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07/25/2022
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration
Image restoration schemes based on the pre-trained deep models have rece...
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07/18/2022
Learning Sparsity-Promoting Regularizers using Bilevel Optimization
We present a method for supervised learning of sparsity-promoting regula...
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06/01/2022
Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data
Recent medical image reconstruction techniques focus on generating high-...
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03/22/2022
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction
The recently proposed sparsifying transform models incur low computation...
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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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11/21/2021
Bilevel learning of l1-regularizers with closed-form gradients(BLORC)
We present a method for supervised learning of sparsity-promoting regula...
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11/17/2021
Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
There is much recent interest in techniques to accelerate the data acqui...
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10/28/2021
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography
Object density reconstruction from projections containing scattered radi...
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03/26/2021
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Many techniques have been proposed for image reconstruction in medical i...
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11/01/2020
Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
Achieving high-quality reconstructions from low-dose computed tomography...
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10/10/2020
Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction
Signal models based on sparse representations have received considerable...
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06/24/2020
Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration
Constructing effective image priors is critical to solving ill-posed inv...
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06/09/2020
Supervised Learning of Sparsity-Promoting Regularizers for Denoising
We present a method for supervised learning of sparsity-promoting regula...
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05/08/2020
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction
Signal models based on sparse representation have received considerable ...
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10/26/2019
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Recent years have witnessed growing interest in machine learning-based m...
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06/01/2019
Multi-layer Residual Sparsifying Transform Learning for Image Reconstruction
Signal models based on sparsity, low-rank and other properties have been...
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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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03/25/2019
Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
Magnetic resonance imaging (MRI) is widely used in clinical practice for...
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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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10/19/2018
Learning Multi-Layer Transform Models
Learned data models based on sparsity are widely used in signal processi...
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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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05/31/2018
Analysis of Fast Structured Dictionary Learning
Sparsity-based models and techniques have been exploited in many signal ...
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02/01/2018
Analysis of Fast Alternating Minimization for Structured Dictionary Learning
Methods exploiting sparsity have been popular in imaging and signal proc...
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10/03/2017
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Techniques exploiting the sparsity of images in a transform domain have ...
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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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11/04/2015
Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing
Compressed sensing is a powerful tool in applications such as magnetic r...
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01/13/2015
Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI
Natural signals and images are well-known to be approximately sparse in ...
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01/13/2015
