56 citations · 89 across the 7 of their papers we have counts for
6 papers · 1 filter
Joint Reconstruction and Calibration using Regularization by Denoising
Mingyang Xie, Yu Sun, Jiaming Liu +2
Regularization by denoising (RED) is a broadly applicable framework for solving inverse problems by using priors specified as denoisers. While RED has been shown to provide state-o…
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…
Scalable Plug-and-Play ADMM with Convergence Guarantees
Yu Sun, Zihui Wu, Xiaojian Xu +2
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the st…
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…