56 citations · 121 across the 17 of their papers we have counts for
15 papers · 1 filter
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…
EATEN: Entity-aware Attention for Single Shot Visual Text Extraction
He guo, Xiameng Qin, Jiaming Liu +3
Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical 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…
Disentangled Image Matting
Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan +6
Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.…