New Preprint: “DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction”. We present DOLCE, a new deep modelbased framework for LACT that uses a conditional diffusion model as an image prior.
About me
I am a fourth-year Ph.D student in the Computational Imaging Group (CIG) from ESE Department at Washington University in St. Louis (WashU), advised by Professor Ulugbek S. Kamilov. Before starting the Ph.D, I received the B.Sc. degree in electronic and information engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2017, and the M.S. degree in electrical and engineering, in 2018, from Washington University in St. Louis, St. Louis, MO, USA. I'm currently pursuing the Ph.D. degree with the Computational Imaging Group. My research interests include inverse problems, deep learning, and computational imaging. Feel free to contact me if you are interested in our research !
News
2022
In Press: Our paper “Online Deep Equilibrium Learning for Regularization by Denoising” has been accepted at NeurIPS 2022! We proposed a new online deep equilibrium learning framework for data-intensive imaging modalities based on implicit neural network.
2021
TCI Accepted: Our paper “CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems” has been on the IEEE Transactions on Computational Imaging (TCI)!! It extends coordinate-based representation to computational imaging. Here is a short video summary.
NeurIPS Accepted: Our paper “Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition” has been accepted at NeurIPS 2021! It establishes theoretical recovery guarantees for PnP/RED in compressive sensing.
LANL Research: Extremely happy to share that I will join LANL as a summer intern in 2021!
TCI Accepted: Our paper “SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees” has been accepted on the IEEE Transactions on Computational Imaging (TCI)!
ICLR Accepted: Our paper “Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors” has been accepted to International Coference on Learning Represetations (ICLR) as spotlight.
2020
JSTSP Accepted: Our paper “SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors” has been published on the IEEE Journal of Selected Topics in Signal Processing (JSTSP)!
JSTSP Accepted: Our paper “RARE: image reconstruction using deep priors learned without groundtruth” has been published on the IEEE Journal of Selected Topics in Signal Processing (JSTSP)!