Research Projects
2025 Summer
- Enhancing Brian MRI using Diffusion Model: MRI has become an indispensable tool for measuring the blood flow in the brain for diagnosis (e.g. stroke). However, current scan suffers from low SNR and long acquisition time. The goal of this project is to develop novel algorithms to enhance the MRI scan quality.
Status: reserved (Haoyue Guan)
- Motion Compensated MRI reconstruction: MRI often requires long acquisition time, which can suffer artifacts caused by patient motion. This project aims to investigate computational algorithms to compensate motion artifacts while reconstructing high-quality images.
Status: available
- Flow Matching for Super-Resolution in Optics: Flow matching is a novel generative model that outperforms diffusion models. This project aims to explore the use of flow matching for super-resolving optical images.
Status: reserved (Yetao He)
- Normalizing Flow for Imaging Inverse Problem: Normalizing flow is a powerful variational generative model. This project aims to explore the use of NF for performing posterior sampling in the context of inverse problems.
Status: available
2024 Fall
Neural Inverse Scattering with Score-based Regularization (Yuan Gao, Yu Sun)
Blind Deblurring with Application to Optometry (Guannan He, Yuanyun Hu, Yu Sun)
Exploration of Semantic Uncertainty in Diffusion Models (Xinyuan Shao, Yu Sun)
Design of Efficient Plug-and-Play Algorithms (Bingyan Liang, XInmin Shen, Yu Sun)
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Electrical and Computer Engineering
Department
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