Biography

Dr. Xin Yuan received his B.Eng. and M.Eng. degrees from Xidian University, in 2007 and 2009, respectively, and his Ph.D. degree from The Hong Kong Polytechnic University in 2012. From 2012 to 2015, he had been a Postdoctoral Associate with the Department of Electrical and Computer Engineering, Duke University, where he was working on compressive sensing and machine learning. From 2015 to 2021, he was a Video Analysis and Coding Lead Researcher at Bell Labs, Murray Hill, NJ 07974, USA. He had received several best paper awards in international conferences. He has been the Associate Editor of Pattern Recognition since 2019. He is the leading guest editor of the special issue of “Deep Learning for High Dimensional Sensing” in the IEEE Journal of Selective Topics in Signal Processing (2021). Dr. Yuan Joined the Westlake University in 2021 as an Associate Professor in School of Engineering.

Research

Dr. Xin Yuan has been working on computational imaging since 2012, which includes the hardware system design (usually using optics, please refer to papers published in CVPR, ICCV, ECCV, Optica, Optics Letters, Optics Express and APL Photonics), algorithm development including optimization-based algorithms (please refer to papers published in IEEE T-PAMI, T-IP, T-SP and IJCV) and deep-learning-based algorithms (please refer to papers published in CVPR, ICCV and ECCV). Furthermore, Dr. Yuan works with colleagues on the theoretical derivation of computational imaging systems (please refer to papers published in IEEE T-IT). Dr. Yuan also works on machine learning models for other data analysis (please refer to papers published in ICML, NIPS and AISTATS).

Currently, Dr. Yuan is working on the Snapshot Compressive Imaging (refer to the review paper published in IEEE Signal Processing Magazine entitled “Snapshot Compressive Imaging: Theory, Algorithms and Applications” doi: 10.1109/MSP.2020.3023869), also known as the SCI. SCI uses a two-dimensional (2D) detector to capture high-dimensional (HD, i.e., 3D or larger) data in a snapshot measurement. Via novel optical designs, the 2D detector samples the HD data in a compressive manner; following this, algorithms are employed to reconstruct the desired HD data-cube. SCI has been used in hyperspectral imaging, video, holography, tomography, focal depth imaging, polarization imaging, microscopy, etc. Though the hardware has been investigated for more than a decade, the theoretical guarantees have only recently been derived in 2019. Inspired by deep learning, various deep neural networks have also been developed to reconstruct the HD data-cube in spectral SCI and video SCI. Dr. Yuan and his collaborators are leading the state-of-the-art SCI reconstruction algorithms, both in optimization (T-PAMI 2019, 2021) and deep learning (CVPR 2020, 2021, ECCV 2020 and ICCV 2021).

SCI Lab currently offers multiple Postdoctoral, Ph.D. and Research Assistant positions. SCI Lab welcomes highly self-motivated applicants. For student who have enrolled in other universities but filled with brilliant ideas and strong willingness to work in Computational Imaging, we highly encourage you to apply as a visiting student. 

Representative Publications

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.