92 citations · 241 across the 20 of their papers we have counts for
20 papers
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…