56 citations · 119 across the 14 of their papers we have counts for
14 papers · 1 filter
Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
Weijie Gan, Cihat Eldeniz, Jiaming Liu +3
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors. Our prior…
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
Yu Sun, Jiaming Liu, Mingyang Xie +2
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements. Unlike traditional DL methods that l…
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Jiaming Liu, Yu Sun, Weijie Gan +3
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems. However, the computational and memory complexity of data-consistency lay…
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…
Practical Deep Raw Image Denoising on Mobile Devices
Yuzhi Wang, Haibin Huang, Qin Xu +3
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks ar…
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-…