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，是国际上单曝光压缩成像 (Snapshot Compressive Imaging) 的主要推动者。在该领域顶级期刊上（如Nature Communications、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、NeurIPS）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）。根据谷歌学术统计，论文引用近8000次，H指数48；申请国际专利20余项（已授权9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. Zhang, W., Suo, J., Dong, K., Li, L., Yuan, X., Pei, C., & Dai, Q. (2023). Handheld snapshot multi-spectral camera at tens-of-megapixel resolution. Nature Communications, 14(1), 5043.

2. Xu, P., Liu, L., Zheng, H., Yuan, X., Xu, C., & Xue, L. (2023). Degradation-aware Dynamic Fourier-based Network For Spectral Compressive Imaging. IEEE Transactions on Multimedia.

3. Meng, Z., Yuan, X., & Jalali, S. (2023). Deep Unfolding for Snapshot Compressive Imaging. International Journal of Computer Vision, 1-26.

4.  Zhao, Y., Zheng, S., & Yuan, X*. (2023, June). Deep Equilibrium Models for Snapshot Compressive Imaging. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 3, pp. 3642-3650).

5.  Zha, Z., Wen, B., Yuan, X., Zhang, J., Zhou, J., Lu, Y., & Zhu, C. (2023). Non-Local Structured Sparsity Regularization Modeling for Hyperspectral Image Denoising. IEEE Transactions on Geoscience and Remote Sensing.

6. Luo, T., Wang, L., & Yuan, X*. (2023). Grating-based coded aperture compressive spectral imaging to reconstruct over 190 spectral bands from a snapshot measurement. Journal of Physics D: Applied Physics, 56(25), 254004.

7. Huang, T., Yuan, X., Dong, W., Wu, J., & Shi, G. (2023). Deep Gaussian Scale Mixture Prior for Image Reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence.

8. Xue, Y., Su, X., Zhang, S., & Yuan, X*. (2023). Optical implementation and robustness validation for multi-scale masked autoencoder. APL Photonics, 8(4).

9. Wu, Z., Yang, C., Su, X., & Yuan, X*. (2023). Adaptive deep pnp algorithm for video snapshot compressive imaging. International Journal of Computer Vision, 1-18.

10. Zha, Z., Wen, B., Yuan, X., Zhang, J., Zhou, J., Jiang, X., & Zhu, C. (2023). Multiple complementary priors for multispectral image compressive sensing reconstruction. IEEE Transactions on Cybernetics.

11. Xu, Q., Shi, Y., Yuan, X., & Zhu, X. X. (2023). Universal Domain Adaptation for Remote Sensing Image Scene Classification. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15.

12. Zha, Z., Wen, B., Yuan, X., Ravishankar, S., Zhou, J., & Zhu, C. (2023). Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling. IEEE Signal Processing Magazine, 40(1), 32-44.

13. Wang, L., Cao, M., & Yuan, X*. (2023). Efficientsci: Densely connected network with space-time factorization for large-scale video snapshot compressive imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 18477-18486).

14. Qiao, M., & Yuan, X*. (2023). Coded aperture compressive temporal imaging using complementary codes and untrained neural networks for high-quality reconstruction. Optics Letters, 48(1), 109-112.

15. Yu, Z., Liu, D., Cheng, L., Meng, Z., Zhao, Z., Yuan, X., & Xu, K. (2022). Deep learning enabled reflective coded aperture snapshot spectral imaging. Optics Express, 30(26), 46822-46837.

16. Zhang, Z., Zhang, B., Yuan, X., Zheng, S., Su, X., Suo, J., ... & Dai, Q. (2022). From compressive sampling to compressive tasking: Retrieving semantics in compressed domain with low bandwidth. PhotoniX, 3(1), 1-22.

17. Chen, Z., Zhang, Y., Gu, J., Kong, L., & Yuan, X*. (2022). Cross Aggregation Transformer for Image Restoration. Advances in Neural Information Processing Systems, 35, 25478-25490.

