research
∙
07/06/2023
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization
Principal component analysis (PCA) is a key tool in the field of data di...
share
research
∙
05/12/2023
AWFSD: Accelerated Wirtinger Flow with Score-based Diffusion Image Prior for Poisson-Gaussian Holographic Phase Retrieval
Phase retrieval (PR) is an essential problem in a number of coherent ima...
research
∙
03/26/2023
Dynamic Subspace Estimation with Grassmannian Geodesics
Dynamic subspace estimation, or subspace tracking, is a fundamental prob...
research
∙
03/05/2023
A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI
Model-based methods are widely used for reconstruction in compressed sen...
research
∙
01/21/2023
HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise
Mixtures of probabilistic principal component analysis (MPPCA) is a well...
research
∙
01/23/2022
Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data
Reconstruction of CT images from a limited set of projections through an...
research
∙
04/02/2021
Algorithms for Poisson Phase Retrieval
This paper discusses algorithms for phase retrieval where the measuremen...
research
∙
01/10/2021
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
Principal component analysis (PCA) is a classical and ubiquitous method ...
research
∙
08/04/2019
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Obtaining accurate and reliable images from low-dose computed tomography...
research
∙
07/26/2019
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Iterative neural networks (INN) are rapidly gaining attention for solvin...
research
∙
06/05/2019
Improved low-count quantitative PET reconstruction with a variational neural network
Image reconstruction in low-count PET is particularly challenging becaus...
research
∙
05/15/2019
Optimizing MRF-ASL Scan Design for Precise Quantification of Brain Hemodynamics using Neural Network Regression
Purpose: Arterial Spin Labeling (ASL) is a quantitative, non-invasive al...
research
∙
04/04/2019
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
The field of image reconstruction has undergone four waves of methods. T...
research
∙
02/21/2019
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised...
research
∙
01/01/2019
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Dual energy computed tomography (DECT) imaging plays an important role i...
research
∙
10/30/2018
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
Modern applications increasingly involve high-dimensional and heterogene...
research
∙
09/24/2018
Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning
Purpose: To investigate the feasibility of myelin water content quantifi...
research
∙
09/06/2018
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Sparsity and low-rank models have been popular for reconstructing images...
research
∙
02/20/2018
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
In "extreme" computational imaging that collects extremely undersampled ...
research
∙
02/15/2018
Convolutional Analysis Operator Learning: Acceleration, Convergence, Application, and Neural Networks
Convolutional operator learning is increasingly gaining attention in man...
research
∙
11/02/2017
Sparse-View X-Ray CT Reconstruction Using ℓ_1 Prior with Learned Transform
A major challenge in X-ray computed tomography (CT) is reducing radiatio...
research
∙
10/06/2017
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels
This paper introduces a fast, general method for dictionary-free paramet...
research
∙
07/10/2017
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to...
research
∙
03/27/2017
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
The development of computed tomography (CT) image reconstruction methods...
research
∙
11/13/2016
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging
Sparsity-based approaches have been popular in many applications in imag...
research
∙
10/12/2016
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Principal Component Analysis (PCA) is a method for estimating a subspace...
research
∙
12/14/2015
Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction
Statistical image reconstruction (SIR) methods are studied extensively f...
research
∙
02/18/2014
Fast X-ray CT image reconstruction using the linearized augmented Lagrangian method with ordered subsets
The augmented Lagrangian (AL) method that solves convex optimization pro...
research
∙
02/18/2014