个人简介

袁鑫博士，2003-2009年西安电子科技大学本硕连读，2009年在雷达信号处理国家重点实验室获得硕士学位；2009-2012年在香港理工大学攻读博士学位，主要从事阵列信号处理方向研究；2012-2015年在美国杜克大学从事博士后研究，主要研究方向为计算成像和机器学习。2015年加入美国新泽西贝尔实验室，担任视频分析与编码首席研究员。2021年秋全职加入西湖大学，担任工学院副教授。

学术成果

袁鑫博士致力于计算成像，包含成像系统的研发和基于机器学习的算法研究，是单次曝光压缩成像 (Snapshot Compressive Imaging) 的主要推动者之一。在该领域顶级期刊上（如SPM、TPAMI、 Cell Patterns、 IJCV、 TIP、Optica、OE、 OL等）发表论文80多篇；在顶级会议上（如CVPR、ICCV、 ECCV、ICML、NIPS）发表论文20多篇；在业内顶级期刊 IEEE Signal Processing Magazine 发表关于SCI的综述文章（IEEE SPM，2021），受邀在Cell子刊 The Innovation上撰写评论文章，重点阐述了中子单像素成像与人工智能相结合取得的突破性进展（The Innovation：Cell Press，2021年3月）。根据谷歌学术统计，论文引用4200多次（截至2021年10月），H指数33；申请国际专利20余项（已授权10项），其中多项专利已进行产业孵化。

代表论文（*代表通信作者）

1. X. Yuan*^#, Y. Liu^#, J. Suo, F. Durand and Q. Dai, “Plug-and-Play Algorithms for Video Snapshot Compressive Imaging,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.

2. R. Lu, B. Chen*, G. Liu, Z. Cheng, M. Qiao and X. Yuan*, “Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network,” International Journal of Computer Vision (IJCV), 2021.

3. Z. Meng, Z. Yu, K. Xu and X. Yuan*, “Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging,” IEEE/CVF International Conference on Computer Vision (ICCV), 2021.

4. X. Li, J. Suo, W. Zhang, X. Yuan, and Q. Dai, “Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution,” IEEE/CVF International Conference on Computer Vision (ICCV), 2021.

5. M. Qiao, Y. Sun, J. Ma, Z. Meng, X. Liu and X. Yuan*, “Snapshot Coherence Tomographic Imaging” IEEE Transactions on Computational Imaging, 2021.

6. Z. Zha, B. Wen*, X. Yuan, J. T. Zhou, J. Zhou and C. Zhu, “Triply Complementary Priors for Image Restoration,” IEEE Transactions on Image Processing, 2021.

7. Z. Zha, X. Yuan, B. Wen, J. Zhang and C. Zhu, “Non-Convex Structural Sparsity Residual Constraint for Image Restoration,” IEEE Transactions on Cybernetics, 2021.

8. S. Zheng, C. Wang, X. Yuan* and H. Xin*, “Super-compression of large electron microscopy time-series by deep compressive sensing learning,” Cell Patterns, 2021.

9. X. Yuan* and S. Han, “Single-Pixel Neutron Imaging with Artificial Intelligence: Breaking the Barrier in Multi-Parameter Imaging, Sensitivity and Spatial Resolution,” The Innovation: Cell Press, 2021.

10. Z. Cheng, B. Chen*, G. Liu, H. Zhang, R. Lu, Z. Wang and X. Yuan*, “Memory-Efficient Network for Large-scale Video Compressive Sensing,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

11. Z. Wang, H. Zhang, Z, Cheng, B. Chen* and X. Yuan*, “Meta SCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

12. T. Huang, W. Dong*, X. Yuan*, J. Wu and G. Shi, “Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

13. Z. Zha, X. Yuan, B. Wen, J. Zhang and C. Zhu, “Non-Convex Structural Sparsity Residual Constraint for Image Restoration,” IEEE Transactions on Cybernetics, 2021.

14. M. Qiao, X. Liu and X. Yuan*, “Snapshot Temporal Compressive Microscopy Using an Iterative Algorithm with Untrained Deep Neural Networks,” Optics Letters, 2021.

15. X. Yuan*, D. Brady, and A. Katsaggelos, “Snapshot Compressive Imaging: Theory, Algorithms and Applications,” IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 65-88, March 2021.

16. Z. Zha, B. Wen, X. Yuan, J. Zhou, C. Zhu and A. C. Kot, “A Hybrid Structural Sparsification Error Model for Image Restoration," IEEE Transactions on Neural Networks and Learning Systems, 2021.

17. S. Zheng, Y. Liu, Z. Meng, M. Qiao, Z. Tong, X. Yang, S. Han and X. Yuan*, “Deep Plug-and-Play Priors for Spectral Snapshot Compressive Imaging," Photonics Research, vol. 9, B18-B29, 2021.

18. S. Lu, X. Yuan and W. Shi, “An Integrated Framework for Compressive Imaging Processing on CAVs,” The Fifth ACM/IEEE Symposium on Edge Computing (SEC), San Jose, CA, USA, November 2020.

19. Z. Meng, J. Ma, X. Yuan*, “End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention,” European Conference on Computer Vision (ECCV), 2020.

