
PrimalDual Sequential Subspace Optimization for Saddlepoint Problems
We introduce a new sequential subspace optimization method for largesca...
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PILOT: PhysicsInformed Learned Optimal Trajectories for Accelerated MRI
Magnetic Resonance Imaging (MRI) has long been considered to be among "t...
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Texture and Structure Twoview Classification of Images
Textural and structural features can be regraded as "twoview" feature s...
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Selfsupervised learning of inverse problem solvers in medical imaging
In the past few years, deep learningbased methods have demonstrated eno...
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Learning Fast Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) is considered today the goldenstandard...
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Learning beamforming in ultrasound imaging
Medical ultrasound (US) is a widespread imaging modality owing its popul...
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Accelerating Multigrid Optimization via SESOP
A merger of two optimization frameworks is introduced: SEquential Subspa...
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High framerate cardiac ultrasound imaging with deep learning
Cardiac ultrasound imaging requires a high frame rate in order to captur...
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High quality ultrasonic multiline transmission through deep learning
Frame rate is a crucial consideration in cardiac ultrasound imaging and ...
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Towards CTquality Ultrasound Imaging using Deep Learning
The costeffectiveness and practical harmlessness of ultrasound imaging ...
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Perceptual audio loss function for deep learning
PESQ and POLQA , are standards are standards for automated assessment of...
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Compressed Learning: A Deep Neural Network Approach
Compressed Learning (CL) is a joint signal processing and machine learni...
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SEBOOST  Boosting Stochastic Learning Using Subspace Optimization Techniques
We present SEBOOST, a technique for boosting the performance of existing...
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A Deep Learning Approach to Blockbased Compressed Sensing of Images
Compressed sensing (CS) is a signal processing framework for efficiently...
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PatchOrdering as a Regularization for Inverse Problems in Image Processing
Recent work in image processing suggests that operating on (overlapping)...
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Trainlets: Dictionary Learning in High Dimensions
Sparse representations has shown to be a very powerful model for real wo...
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SpatiallyAdaptive Reconstruction in Computed Tomography using Neural Networks
We propose a supervised machine learning approach for boosting existing ...
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SpatiallyAdaptive Reconstruction in Computed Tomography Based on Statistical Learning
We propose a direct reconstruction algorithm for Computed Tomography, ba...
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Michael Zibulevsky
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