activity
20122022
most citedLow-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

1.9k citations · 5.3k across the 34 of their papers we have counts for

collaborators
Showing eess.IVShow all

7 papers · 1 filter

eess.IV2022★ 38 cited

Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging

Ziyuan Yang, Wenjun Xia, Zexin Lu +3

Computed tomography (CT) is of great importance in clinical practice due to its powerful ability to provide patients' anatomical information without any invasive inspection, but it…

eess.IV2021★ 1 cited

LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes

Jiahui Ni, Zhimin Shao, Zhongzhou Zhang +4

Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchroma…

eess.IV2021

Unsupervised PET Reconstruction from a Bayesian Perspective

Chenyu Shen, Wenjun Xia, Hongwei Ye +5

Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve i…

eess.IV2021

One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline

Zhiwen Wang, Wenjun Xia, Zexin Lu +5

Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow. It is easy to notic…

eess.IV2020

Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model

Zhuonan He, Yikun Zhang, Yu Guan +4

Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction is one of the most promising ways to compen…

eess.IV2020★ 3 cited

MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction

Wenjun Xia, Zexin Lu, Yongqiang Huang +6

Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstru…

eess.IV2020

Noise-Powered Disentangled Representation for Unsupervised Speckle Reduction of Optical Coherence Tomography Images

Yongqiang Huang, Wenjun Xia, Zexin Lu +5

Due to its noninvasive character, optical coherence tomography (OCT) has become a popular diagnostic method in clinical settings. However, the low-coherence interferometric imaging…