20. Z. Cheng, R. Lu, Z. Wang, H. Zhang, B. Chen*, Z. Meng, X. Yuan*, “BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging,” European Conference on Computer Vision (ECCV), 2020.

21. X. Yuan, Y. Liu, J. Suo and Q. Dai, “Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, June 2020.

22. Q. Xu^#, X. Yuan^#, and C. Ouyang, “Class-aware Domain Adaptation for Semantic Segmentation of Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing, 2020.

23. Z. Meng, M. Qiao, J. Ma, Z. Yu, K. Xu, and X. Yuan*, “Snapshot Multispectral Endomicroscopy”, Optics Letters, vol. 45, issue 4. pp. 3897-3900, 2020. DOI: 10.1364/OL.393213.

24. Z. Zha, X. Yuan, J. Zhou, C. Zhu and B. Wen, “Image Restoration via Simultaneous Nonlocal Self-Similarity Priors,” IEEE Transactions on Image Processing, vol. 29, pp. 8561-8576, 2020.

25. Z. Zha^#, X. Yuan^#, B. Wen, J. Zhang, J. Zhou and C. Zhu, “Image Restoration Using Joint Patch-Group Based Sparse Representation,” IEEE Transactions on Image Processing, vol. 29, pp. 7735-7750, 2020.

26. M. Qiao, Z. Meng, J. Ma and X Yuan*, “Deep Learning for Video Compressive Sensing," APL Photonics (Invited paper for Special Topic: Photonics and AI), vol.5, Issue 3, 2020.  Selected as the feature article and reported by Scilight “Deep learning speeds up video compressive sensing from days to minutes” at: https://doi.org/10.1063/10.0000928.

27. M. Qiao, X. Liu and X Yuan*, “Snapshot spatial-temporal compressive imaging," Optics Letters, vol 45, pp. 1659-1662, 2020.

28. X Yuan and R. Haimi-Cohen, “Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG," IEEE Transactions on Multimedia, vol. 22, no. 11, pp. 2889-2904, Nov. 2020.

29. Z. Zha, X Yuan, B. Wen, J. Zhou, J. Zhang and C. Zhu, “A Benchmark for Sparse Coding: When Group Sparsity Meets Rank Minimization,” IEEE Transactions on Image Processing, vol. 29, pp. 5094-5109, 2020.

30. Z. Zha, X Yuan, B. Wen, J. Zhou, J. Zhang and C. Zhu,” From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration," IEEE Transactions on Image Processing, vol. 29, pp. 3254-3269, 2020.

31. J. Ma, X-Y. Liu, Z. Shou and X. Yuan, “Deep Tensor ADMM-Net for Snapshot Compressive Imaging," IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019.

32. X. Miao, X. Yuan*, Y. Pu and V. Athitsos, “Lambda-net: Reconstruct Hyperepsectral Images from a Snapshot Measurement," IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, October 2019.

33. S. Jalali and X Yuan, “Snapshot Compressed Sensing: Performance Bounds and Algorithms," IEEE Transactions on Information Theory, vol. 65, no. 12, pp. 8005-8024, Dec. 2019.

34. Y. Liu^#, X. Yuan^#, J. Suo, D. Brady and Q. Dai, “Rank Minimization for Snapshot Compressive Imaging”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 12, pp. 2990-3006, 1 Dec. 2019.

35. X. Zhang, X. Yuan* and L. Carin, “Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, Utah, USA, June 2018.

36. Y. Pu, Z. Gan, R. Henao, X. Yuan, C. Li, A. Stevens and L. Carin, "Variational Autoencoder for Deep Learning of Images, Labels and Captions," Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.

37. Y. Pu, X. Yuan, A. Stevens, C. Li and L. Carin, "A Deep Generative Deconvolutional Image Model," International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, May 2016.

38. X. Yuan, R. Henao, E. L. Tsalik and L. Carin, "Non-Gaussian Discriminative Factor Models via the Max-Margin Rank Likelihood", International Conference on Machine Learning (ICML), Lille, France, July 2015.

39. P. Llull, X. Yuan, L. Carin, and D. J. Brady, “Image Translation for Single-Shot Focal Tomography,” Optica, vol. 2, Issue 9, pp. 822-825, 2015.

40. R. Henao, X. Yuan and L. Carin, "Bayesian Nonlinear Support Vector Machines and Supervised Factor Modeling," Neural Information Processing Systems (NIPS), Montreal, Canada, December 2014.

41. X. Yuan, P. Llull, X. Liao, J. Yang, G. Sapiro, D. J. Brady, and L. Carin, "Low-Cost Compressive Sensing for Color Video and Depth," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, USA, June 2014.

联系方式

电子邮箱：xylab@westlake.edu.cn

袁鑫课题组致力于计算成像，包含成像系统的研发和基于人工智能的算法研究。代表性成像系统有：高速视频、高光谱、大视场、高速三维以及相干高速压缩成像等。算法研究包括：基于深度学习的高光谱、高速视频重建，基于元学习、目标检测和识别的自适应信息重构、以及基于增强学习的自适应成像系统的研发。同时致力于各种图像和视频的压缩、恢复、增强等逆问题研究， 基于贝叶斯统计模型的自适应学习和强化学习等方向的研究。课题组长期招博士后，科研助理，访问学生等，待遇从优。

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