6 citations · 17 across the 10 of their papers we have counts for
10 papers
LAMA-Net: A Convergent Network Architecture for Dual-Domain Reconstruction
Chi Ding, Qingchao Zhang, Ge Wang +2
We propose a learnable variational model that learns the features and leverages complementary information from both image and measurement domains for image reconstruction. In parti…
An Immediate Update Strategy of Multi-State Constraint Kalman Filter
Qingchao Zhang, Wei Ouyang, Jiale Han +3
The lightweight Multi-state Constraint Kalman Filter (MSCKF) has been well-known for its high efficiency, in which the delayed update has been usually adopted since its proposal. T…
LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT
Chi Ding, Qingchao Zhang, Ge Wang +2
Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block opt…
Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction
Chi Ding, Qingchao Zhang, Ge Wang +2
We propose a novel Learned Alternating Minimization Algorithm (LAMA) for dual-domain sparse-view CT image reconstruction. LAMA is naturally induced by a variational model for CT re…
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis
Wanyu Bian, Qingchao Zhang, Xiaojing Ye +1
Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propos…
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset
Wanyu Bian, Yunmei Chen, Xiaojing Ye +1
Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning o…
Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction
Qingchao Zhang, Mehrdad Alvandipour, Wenjun Xia +3
We propose a provably convergent method, called Efficient Learned Descent Algorithm (ELDA), for low-dose CT (LDCT) reconstruction. ELDA is a highly interpretable neural network arc…
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction
Yunmei Chen, Hongcheng Liu, Xiaojing Ye +1
We propose a general learning based framework for solving nonsmooth and nonconvex image reconstruction problems. We model the regularization function as the composition of the $l_{…
A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
Qingchao Zhang, Xiaojing Ye, Hongcheng Liu +1
Optimization algorithms for solving nonconvex inverse problem have attracted significant interests recently. However, existing methods require the nonconvex regularization to be sm…
Extra Proximal-Gradient Inspired Non-local Network
Qingchao Zhang, Yunmei Chen
Variational method and deep learning method are two mainstream powerful approaches to solve inverse problems in computer vision. To take advantages of advanced optimization algorit…