18. Wang, J., Zhang, Y., Yuan, X., Meng, Z., & Tao, Z. (2022, October). Modeling mask uncertainty in hyperspectral image reconstruction. In European Conference on Computer Vision (pp. 112-129). Cham: Springer Nature Switzerland.

19. Yang, C., Zhang, S., & Yuan, X*. (2022, October). Ensemble learning priors driven deep unfolding for scalable video snapshot compressive imaging. In European Conference on Computer Vision (pp. 600-618). Cham: Springer Nature Switzerland.

20. Zhang, J., Zhang, Y., Gu, J., Zhang, Y., Kong, L., & Yuan, X. (2022, September). Accurate Image Restoration with Attention Retractable Transformer. In The Eleventh International Conference on Learning Representations.

21. Cheng, S., Zhang, Y., Li, X., Yang, L., Yuan, X., & Li, S. Z. (2022). Roadmap toward the metaverse: An AI perspective. The Innovation, 3(5).

22. Wang, L., Cao, M., Zhong, Y., & Yuan, X*. (2022). Spatial-temporal transformer for video snapshot compressive imaging. IEEE Transactions on Pattern Analysis and Machine Intelligence.

23. Wang, L., Wu, Z., Zhong, Y., & Yuan, X*. (2022). Snapshot spectral compressive imaging reconstruction using convolution and contextual transformer. Photonics Research, 10(8), 1848-1858.

24. Xu, Q., Ouyang, C., Jiang, T., Yuan, X., Fan, X., & Cheng, D. (2022). MFFENet and ADANet: a robust deep transfer learning method and its application in high precision and fast cross-scene recognition of earthquake-induced landslides. Landslides, 19(7), 1617-1647.

25. Chen, Z., Zheng, S., Tong, Z., & Yuan, X*. (2022). Physics-driven deep learning enables temporal compressive coherent diffraction imaging. Optica, 9(6), 677-680.

26. Cai, Y., Lin, J., Wang, H., Yuan, X., Ding, H., Zhang, Y., ... & Gool, L. V. (2022). Degradation-aware unfolding half-shuffle transformer for spectral compressive imaging. Advances in Neural Information Processing Systems, 35, 37749-37761.

27. Zhang, B., Yuan, X., Deng, C., Zhang, Z., Suo, J., & Dai, Q. (2022). End-to-end snapshot compressed super-resolution imaging with deep optics. Optica, 9(4), 451-454.

28. Cheng, Z., Chen, B., Lu, R., Wang, Z., Zhang, H., Meng, Z., & Yuan, X*. (2022). Recurrent neural networks for snapshot compressive imaging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2), 2264-2281.

29. Cai, Y., Lin, J., Hu, X., Wang, H., Yuan, X., Zhang, Y., ... & Van Gool, L. (2022, October). Coarse-to-fine sparse transformer for hyperspectral image reconstruction. In European Conference on Computer Vision (pp. 686-704). Cham: Springer Nature Switzerland.

30. Hu, X., Cai, Y., Lin, J., Wang, H., Yuan, X., Zhang, Y., ... & Van Gool, L. (2022). Hdnet: High-resolution dual-domain learning for spectral compressive imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 17542-17551).

31. Zha, Z., Wen, B., Yuan, X., Zhou, J., Zhu, C., & Kot, A. C. (2022). Low-rankness guided group sparse representation for image restoration. IEEE Transactions on Neural Networks and Learning Systems.

32. Yuan, X.*#, Liu, Y.#, Suo, J., Durand, F., & Dai, Q. (2021). Plug-and-play algorithms for video snapshot compressive imaging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 7093-7111.

33. Lu, R., Chen, B.*, Liu, G., Cheng, Z., Qiao, M., & Yuan, X.* (2021). Dual-view snapshot compressive imaging via optical flow aided recurrent neural network. International Journal of Computer Vision, 129, 3279-3298.

