Yipeng Sun

Lehrstuhl für Informatik 5 (Mustererkennung)

Research associates

Address

Martensstraße 3
91058 Erlangen

Contact

yipeng.sun@fau.de
+49 9131 85-27775
lme.tf.fau.de/

Yipeng Sun

  • Since 07/2023:
    Ph.D. Student at Pattern Recognition Lab, FAU Erlangen-Nürnberg
  • 10/2020 – 06/2023:
    M.Sc. Medical Engineering, FAU Erlangen-Nürnberg
  • 09/2015 – 07/2019:
    B.Eng. Measurement & Control Technology & Instrument, Nanjing University of Science and Technology

2023

  • An AI-based framework for visualizing and analyzing massive amounts of 4D tomography data for beamline end users

    (Third Party Funds Group – Overall project)

    Project leader: ,
    Term: March 1, 2023 - February 28, 2026
    Acronym: KI4D4E
    Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
    URL: https://foerderportal.bund.de/foekat/jsp/SucheAction.do?actionMode=view&fkz=05D23WE1
    Abstract

    Synchrotron tomography is characterized by extremely brilliant X-rays, which enables almost artifact-free imaging. Furthermore, very high resolution can be achieved by using special X-ray optics, and the special design of synchrotron facilities also allows fast in-situ experiments, i.e. 4D tomography.  The combination of these features enables high-resolution computed tomography on objects where conventional laboratory CT fails. At the same time, however, this also produces enormous amounts of data that are generally unprocessable by end users, pushing even the operators of synchrotrons to their limits.

    The goal of the KI4D4E project is to develop AI-based methods that can be used by end users to process the enormous amounts of data in such 4D CT measurements. This includes improving image quality through artifact reduction, reduction and accessibility of data to end users to help the latter interpret the results.

    The project focuses on the topics of artifact reduction, segmentation and visualization of large 4D data sets. The resulting methods should be applicable to data from both photon and neutron sources.

2026

Journal Articles

Conference Contributions

2025

Journal Articles

Conference Contributions

2024

Conference Contributions

2023

Conference Contributions

No awards found.

Current Theses & Projects

Title Type Student Period Status
Robust CT image restoration based on evolutionary algorithms MA thesis Ziqing Lu Mar 2026 – Sep 2026 running
Autoregressive Model Based on the Hilbert Curve for CT Artifact Removal MA thesis Debadrita Mukherjee Jan 2026 – Jun 2026 running
A Cascaded Encoder–Decoder Network for CT Image Restoration MA thesis Mohammad Ulla Shiblu Nov 2025 – May 2026 running

Completed Theses & Projects

Title Type Student Period Status
Evolving Universal Datasets: Cross-Architecture Generalization via Evolutionary Distillation MA thesis Shouqiang Wu Sep 2025 – Mar 2026 finished
A Reasoning Agent for Chest X-ray with Memory MA thesis Yipeng Zhang Aug 2025 – Feb 2026 finished
Diffusion Transformer for CT artifacts compensation MA thesis Ziye Wang May 2025 – Nov 2025 finished
CT Field-of-View Extension Dataset Simulation Project Qianxin Wang Oct 2024 – Mar 2025 finished
Category-Level Segmentation of industrial Parts Using SAM2 Memory System MA thesis Jiayi Wang Feb 2025 – Jul 2025 finished
Frequency Domain Hierarchical Vision Transformer-based Perceptual Loss Project finished
Universal Image Artifact Reduction via Heterogeneous Mixture of Experts MA thesis Hanqing Liu Nov 2024 – May 2025 finished
Artifacts Simulation in CT Images Project Ziye Wang May 2024 – Nov 2024 finished
Diffusion Model-Enabled Energy Level Transformation in Photon Counting Computed Tomography (PCCT) Project finished
A Comparative Analysis of Loss Functions in Deep Learning-Based Inverse Problems Project Hongrun Dong Feb 2024 finished
Deep Learning Computed Tomography based on the Defrise and Clack Algorithm for Specific CBCT Orbits MA thesis Chengze Ye Dec 2023 – May 2024 finished
Investigating the Possibilities of CT Reconstruction using Fourier Neural Operator Project finished