51 citations · 157 across the 25 of their papers we have counts for
25 papers
Arbitrary Distributions Mapping via SyMOT-Flow: A Flow-based Approach Integrating Maximum Mean Discrepancy and Optimal Transport
Zhe Xiong, Qiaoqiao Ding, Xiaoqun Zhang
Finding a transformation between two unknown probability distributions from finite samples is crucial for modeling complex data distributions and performing tasks such as sample ge…
Approximate Primal-Dual Fixed-Point based Langevin Algorithms for Non-smooth Convex Potentials
Ziruo Cai, Jinglai Li, Xiaoqun Zhang
The Langevin algorithms are frequently used to sample the posterior distributions in Bayesian inference. In many practical problems, however, the posterior distributions often cons…
On the convergence of continuous and discrete unbalanced optimal transport models
Zhe Xiong, Lei Li, Ya-Nan Zhu +1
We consider a Beckmann formulation of an unbalanced optimal transport (UOT) problem. The -convergence of this formulation of UOT to the corresponding optimal transport (OT) prob…
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction
Qiaoqiao Ding, Hui Ji, Yuhui Quan +1
Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation. In recent years, supervised deep learning (DL) has been…
A stochastic three-block splitting algorithm and its application to quantized deep neural networks
Fengmiao Bian, Ren Liu, Xiaoqun Zhang
Deep neural networks (DNNs) have made great progress in various fields. In particular, the quantized neural network is a promising technique making DNNs compatible on resource-limi…
Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstruction
Zhao He, Ya-Nan Zhu, Suhao Qiu +2
Objective: Interventional MRI (i-MRI) is crucial for MR image-guided therapy. Current image reconstruction methods for dynamic MR imaging are mostly retrospective that may not be s…
A Stochastic Alternating Direction Method of Multipliers for Non-smooth and Non-convex Optimization
Fengmiao Bian, Jingwei Liang, Xiaoqun Zhang
Alternating direction method of multipliers (ADMM) is a popular first-order method owing to its simplicity and efficiency. However, similar to other proximal splitting methods, the…
A Stochastic Variance Reduced Primal Dual Fixed Point Method For Linearly Constrained Separable Optimization
Ya-Nan Zhu, Xiaoqun Zhang
In this paper we combine the stochastic variance reduced gradient (SVRG) method [17] with the primal dual fixed point method (PDFP) proposed in [7] to solve a sum of two convex fun…
A three-operator splitting algorithm for nonconvex sparsity regularization
Fengmiao Bian, Xiaoqun Zhang
Sparsity regularization has been largely applied in many fields, such as signal and image processing and machine learning. In this paper, we mainly consider nonconvex minimization…
Stochastic primal dual fixed point method for composite optimization
YaNanZhu, XiaoqunZhang
In this paper we propose a stochastic primal dual fixed point method (SPDFP) for solving the sum of two proper lower semi-continuous convex function and one of which is composite.…
Semi-Implicit Back Propagation
Ren Liu, Xiaoqun Zhang
Neural network has attracted great attention for a long time and many researchers are devoted to improve the effectiveness of neural network training algorithms. Though stochastic…
A parameterized Douglas-Rachford Splitting algorithm for nonconvex optimization
Fengmiao Bian, Xiaoqun Zhang
In this paper, we study a parameterized Douglas-Rachford splitting method for a class of nonconvex optimization problem. A new merit function is constructed to establish the conver…
Low-Dose CT with Deep Learning Regularization via Proximal Forward Backward Splitting
Qiaoqiao Ding, Gaoyu Chen, Xiaoqun Zhang +2
Low dose X-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops image reconstruction methods with deep learning (DL) regularization for LDCT. Ou…
A greedy algorithm for sparse precision matrix approximation
Didi Lv, Xiaoqun Zhang
Precision matrix estimation is an important problem in statistical data analysis. This paper introduces a fast sparse precision matrix estimation algorithm, namely GISS, whic…
Bayesian inference and uncertainty quantification for image reconstruction with Poisson data
Qingping Zhou, Tengchao Yu, Xiaoqun Zhang +1
We provide a complete framework for performing infinite-dimensional Bayesian inference and uncertainty quantification for image reconstruction with Poisson data. In particular, we…
NLTG Priors in Medical Image: Nonlocal TV-Gaussian (NLTG) prior for Bayesian inverse problems with applications to Limited CT Reconstruction
Didi Lv, Qingping Zhou, Jae Kyu Choi +2
Bayesian inference methods have been widely applied in inverse problems, {largely due to their ability to characterize the uncertainty associated with the estimation results.} {In…
Statistical Image Reconstruction Using Mixed Poisson-Gaussian Noise Model for X-Ray CT
Qiaoqiao Ding, Yong Long, Xiaoqun Zhang +1
Statistical image reconstruction (SIR) methods for X-ray CT produce high-quality and accurate images, while greatly reducing patient exposure to radiation. When further reducing X-…
Image-domain multi-material decomposition for dual-energy CT based on correlation and sparsity of material images
Qiaoqiao Ding, Tianye Niu, Xiaoqun Zhang +1
Dual energy CT (DECT) enhances tissue characterization because it can produce images of basis materials such as soft-tissue and bone. DECT is of great interest in applications to m…
Dynamic SPECT reconstruction with temporal edge correlation
Qiaoqiao Ding, Martin Burger, Xiaoqun Zhang
In dynamic imaging, a key challenge is to reconstruct image sequences with high temporal resolution from strong undersampling projections due to a relatively slow data acquisition…
PET-MRI Joint Reconstruction by Joint Sparsity Based Tight Frame Regularization
Jae Kyu Choi, Chenglong Bao, Xiaoqun Zhang
Recent technical advances lead to the coupling of PET and MRI scanners, enabling to acquire functional and anatomical data simultaneously. In this paper, we propose a tight frame b…
An Edge Driven Wavelet Frame Model for Image Restoration
Jae Kyu Choi, Bin Dong, Xiaoqun Zhang
Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for ima…
Simultaneous Reconstruction and Segmentation for Dynamic SPECT Imaging
Martin Burger, Carolin Rossmanith, Xiaoqun Zhang
This work deals with the reconstruction of dynamic images that incorporate characteristic dynamics in certain subregions, as arising for the kinetics of many tracers in emission to…
Limited Tomography Reconstruction via Tight Frame and Sinogram Extrapolation
Jae Kyu Choi, Bin Dong, Xiaoqun Zhang
X-ray computed tomography (CT) is one of widely used diagnostic tools for medical and dental tomographic imaging of the human body. However, the standard filtered backprojection re…
A primal-dual fixed point algorithm for multi-block convex minimization
Peijun Chen, Jianguo Huang, Xiaoqun Zhang
We extend a primal-dual fixed point algorithm (PDFP) proposed in [5] to solve two kinds of separable multi-block minimization problems, arising in signal processing and imaging sci…