21 citations · 26 across the 7 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…