32 citations · 70 across the 11 of their papers we have counts for
12 papers
Stochastic Natural Thresholding Algorithms
Rachel Grotheer, Shuang Li, Anna Ma +2
Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of…
Learning Homeomorphic Image Registration via Conformal-Invariant Hyperelastic Regularisation
Jing Zou, Noémie Debroux, Lihao Liu +3
Deformable image registration is a fundamental task in medical image analysis and plays a crucial role in a wide range of clinical applications. Recently, deep learning-based appro…
Federated Gradient Matching Pursuit
Halyun Jeong, Deanna Needell, Jing Qin
Traditional machine learning techniques require centralizing all training data on one server or data hub. Due to the development of communication technologies and a huge amount of…
Tuning-parameter-free optimal propensity score matching approach for causal inference
Yukun Liu, Jing Qin
Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or…
A Novel Regularization Based on the Error Function for Sparse Recovery
Weihong Guo, Yifei Lou, Jing Qin +1
Regularization plays an important role in solving ill-posed problems by adding extra information about the desired solution, such as sparsity. Many regularization terms usually inv…
Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery
Rachel Grotheer, Shuang Li, Anna Ma +2
Low-rank tensor recovery problems have been widely studied in many applications of signal processing and machine learning. Tucker decomposition is known as one of the most popular…
Iterative Hard Thresholding for Low CP-rank Tensor Models
Rachel Grotheer, Shuang Li, Anna Ma +2
Recovery of low-rank matrices from a small number of linear measurements is now well-known to be possible under various model assumptions on the measurements. Such results demonstr…
Compressed Anomaly Detection with Multiple Mixed Observations
Natalie Durgin, Rachel Grotheer, Chenxi Huang +4
We consider a collection of independent random variables that are identically distributed, except for a small subset which follows a different, anomalous distribution. We study the…
Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors
Natalie Durgin, Rachel Grotheer, Chenxi Huang +4
While single measurement vector (SMV) models have been widely studied in signal processing, there is a surging interest in addressing the multiple measurement vectors (MMV) problem…
Stochastic Greedy Algorithms For Multiple Measurement Vectors
Jing Qin, Shuang Li, Deanna Needell +4
Sparse representation of a single measurement vector (SMV) has been explored in a variety of compressive sensing applications. Recently, SMV models have been extended to solve mult…
A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-norm Fidelity
Fang Li, Stanley Osher, Jing Qin +1
In this paper, we propose a variational multiphase image segmentation model based on fuzzy membership functions and L1-norm fidelity. Then we apply the alternating direction method…
Two-stage Geometric Information Guided Image Reconstruction
Jing Qin, Weihong Guo
In compressive sensing, it is challenging to reconstruct image of high quality from very few noisy linear projections. Existing methods mostly work well on piecewise constant image…