World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
61
Citations
12685
World Ranking
3117
National Ranking
54

Overview

Xin Yuan is affiliated with Nanyang Technological University in Singapore and has contributed extensively to research in engineering and computer science domains. Their work predominantly centers on computational imaging, signal processing, and related technological applications.

Their research focuses on key areas including sparse and compressive sensing techniques, photoacoustic and ultrasonic imaging, image and signal denoising methods, advanced image processing techniques, medical imaging techniques and applications, advanced image fusion techniques, and advanced MRI techniques and applications.

Xin Yuan has authored influential papers, such as:

  • Snapshot Compressive Imaging: Theory, Algorithms, and Applications (2021, IEEE Signal Processing Magazine)
  • Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition)
  • Deep learning for video compressive sensing (2020, APL Photonics)
  • HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging (2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition)
  • Image Restoration via Simultaneous Nonlocal Self-Similarity Priors (2020, IEEE Transactions on Image Processing)

Frequent co-authors collaborating with Xin Yuan include:

  • Yulun Zhang
  • Ziyi Meng
  • Ce Zhu
  • Zhiyuan Zha
  • Bihan Wen

Xin Yuan's research has been published regularly in notable venues, including:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Optics Letters
  • IEEE Transactions on Image Processing
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

The scientist's work engages deeply with computer vision and pattern recognition, biomedical engineering, computational mechanics, radiology, nuclear medicine and imaging, and media technology, reflecting interdisciplinary approaches to advanced imaging and computational methodologies.

Best Publications

  • Variational autoencoder for deep learning of images, labels and captions

    Yunchen Pu;Zhe Gan;Ricardo Henao;Xin Yuan

  • Coded aperture compressive temporal imaging

    Patrick Llull;Xuejun Liao;Xin Yuan;Jianbo Yang

  • Rank Minimization for Snapshot Compressive Imaging

    Yang Liu;Xin Yuan;Jinli Suo;David J. Brady

  • Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction

    Unknown

  • Generalized alternating projection based total variation minimization for compressive sensing

    Xin Yuan

  • Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications.

    Xin Yuan;David J. Brady;Aggelos K. Katsaggelos

  • Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

    Shaohui Mei;Xin Yuan;Jingyu Ji;Yifan Zhang

  • Variational Autoencoder for Deep Learning of Images, Labels and Captions

    Yunchen Pu;Zhe Gan;Ricardo Henao;Xin Yuan

  • Computational Snapshot Multispectral Cameras: Toward dynamic capture of the spectral world

    Xun Cao;Tao Yue;Xing Lin;Stephen Lin

  • HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging

    Unknown

  • Video compressive sensing using Gaussian mixture models.

    Jianbo Yang;Xin Yuan;Xuejun Liao;Patrick Llull

  • Compressive Sensing by Learning a Gaussian Mixture Model From Measurements

    Jianbo Yang;Xuejun Liao;Xin Yuan;Patrick Llull

  • Compressive Hyperspectral Imaging With Side Information

    Xin Yuan;Tsung-Han Tsai;Ruoyu Zhu;Patrick Llull

  • lambda-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement

    Xin Miao;Xin Yuan;Yunchen Pu;Vassilis Athitsos

  • Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging

    Xin Yuan;Yang Liu;Jinli Suo;Qionghai Dai

  • End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention

    Ziyi Meng;Jiawei Ma;Xin Yuan

  • “Vector Cross-Product Direction-Finding” With an Electromagnetic Vector-Sensor of Six Orthogonally Oriented But Spatially Noncollocating Dipoles/Loops

    Kainam Thomas Wong;Xin Yuan

  • Deep Tensor ADMM-Net for Snapshot Compressive Imaging

    Jiawei Ma;Xiao-Yang Liu;Zheng Shou;Xin Yuan

  • Deep learning for video compressive sensing

    Mu Qiao;Ziyi Meng;Ziyi Meng;Jiawei Ma;Xin Yuan

  • Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

    Tao Huang;Weisheng Dong;Xin Yuan;Jinjian Wu

  • Image Restoration via Simultaneous Nonlocal Self-Similarity Priors

    Zhiyuan Zha;Xin Yuan;Jiantao Zhou;Ce Zhu

  • Low-Cost Compressive Sensing for Color Video and Depth

    Xin Yuan;Patrick Llull;Xuejun Liao;Jianbo Yang

  • Spectral-temporal compressive imaging

    Tsung Han Tsai;Patrick Llull;Xin Yuan;Lawrence Carin

Frequent Co-Authors

Lawrence Carin
Lawrence Carin Duke University
David J. Brady
David J. Brady University of Arizona
Ce Zhu
Ce Zhu University of Electronic Science and Technology of China
Jiantao Zhou
Jiantao Zhou University of Macau
Guillermo Sapiro
Guillermo Sapiro Princeton University
Qionghai Dai
Qionghai Dai Tsinghua University
Jinli Suo
Jinli Suo Tsinghua University
Christopher H. T. Lee
Christopher H. T. Lee Nanyang Technological University
Bo Chen
Bo Chen Xidian University
Miguel R. D. Rodrigues
Miguel R. D. Rodrigues University College London

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