Weijie Gan 甘伟捷

I am a 5th year PhD student in Computational Imaging Group (CIG) from Computer Science and Engineering Department at Washington University in St. Louis (WashU), supervised by Prof. Ulugbek Kamilov and Prof. Hongyu An. Before starting my PhD, I received the M.Sc. degree in Computer Science, in 2020, also from WashU, and the B.Sc. degree in Automation from South China University of Technology, Guangzhou, China, in 2018.

My research interests are computational imaging, machine learning, computer vision, inverse problems, optimization and medical imaging.

My research focuses on algorithms designs and theoretical analysis for deep learning-based computational imaging. My work lies at the intersection of computational imaging, computer vision, machine learning, optimization, image processing, and physics. I am passionate about incorporating cross-domain knowledge to develop the state-of-the-art computer vision and generative AI models to solve the real-world challenging problems for various imaging systems as well as establishing theoretical analysis. My research topics include image reconstruction/restoration, inverse problems, diffusion models, image registration, self-supervised learning and correction of physical model uncertainty.

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2024/03: Our paper SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction has been accepted to MRM.
2024/02: Our paper PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction has been accepted to IEEE OJSP.
2024/01: Our paper A Plug-and-PlayImage Registration Network has been accepted to ICLR 2024.
2023/11: Two new preprints: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging and A Structured Pruning Algorithm for Model-based Deep Learning.
2023/10: New preprint: PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction.
2023/10: New preprint: A Plug-and-PlayImage Registration Network.
2023/09: Our BC-PnP paper got accepted to NeurIPS 2023.
2023/07: Our SelfDEQ paper got accepted to IEEE TCI.
2023/05: Excited to share that I will join Siemens Healthineers, Knoxville, TN, as a summer research intern in 2023, supervised by Dr. Jorge Cabello and Dr. Maurizio Conti.
2022/05: Excited to share that I will join Los Alamos National Lab (LANL), Los Alamos, NM, as a summer research intern in 2022, supervised by Dr. Brendt Wohlberg.

My complete publications are available at google scholar. Representative papers are highlighted. (* denotes co-first authors)

A SOTA diffusion model for 3D ultra low dose whole-body PET denoising.

A novel application of network pruning for efficient deep model-based networks.

We proposed a new ptychographic image reconstruction algorithm based on vision transformer and deep unfolding.

The first plug-and-play method for image registration that uses a pre-trained denoiser as the prior information.

The first self-supervised method for automatic coil sensitivity calibration in MRI.

A block-coordinate PnP method with theoretical analysis for blind inverse problems.

First to propose self-supervised learning for deep equilibrium model with theoretical guarantees.

Joint image reconstruction and image registration without any ground-truth supervision.

First to propose a video frame interpolation algorithm based on neural field that can achieve SOTA performance.

Propose a new deep learning method to boost MRI results without requiring clean training data.

Exploit statistical priors specified by artifact-removing CNNs trained without groundtruth.

Proposed method with practical GUI tool to detect and track single micro-tubule movement in scan video. The tools also record the growth of target microtubule during video.

Designed a an auto-run smart car, which could recognize a pure white road with black borders by a specifics cam-era(Linear CCD) captured only one-row data(single dimension).

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