34. Meng, Z., Yu, Z., Xu, K., & Yuan, X.* (2021). Self-supervised neural networks for spectral snapshot compressive imaging. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 2622-2631).

35. Li, X., Suo, J., Zhang, W., Yuan, X., & Dai, Q. (2021). Universal and flexible optical aberration correction using deep-prior based deconvolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2613-2621).

36. Qiao, M., Sun, Y., Ma, J., Meng, Z., Liu, X., & Yuan, X.* (2021). Snapshot coherence tomographic imaging. IEEE Transactions on Computational Imaging, 7, 624-637.

37. Zha, Z., Wen, B., Yuan, X., Zhou, J. T., Zhou, J., & Zhu, C. (2021). Triply complementary priors for image restoration. IEEE Transactions on Image Processing, 30, 5819-5834.

38. Zha, Z., Yuan, X., Wen, B., Zhang, J., & Zhu, C. (2021). Nonconvex structural sparsity residual constraint for image restoration. IEEE Transactions on Cybernetics, 52(11), 12440-12453.

39. Zheng, S., Wang, C., Yuan, X.*, & Xin, H. L.* (2021). Super-compression of large electron microscopy time series by deep compressive sensing learning. Patterns, 2(7).

40. Yuan, X.*, & Han, S. (2021). Single-pixel neutron imaging with artificial intelligence: Breaking the barrier in multi-parameter imaging, sensitivity, and spatial resolution. The Innovation, 2(2).

41. Cheng, Z., Chen, B.*, Liu, G., Zhang, H., Lu, R., Wang, Z., & Yuan, X.* (2021). Memory-efficient network for large-scale video compressive sensing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16246-16255).

42. Wang, Z., Zhang, H., Cheng, Z., Chen, B.*, & Yuan, X.* (2021). Metasci: Scalable and adaptive reconstruction for video compressive sensing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2083-2092). 

43. Huang, T., Dong, W.*, Yuan, X.*, Wu, J., & Shi, G. (2021). Deep gaussian scale mixture prior for spectral compressive imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16216-16225).

44. Zha, Z., Yuan, X., Wen, B., Zhang, J., & Zhu, C. (2021). Nonconvex structural sparsity residual constraint for image restoration. IEEE Transactions on Cybernetics, 52(11), 12440-12453.

45. Qiao, M., Liu, X., & Yuan, X.* (2021). Snapshot temporal compressive microscopy using an iterative algorithm with untrained neural networks. Optics Letters, 46(8), 1888-1891. 

46. Yuan, X.*, Brady, D. J., & Katsaggelos, A. K. (2021). Snapshot compressive imaging: Theory, algorithms, and applications. IEEE Signal Processing Magazine, 38(2), 65-88.

47. Zha, Z., Wen, B., Yuan, X., Zhou, J., Zhu, C., & Kot, A. C. (2021). A hybrid structural sparsification error model for image restoration. IEEE Transactions on Neural Networks and Learning Systems, 33(9), 4451-4465.

48. Zheng, S., Liu, Y., Meng, Z., Qiao, M., Tong, Z., Yang, X., ... & Yuan, X.* (2021). Deep plug-and-play priors for spectral snapshot compressive imaging. Photonics Research, 9(2), B18-B29.

49. Lu, S., Yuan, X., & Shi, W. (2020, November). Edge compression: An integrated framework for compressive imaging processing on cavs. In 2020 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 125-138). IEEE.

50. Meng, Z., Ma, J., & Yuan, X.* (2020, August). End-to-end low cost compressive spectral imaging with spatial-spectral self-attention. In European conference on computer vision (pp. 187-204). Cham: Springer International Publishing.

