Xuanyu Tian

I am Xuanyu Tian (田烜宇 in Chinese), a forth-year Ph.D. student in SMILE Lab at ShanghaiTech University (Shanghai, China), advised by Prof. Yuyao Zhang. Before that, I received my B.E. degree at Wuhan University of Technology in 2021.

I am interested in a variety of topics in medical image reconstruction and image restoration. My current research focuses include:

Computational Imaging;
Inverse Problems;
Self-supervised Learning;
Medical Imaging.

See more in the detailed CV.

news

Sep 5, 2024	One paper was accepted by IEEE Transactions on Compuational Imaging (IEEE TCI)
May 14, 2024	One paper was accepted by MICCAI 2024
Feb 3, 2024	One paper was accepted by IEEE ISBI 2024
Mar 2, 2023	One paper was accepted by IEEE ISBI 2023
Sep 18, 2022	One paper was accepted by MICCAI 2022

selected publications

* denotes the co-first author

IEEE TCI

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Zero-Shot Image Denoising for High-Resolution Electron Microscopy

Xuanyu Tian, Zhuoya Dong, Xiyue Li, Yue Gao, Hongjiang Wei, Yanhang Ma, Yu Jingyi, and Yuyao Zhang

IEEE Transactions on Computational Imaging 2024

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High-resolution electron microscopy (HREM) imaging technique is a powerful tool for directly visualizing a broad range of materials in real-space. However, it faces challenges in denoising due to ultra-low signal-to-noise ratio (SNR) and scarce data availability. In this work, we propose Noise2SR, a zero- shot self-supervised learning (ZS-SSL) denoising framework for HREM. Within our framework, we propose a super-resolution (SR) based self-supervised training strategy, incorporating the Random Sub-sampler module. The Random Sub-sampler is designed to generate approximate infinite noisy pairs from a single noisy image, serving as an effective data augmentation in zero-shot denoising. Noise2SR trains the network with paired noisy images of different resolutions, which is conducted via SR strategy. The SR-based training facilitates the network adopting more pixels for supervision, and the random sub-sampling helps compel the network to learn continuous signals enhancing the robustness. Meanwhile, we mitigate the uncertainty caused by random-sampling by adopting minimum mean squared error (MMSE) estimation for the denoised results. With the distinctive integration of training strategy and proposed designs, Noise2SR can achieve superior denoising performance using a single noisy HREM image. We evaluate the performance of Noise2SR in both simulated and real HREM denoising tasks. It outperforms state- of-the-art ZS-SSL methods and achieves comparable denoising performance with supervised methods. The success of Noise2SR suggests its potential for improving the SNR of images in material imaging domains.
ISBI 2023

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Self-supervised High-Dimensional Magnetic Resonance Image Denoising Using Super-resolved Single Noisy Image

Changhao Jiang*, Xuanyu Tian*, Yanbin Li, Jiangjie Wu, Xin Mu, Lei Zhang, and Yuyao Zhang

IEEE International Symposium on Biomedical Imaging 2023

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Denoising of magnetic resonance image (MRI) is a critical step in MRI image processing and analysis. With the advantage of not requiring paired noisy-clean images for training, self-supervised denoising methods are emerging as competitive alternatives to supervised denoising methods in MRI denoising. However, current self-supervised image denoising methods are not effective enough for MRI. In this work, we propose Noise2SR-M (N2SR-M), a self-supervised denoising method for MR images, which is more efficient for high-dimensional MR images. N2SR-M is designed for training with paired noisy data of different sizes divided from a single high-dimensional noisy input image. Our N2SR-M model is able to utilize the redundant information from the additional image dimension to generate noisy image pairs for the denoising task. With the combination of additional dimension constraint and the effectiveness of SR method based training image pair generation, our model is more efficient for denoising high-dimensional MR images. The quantitative and qualitative improvements in blood oxygenation level dependent (BOLD) imaging denoising task demonstrate that N2SRM successfully restores detailed image contents and removes tiny structural noise and artifacts from noise-corrupted high-dimensional MRI. Moreover, the denoised BOLD image also induces more efficient R2* image computation.
MICCAI 2022

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Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image

Xuanyu Tian, Qing Wu, Hongjiang Wei, and Yuyao Zhang

Medical Image Computing and Computer Assisted Intervention 2022

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Fluorescence microscopy is a key driver to promote discoveries of biomedical research. However, with the limitation of microscope hardware and characteristics of the observed samples, the fluorescence microscopy images are susceptible to noise. Recently, a few self-supervised deep learning (DL) denoising methods have been proposed. However, the training efficiency and denoising performance of existing methods are relatively low in real scene noise removal. To address this issue, this paper proposed self-supervised image denoising method Noise2SR (N2SR) to train a simple and effective image denoising model based on single noisy observation. Our Noise2SR denoising model is designed for training with paired noisy images of different dimensions. Benefiting from this training strategy, Noise2SR is more efficiently self-supervised and able to restore more image details from a single noisy observation. Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods. We envision that Noise2SR has the potential to improve more other kind of scientific imaging quality.