76 citations · 122 across the 11 of their papers we have counts for
12 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…
Consistent 3D Hand Reconstruction in Video via self-supervised Learning
Zhigang Tu, Zhisheng Huang, Yujin Chen +4
We present a method for reconstructing accurate and consistent 3D hands from a monocular video. We observe that detected 2D hand keypoints and the image texture provide important c…
Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
Zhishen Huang, Saiprasad Ravishankar
There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements. Often sophisticated reconstruction algorithms are d…
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography
Zhishen Huang, Marc Klasky, Trevor Wilcox +1
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reco…
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…
Stochastic Gradient Langevin Dynamics with Variance Reduction
Zhishen Huang, Stephen Becker
Stochastic gradient Langevin dynamics (SGLD) has gained the attention of optimization researchers due to its global optimization properties. This paper proves an improved convergen…
Spectral estimation from simulations via sketching
Zhishen Huang, Stephen Becker
Sketching is a stochastic dimension reduction method that preserves geometric structures of data and has applications in high-dimensional regression, low rank approximation and gra…
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…
Smoothing of Gaussian quantum dynamics for force detection
Zhishen Huang, Mohan Sarovar
Building on recent work by Gammelmark et al. [Phys. Rev. Lett. 111, 160401 (2013)] we develop a formalism for prediction and retrodiction of Gaussian quantum systems undergoing con…