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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...
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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...
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03/26/2023
Dynamic Subspace Estimation with Grassmannian Geodesics
Dynamic subspace estimation, or subspace tracking, is a fundamental prob...
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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...
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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...
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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...
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04/02/2021
Algorithms for Poisson Phase Retrieval
This paper discusses algorithms for phase retrieval where the measuremen...
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01/10/2021
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
Principal component analysis (PCA) is a classical and ubiquitous method ...
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08/04/2019
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Obtaining accurate and reliable images from low-dose computed tomography...
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07/26/2019
Momentum-Net: Fast and convergent iterative neural network for inverse problems
Iterative neural networks (INN) are rapidly gaining attention for solvin...
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06/05/2019
Improved low-count quantitative PET reconstruction with a variational neural network
Image reconstruction in low-count PET is particularly challenging becaus...
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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...
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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...
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02/21/2019
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised...
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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...
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10/30/2018
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
Modern applications increasingly involve high-dimensional and heterogene...
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09/24/2018
Fast, Precise Myelin Water Quantification using DESS MRI and Kernel Learning
Purpose: To investigate the feasibility of myelin water content quantifi...
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09/06/2018
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Sparsity and low-rank models have been popular for reconstructing images...
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02/20/2018
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
In "extreme" computational imaging that collects extremely undersampled ...
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02/15/2018
Convolutional Analysis Operator Learning: Acceleration, Convergence, Application, and Neural Networks
Convolutional operator learning is increasingly gaining attention in man...
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11/02/2017
Sparse-View X-Ray CT Reconstruction Using â„“_1 Prior with Learned Transform
A major challenge in X-ray computed tomography (CT) is reducing radiatio...
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10/06/2017
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels
This paper introduces a fast, general method for dictionary-free paramet...
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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...
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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...
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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...
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10/12/2016
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Principal Component Analysis (PCA) is a method for estimating a subspace...
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12/14/2015
Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction
Statistical image reconstruction (SIR) methods are studied extensively f...
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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...
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02/18/2014