research
∙
04/07/2023
RED-PSM: Regularization by Denoising of Partially Separable Models for Dynamic Imaging
Dynamic imaging addresses the recovery of a time-varying 2D or 3D object...
share
research
∙
03/07/2022
Learning to Bound: A Generative Cramér-Rao Bound
The Cramér-Rao bound (CRB), a well-known lower bound on the performance ...
research
∙
03/21/2021
Scatter Correction in X-ray CT by Physics-Inspired Deep Learning
A fundamental problem in X-ray Computed Tomography (CT) is the scatter d...
research
∙
01/14/2021
Joint Dimensionality Reduction for Separable Embedding Estimation
Low-dimensional embeddings for data from disparate sources play critical...
research
∙
03/29/2020
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems
Image prior modeling is the key issue in image recovery, computational i...
research
∙
02/26/2020
Improving Robustness of Deep-Learning-Based Image Reconstruction
Deep-learning-based methods for different applications have been shown v...
research
∙
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...
research
∙
02/26/2019
GAN-based Projector for Faster Recovery in Compressed Sensing with Convergence Guarantees
A Generative Adversarial Network (GAN) with generator G trained to model...
research
∙
08/03/2018
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling
Recent works on adaptive sparse and on low-rank signal modeling have dem...
research
∙
05/26/2018
Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere
Multichannel blind deconvolution is the problem of recovering an unknown...
research
∙
03/06/2018
Learning Filter Bank Sparsifying Transforms
Data is said to follow the transform (or analysis) sparsity model if it ...
research
∙
12/05/2017
Optimal Sample Complexity for Stable Matrix Recovery
Tremendous efforts have been made to study the theoretical and algorithm...
research
∙
11/30/2017
Blind Gain and Phase Calibration via Sparse Spectral Methods
Blind gain and phase calibration (BGPC) is a bilinear inverse problem in...
research
∙
10/03/2017
VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising
Techniques exploiting the sparsity of images in a transform domain have ...
research
∙
02/13/2016
Joint Dimensionality Reduction for Two Feature Vectors
Many machine learning problems, especially multi-modal learning problems...
research
∙
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...
research
∙
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 ...
research
∙
01/13/2015