no code implementations • 19 Nov 2023 • Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar
In our experiments, we study combinations of supervised deep network reconstructors and MBIR solver with learned sparse representation-based priors or analytical priors.
1 code implementation • 28 Aug 2023 • Zhishen Huang
Accelerated magnetic resonance imaging resorts to either Fourier-domain subsampling or better reconstruction algorithms to deal with fewer measurements while still generating medical images of high quality.
no code implementations • 19 May 2022 • Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar
Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise.
no code implementations • 22 Mar 2022 • Xikai Yang, Zhishen Huang, Yong Long, Saiprasad Ravishankar
In this study, we propose a network-structured sparsifying transform learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning.
1 code implementation • 17 Nov 2021 • Zhishen Huang, Saiprasad Ravishankar
There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements.
1 code implementation • 28 Oct 2021 • Zhishen Huang, Marc Klasky, Trevor Wilcox, Saiprasad Ravishankar
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications.
no code implementations • 26 Mar 2021 • Zhishen Huang, Siqi Ye, Michael T. McCann, Saiprasad Ravishankar
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.
no code implementations • 12 Feb 2021 • Zhishen Huang, Stephen Becker
Stochastic gradient Langevin dynamics (SGLD) has gained the attention of optimization researchers due to its global optimization properties.
no code implementations • 21 Jul 2020 • 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 graph sparsification.
no code implementations • 24 Jan 2019 • Zhishen Huang, Stephen Becker
We consider the problem of finding local minimizers in non-convex and non-smooth optimization.