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 has been accepted by ICCV 2023!
MERL Research: Extremely happy to share that I will join MERL as a summer intern in 2023!
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.
LLNL Research: Extremely happy to share that I will join LLNL as a summer intern in 2022!
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.
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)!