25 citations · 51 across the 4 of their papers we have counts for
5 papers
Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond
Zhishen Huang, Siqi Ye, Michael T. McCann +1
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements. Model-base…
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction
Siqi Ye, Zhipeng Li, Michael T. McCann +2
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent machine learning methods for image reconstruction typ…
Momentum-Net for Low-Dose CT Image Reconstruction
Siqi Ye, Yong Long, Il Yong Chun
This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. At…
SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction
Zhipeng Li, Siqi Ye, Yong Long +1
Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized in…
SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models
Siqi Ye, Saiprasad Ravishankar, Yong Long +1
Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-leas…