Search Results for author:
Found 27 papers, 11 papers with code
Surface Coil Intensity Correction for MRI
Modern MRI scanners utilize one or more arrays of small receive-only coils to collect k-space data.
A Conditional Normalizing Flow for Accelerated Multi-Coil MR Imaging
Accelerated magnetic resonance (MR) imaging attempts to reduce acquisition time by collecting data below the Nyquist rate.
MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data
However, unlike traditional PnP approaches that utilize generic denoisers or train application-specific denoisers using high-quality images or image patches, ReSiDe directly trains the denoiser on the image or images that are being reconstructed from the undersampled data.
Denoising Generalized Expectation-Consistent Approximation for MR Image Recovery
To solve inverse problems, plug-and-play (PnP) methods replace the proximal step in a convex optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network (DNN).
Matching Plug-and-Play Algorithms to the Denoiser
To solve inverse problems, plug-and-play (PnP) methods have been developed that replace the proximal step in a convex optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network (DNN).
MRI Recovery with A Self-calibrated Denoiser
The proposed method, called recovery with a self-calibrated denoiser (ReSiDe), trains the denoiser from the patches of the image being recovered.
Deep Neural Networks for Radar Waveform Classification
We consider the problem of classifying radar pulses given raw I/Q waveforms in the presence of noise and absence of synchronization.
Matrix Inference and Estimation in Multi-Layer Models
In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features, as well as training samples, grow to infinity but the number of hidden nodes stays fixed.
Sketching Datasets for Large-Scale Learning (long version)
This article considers "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e. g., clustering, classification, or regression) is performed.
Free-breathing Cardiovascular MRI Using a Plug-and-Play Method with Learned Denoiser
We compare the reconstruction performance of PnP-DL to that of compressed sensing (CS) using eight breath-held and ten real-time (RT) free-breathing cardiac cine datasets.
Inference in Multi-Layer Networks with Matrix-Valued Unknowns
We consider the problem of inferring the input and hidden variables of a stochastic multi-layer neural network from an observation of the output.
Inference with Deep Generative Priors in High Dimensions
This paper presents a novel algorithm, Multi-Layer Vector Approximate Message Passing (ML-VAMP), for inference in multi-layer stochastic neural networks.
Plug and play methods for magnetic resonance imaging (long version)
In this article, we describe the use of "plug-and-play" (PnP) algorithms for MRI image recovery.
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
Estimating a vector $\mathbf{x}$ from noisy linear measurements $\mathbf{Ax}+\mathbf{w}$ often requires use of prior knowledge or structural constraints on $\mathbf{x}$ for accurate reconstruction.
Information Theory Information Theory
Regularization by Denoising: Clarifications and New Interpretations
To explain the RED algorithms, we propose a new framework called Score-Matching by Denoising (SMD), which aims to match a "score" (i. e., the gradient of a log-prior).
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Phase retrieval algorithms have become an important component in many modern computational imaging systems.
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
We show that the parameter estimates and mean squared error (MSE) of x in each iteration converge to deterministic limits that can be precisely predicted by a simple set of state evolution (SE) equations.
A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm
In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL).
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP.
Information Theory Information Theory
Denoising based Vector Approximate Message Passing
The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery.
Information Theory Information Theory
Vector Approximate Message Passing
The approximate message passing (AMP) algorithm recently proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i. i. d.\ sub-Gaussian matrices $\mathbf{A}$, its per-iteration behavior is rigorously characterized by a scalar state-evolution whose fixed points, when unique, are Bayes optimal.
Information Theory Information Theory
Onsager-corrected deep learning for sparse linear inverse problems
Deep learning has gained great popularity due to its widespread success on many inference problems.
Learning and Free Energies for Vector Approximate Message Passing
Like the AMP proposed by Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous state evolution (SE) that holds under certain large random matrices and that matches the replica prediction of optimality.
Expectation Consistent Approximate Inference: Generalizations and Convergence
Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems.
Sparse Multinomial Logistic Regression via Approximate Message Passing
For the problem of multi-class linear classification and feature selection, we propose approximate message passing approaches to sparse multinomial logistic regression (MLR).
Binary Linear Classification and Feature Selection via Generalized Approximate Message Passing
For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing.
A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments
We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments.
