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Found 20 papers, 7 papers with code
Primal-Dual Sequential Subspace Optimization for Saddle-point Problems
We introduce a new sequential subspace optimization method for large-scale saddle-point problems.
PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI
Such schemes have already demonstrated substantial effectiveness, leading to considerably shorter acquisition times and improved quality of image reconstruction.
Texture and Structure Two-view Classification of Images
Textural and structural features can be regraded as "two-view" feature sets.
Solving RED with Weighted Proximal Methods
REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors.
Joint learning of cartesian undersampling and reconstruction for accelerated MRI
On the other hand, recent works in optical computational imaging have demonstrated growing success of the simultaneous learning-based design of the acquisition and reconstruction schemes manifesting significant improvement in the reconstruction quality with a constrained time budget.
Self-supervised learning of inverse problem solvers in medical imaging
In light of this, we propose a self-supervised approach to training inverse models in medical imaging in the absence of aligned data.
Learning beamforming in ultrasound imaging
Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation.
High frame-rate cardiac ultrasound imaging with deep learning
The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.
High quality ultrasonic multi-line transmission through deep learning
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography.
Towards CT-quality Ultrasound Imaging using Deep Learning
The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis.
Perceptual audio loss function for deep learning
PESQ and POLQA , are standards are standards for automated assessment of voice quality of speech as experienced by human beings.
End to End Deep Neural Network Frequency Demodulation of Speech Signals
Frequency modulation (FM) is a form of radio broadcasting which is widely used nowadays and has been for almost a century.
Compressed Learning: A Deep Neural Network Approach
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 projections of the signal.
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
SEBOOST applies a secondary optimization process in the subspace spanned by the last steps and descent directions.
A Deep Learning Approach to Block-based Compressed Sensing of Images
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.
Patch-Ordering as a Regularization for Inverse Problems in Image Processing
Recent work in image processing suggests that operating on (overlapping) patches in an image may lead to state-of-the-art results.
Trainlets: Dictionary Learning in High Dimensions
Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance.
Classical Scaling Revisited
We present an efficient solver for Classical Scaling (a specific MDS model) by extending the distances measured from a subset of the points to the rest, while exploiting the smoothness property of the distance functions.
Speeding-Up Convergence via Sequential Subspace Optimization: Current State and Future Directions
This is an overview paper written in style of research proposal.
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
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 computed tomography.
