221 citations · 1.5k across the 104 of their papers we have counts for
9 papers · 1 filter
Proximal Newton Methods for X-Ray Imaging with Non-Smooth Regularization
Tao Ge, Umberto Villa, Ulugbek S. Kamilov +1
Non-smooth regularization is widely used in image reconstruction to eliminate the noise while preserving subtle image structures. In this work, we investigate the use of proximal N…
Deep learning using a biophysical model for Robust and Accelerated Reconstruction (RoAR) of quantitative and artifact-free R2* images
Max Torop, Satya VVN Kothapalli, Yu Sun +4
Purpose: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* maps from multi-gradient reca…
RARE: Image Reconstruction using Deep Priors Learned without Ground Truth
Jiaming Liu, Yu Sun, Cihat Eldeniz +3
Regularization by denoising (RED) is an image reconstruction framework that uses an image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED with le…
SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors
Zihui Wu, Yu Sun, Alex Matlock +3
Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large d…
Infusing Learned Priors into Model-Based Multispectral Imaging
Jiaming Liu, Yu Sun, Ulugbek S. Kamilov
We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements. Unlike traditional approaches, the proposed algorithm regul…
Online Regularization by Denoising with Applications to Phase Retrieval
Zihui Wu, Yu Sun, Jiaming Liu +1
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability…