92 citations · 241 across the 20 of their papers we have counts for
12 papers · 1 filter
Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and Unsupervised Learning
Ling Chen, Zhishen Huang, Yong Long +1
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image recons…
Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction
Ling Chen, Zhishen Huang, Yong Long +1
Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image recons…
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
Il Yong Chun, Dongwon Park, Xuehang Zheng +2
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies. Yet, studying and understanding self-su…
Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction
Xikai Yang, Zhishen Huang, Yong Long +1
The recently proposed sparsifying transform models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure revea…
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…
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…
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization
Il Yong Chun, Xuehang Zheng, Yong Long +1
Obtaining accurate and reliable images from low-dose computed tomography (CT) is challenging. Regression convolutional neural network (CNN) models that are learned from training da…
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
Xuehang Zheng, Saiprasad Ravishankar, Yong Long +2
Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computation…
DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering
Zhipeng Li, Saiprasad Ravishankar, Yong Long +1
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition ope…