Yu Sun 孙彧

Yu Sun 孙彧

Assistant Professor @ Johns Hopkins University

Publications

2025

 
InverseBench: Benchmarking Plug-and-Play Diffusion Models for Scientific Inverse Problems
Hongkai Zheng, Wenda Chu, Bingliang Zhang, Zihui Wu, Austin Wang, Berthy Feng, Caifeng Zou, Yu Sun, Nikola B. Kovachki, Zachary E. Ross, Yisong Yue, Katie L. Bouman
Spotlight: 587/11500 = 5.1%

2024

 
Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
Zihui Wu, Yu Sun, Yifan Chen, Yisong Yue, Katie L. Bouman
Acceptance rate: 4043/15671 = 25.8%

 
Provable Probabilistic Imaging using Score-Based Generative Priors
Yu Sun, Zihui Wu, Yifan Chen, Berthy T. Feng, Katie L. Bouman

2023

 
Learning Task-Specific Strategies for Accelerated MRI
Zihui Wu, Tianwei Yin, Yu Sun, Robert Frost, Andre van der Kouwe, Adrian V. Dalca, Katie L. Bouman

 
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach
Paul Goyes-Peñafiel, Edwin Vargas, Claudia V. Correa, Yu Sun, U. S. Kamilov Brendt Wohlberg, Henry Arguello,

2022

 
Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields
Renhao Liu*, Yu Sun*, Jiabei Zhu, Lei Tian, U. S. Kamilov

 
CURE: Learning Cross-Video Neural Representations for High-Quality Frame Interpolation
Wentao Shangguan*, Yu Sun*, Weijie Gan, U. S. Kamilov
Acceptance rate: 1492/5803 = 26%

2021

 
Deformation-Compensated Learning for Image Reconstruction without Ground Truth
Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, U. S. Kamilov

 
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, U. S. Kamilov

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, U. S. Kamilov

Joint Reconstruction and Calibration using Regularization by Denoising
Mingyang Xie*, Jiaming Liu*, Yu Sun, Brendt Wohlberg, U. S. Kamilov

Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
Yu Sun, Jiaming Liu, Brendt Wohlberg, U. S. Kamilov
Spotlight: 114/2997 = 4%

2020

 
Scalable Plug-and-Play ADMM with Convergence Guarantees
Yu Sun*, Zihui Wu*, Xiaojian Xu*, Brendt Wohlberg, U. S. Kamilov

A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network
Guangxiao Song, Yu Sun, Jiaming Liu,, Zhijie Wang, U. S. Kamilov

Provable Convergence of Plug-and-Play Priors With MMSE Denoisers
Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, U. S. Kamilov

SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors
Zihui Wu, Yu Sun, Alex Matlock, Jiaming Liu, Lei Tian, U. S. Kamilov

RARE: Image reconstruction using deep priors learned without groundtruth
Jiaming Liu, Yu Sun, Cihat Eldeniz, Weijie Gan, Hongyu An, U. S. Kamilov

Deep learning using a biophysical model for Robust and Accelerated Reconstruction (RoAR) of quantitative and artifact-free R2* images
Max Torop, Satya Kothapalli, Yu Sun, Jiaming Liu, Sayan Kahali, Dmitriy A Yablonskiy, U. S. Kamilov

2019

 
Online Regularization by Denoising with Applications to Phase Retrieval
Zihui Wu, Yu Sun, Jiaming Liu, U. S. Kamilov

Image Restoration Using Total Variation Regularized Deep Image Prior
Jiaming Liu, Yu Sun, Xiaojian Xu, U. S. Kamilov

Regularized Fourier ptychography using an online plug-and-play algorithm
Yu Sun, Brendt Wohlberg, U. S. Kamilov
Oral Presentation

An online Plug-and-play algorithm for regularized image reconstruction
Yu Sun, Brendt Wohlberg, U. S. Kamilov

Block Coordinate Regularization by Denoising
Yu Sun, Jiaming Liu, U. S. Kamilov
Acceptance rate: 1428/6743 = 21%

2018

 
Efficient and accurate inversion of multiple scattering with deep learning
Yu Sun, Zhihao Xia, U. S. Kamilov