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01/06/2023
Radiation design in computed tomography via convex optimization
Proper X-ray radiation design (via dynamic fluence field modulation, FFM...
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08/20/2020
Primal-Dual Sequential Subspace Optimization for Saddle-point Problems
We introduce a new sequential subspace optimization method for large-sca...
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09/12/2019
PILOT: Physics-Informed Learned Optimal Trajectories for Accelerated MRI
Magnetic Resonance Imaging (MRI) has long been considered to be among "t...
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08/25/2019
Texture and Structure Two-view Classification of Images
Textural and structural features can be regraded as "two-view" feature s...
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05/22/2019
Self-supervised learning of inverse problem solvers in medical imaging
In the past few years, deep learning-based methods have demonstrated eno...
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05/22/2019
Learning Fast Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) is considered today the golden-standard...
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12/19/2018
Learning beamforming in ultrasound imaging
Medical ultrasound (US) is a widespread imaging modality owing its popul...
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12/17/2018
Accelerating Multigrid Optimization via SESOP
A merger of two optimization frameworks is introduced: SEquential Subspa...
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08/23/2018
High frame-rate cardiac ultrasound imaging with deep learning
Cardiac ultrasound imaging requires a high frame rate in order to captur...
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08/23/2018
High quality ultrasonic multi-line transmission through deep learning
Frame rate is a crucial consideration in cardiac ultrasound imaging and ...
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10/17/2017
Towards CT-quality Ultrasound Imaging using Deep Learning
The cost-effectiveness and practical harmlessness of ultrasound imaging ...
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08/20/2017
Perceptual audio loss function for deep learning
PESQ and POLQA , are standards are standards for automated assessment of...
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10/30/2016
Compressed Learning: A Deep Neural Network Approach
Compressed Learning (CL) is a joint signal processing and machine learni...
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09/02/2016
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
We present SEBOOST, a technique for boosting the performance of existing...
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06/05/2016
A Deep Learning Approach to Block-based Compressed Sensing of Images
Compressed sensing (CS) is a signal processing framework for efficiently...
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02/26/2016
Patch-Ordering as a Regularization for Inverse Problems in Image Processing
Recent work in image processing suggests that operating on (overlapping)...
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01/31/2016
Trainlets: Dictionary Learning in High Dimensions
Sparse representations has shown to be a very powerful model for real wo...
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11/28/2013
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
We propose a supervised machine learning approach for boosting existing ...
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04/25/2010