6 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…
Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences
Zhishen Huang
Accelerated magnetic resonance imaging resorts to either Fourier-domain subsampling or better reconstruction algorithms to deal with fewer measurements while still generating medic…
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…
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…
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…
Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions
Zhishen Huang, Stephen Becker
We consider the problem of finding local minimizers in non-convex and non-smooth optimization. Under the assumption of strict saddle points, positive results have been derived for…