92 citations · 241 across the 20 of their papers we have counts for
6 papers · 1 filter
An Improved Iterative Neural Network for High-Quality Image-Domain Material Decomposition in Dual-Energy CT
Zhipeng Li, Yong Long, Il Yong Chun
Dual-energy computed tomography (DECT) has been widely used in many applications that need material decomposition. Image-domain methods directly decompose material images from high…
Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction
Xikai Yang, Yong Long, Saiprasad Ravishankar
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings. Model-based image reconstruction methods ha…
Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction
Xikai Yang, Yong Long, Saiprasad Ravishankar
Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, com…
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…
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction
Xikai Yang, Xuehang Zheng, Yong Long +1
Signal models based on sparse representation have received considerable attention in recent years. Compared to synthesis dictionary learning, sparsifying transform learning involve…
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…