisilev
We introduce a new sequential subspace optimization method for large-scale saddle-point problems. It solves iteratively a sequence of auxiliary saddle-point problems in low-dimensi…
PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI
Tomer Weiss, Ortal Senouf, Sanketh Vedula +3
Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging. The long acquisition times, however, render MRI prone to m…
Texture and Structure Two-view Classification of Images
Samah Khawaled, Michael Zibulevsky, Yehoshua Y. Zeevi
Textural and structural features can be regraded as "two-view" feature sets. Inspired by the recent progress in multi-view learning, we propose a novel two-view classification meth…
Self-supervised learning of inverse problem solvers in medical imaging
Ortal Senouf, Sanketh Vedula, Tomer Weiss +3
In the past few years, deep learning-based methods have demonstrated enormous success for solving inverse problems in medical imaging. In this work, we address the following questi…
Solving RED with Weighted Proximal Methods
Tao Hong, Irad Yavneh, Michael Zibulevsky
REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors. A drawback of this a…
Joint learning of cartesian undersampling and reconstruction for accelerated MRI
Tomer Weiss, Sanketh Vedula, Ortal Senouf +3
Magnetic Resonance Imaging (MRI) is considered today the golden-standard modality for soft tissues. The long acquisition times, however, make it more prone to motion artifacts as w…
Learning beamforming in ultrasound imaging
Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov +3
Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation. In this paper, we demo…
Merging Multigrid Optimization with SESOP
Tao Hong, Irad Yavneh, Michael Zibulevsky
A merger of two optimization frameworks is introduced: SEquential Subspace OPtimization (SESOP) with MultiGrid (MG) optimization. At each iteration of the algorithm, the search dir…
High frame-rate cardiac ultrasound imaging with deep learning
Ortal Senouf, Sanketh Vedula, Grigoriy Zurakhov +5
Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received…
High quality ultrasonic multi-line transmission through deep learning
Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov +5
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography. Several methods have been proposed in the medical ultrasound literature aiming at acceleratin…
Towards CT-quality Ultrasound Imaging using Deep Learning
Sanketh Vedula, Ortal Senouf, Alex M. Bronstein +2
The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based i…
Perceptual audio loss function for deep learning
Dan Elbaz, Michael Zibulevsky
PESQ and POLQA , are standards are standards for automated assessment of voice quality of speech as experienced by human beings. The predictions of those objective measures should…
End to End Deep Neural Network Frequency Demodulation of Speech Signals
Dan Elbaz, Michael Zibulevsky
Frequency modulation (FM) is a form of radio broadcasting which is widely used nowadays and has been for almost a century. We suggest a software-defined-radio (SDR) receiver for FM…
Efficient Inter-Geodesic Distance Computation and Fast Classical Scaling
Gil Shamai, Michael Zibulevsky, Ron Kimmel
Multidimensional scaling (MDS) is a dimensionality reduction tool used for information analysis, data visualization and manifold learning. Most MDS procedures embed data points in…
Compressed Learning: A Deep Neural Network Approach
Amir Adler, Michael Elad, Michael Zibulevsky
Compressed Learning (CL) is a joint signal processing and machine learning framework for inference from a signal, using a small number of measurements obtained by linear projection…
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Elad Richardson, Rom Herskovitz, Boris Ginsburg +1
We present SEBOOST, a technique for boosting the performance of existing stochastic optimization methods. SEBOOST applies a secondary optimization process in the subspace spanned b…
A Deep Learning Approach to Block-based Compressed Sensing of Images
Amir Adler, David Boublil, Michael Elad +1
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal.…
Trainlets: Dictionary Learning in High Dimensions
Jeremias Sulam, Boaz Ophir, Michael Zibulevsky +1
Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the abi…
Speeding-Up Convergence via Sequential Subspace Optimization: Current State and Future Directions
Michael Zibulevsky
This is an overview paper written in style of research proposal. In recent years we introduced a general framework for large-scale unconstrained optimization -- Sequential Subspace…
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
Joseph Shtok, Michael Zibulevsky, Michael Elad
We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in compute…
Designing and using prior knowledge for phase retrieval
Eliyahu Osherovich, Michael Zibulevsky, Irad Yavneh
In this work we develop an algorithm for signal reconstruction from the magnitude of its Fourier transform in a situation where some (non-zero) parts of the sought signal are known…
Phase retrieval combined with digital holography
Eliyahu Osherovich, Michael Zibulevsky, Irad Yavneh
We present a new method for real- and complex-valued image reconstruction from two intensity measurements made in the Fourier plane: the Fourier magnitude of the unknown image, and…
Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning
Joseph Shtok, Michael Zibulevsky, Michael Elad
We propose a direct reconstruction algorithm for Computed Tomography, based on a local fusion of a few preliminary image estimates by means of a non-linear fusion rule. One such ru…