56 citations · 65 across the 4 of their papers we have counts for
11 papers
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
Yu Sun, Jiaming Liu, Yiran Sun +2
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-…
Deep Image Reconstruction using Unregistered Measurements without Groundtruth
Weijie Gan, Yu Sun, Cihat Eldeniz +3
One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth…
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Xiaojian Xu, Yu Sun, Jiaming Liu +2
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser. While PnP algorithms are well understood for de…
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling
Xiaojian Xu, Jiaming Liu, Yu Sun +2
Plug-and-play priors (PnP) is an image reconstruction framework that uses an image denoiser as an imaging prior. Unlike traditional regularized inversion, PnP does not require the…
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…
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…