51. Cheng, Z., Lu, R., Wang, Z., Zhang, H., Chen, B.*, Meng, Z., & Yuan, X.* (2020, August). BIRNAT: Bidirectional recurrent neural networks with adversarial training for video snapshot compressive imaging. In European Conference on Computer Vision (pp. 258-275). Cham: Springer International Publishing.

52. Yuan, X., Liu, Y., Suo, J., & Dai, Q. (2020). Plug-and-play algorithms for large-scale snapshot compressive imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1447-1457).

53. 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. 

54. Meng, Z., Qiao, M., Ma, J., Yu, Z., Xu, K., & Yuan, X.* (2020). Snapshot multispectral endomicroscopy. Optics Letters, 45(14), 3897-3900.

55. Zha, Z., Yuan, X., Zhou, J., Zhu, C., & Wen, B. (2020). Image restoration via simultaneous nonlocal self-similarity priors. IEEE Transactions on Image Processing, 29, 8561-8576.

56. Zha, Z., Yuan, X., Wen, B., Zhang, J., Zhou, J., & Zhu, C. (2020). Image restoration using joint patch-group-based sparse representation. IEEE Transactions on Image Processing, 29, 7735-7750.

57. Qiao, M., Meng, Z., Ma, J., & Yuan, X.* (2020). Deep learning for video compressive sensing. APL Photonics, 5(3).

58. Qiao, M., Liu, X., & Yuan, X.* (2020). Snapshot spatial–temporal compressive imaging. Optics letters, 45(7), 1659-1662.

59. Yuan, X., & Haimi-Cohen, R. (2020). Image compression based on compressive sensing: End-to-end comparison with JPEG. IEEE Transactions on Multimedia, 22(11), 2889-2904.

60. Zha, Z., Yuan, X., Wen, B., Zhou, J., Zhang, J., & Zhu, C. (2020). A benchmark for sparse coding: When group sparsity meets rank minimization. IEEE Transactions on Image Processing, 29, 5094-5109.

61. Zha, Z., Yuan, X., Wen, B., Zhou, J., Zhang, J., & Zhu, C. (2019). From rank estimation to rank approximation: Rank residual constraint for image restoration. IEEE Transactions on Image Processing, 29, 3254-3269.

62. Ma, J., Liu, X. Y., Shou, Z., & Yuan, X. (2019). Deep tensor admm-net for snapshot compressive imaging. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10223-10232).

63. Miao, X., Yuan, X.*, Pu, Y., & Athitsos, V. (2019). l-net: Reconstruct hyperspectral images from a snapshot measurement. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4059-4069).

64. Jalali, S., & Yuan, X. (2019). Snapshot compressed sensing: Performance bounds and algorithms. IEEE Transactions on Information Theory, 65(12), 8005-8024.

65. Liu, Y., Yuan, X., Suo, J., Brady, D. J., & Dai, Q. (2018). Rank minimization for snapshot compressive imaging. IEEE transactions on pattern analysis and machine intelligence, 41(12), 2990-3006.

66. Zhang, X., Yuan, X.*, & Carin, L. (2018). Nonlocal low-rank tensor factor analysis for image restoration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8232-8241). 

67. Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A., & Carin, L. (2016). Variational autoencoder for deep learning of images, labels and captions. Advances in neural information processing systems, 29.

68. Pu, Y., Yuan, X., Stevens, A., Li, C., & Carin, L. (2016, May). A deep generative deconvolutional image model. In Artificial Intelligence and Statistics (pp. 741-750). PMLR.

69. Yuan, X., Henao, R., Tsalik, E., Langley, R., & Carin, L. (2015, June). Non-Gaussian discriminative factor models via the max-margin rank-likelihood. In International Conference on Machine Learning (pp. 1254-1263). PMLR.

70. Llull, P., Yuan, X., Carin, L., & Brady, D. J. (2015). Image translation for single-shot focal tomography. Optica, 2(9), 822-825.

71. Henao, R., Yuan, X., & Carin, L. (2014). Bayesian nonlinear support vector machines and discriminative factor modeling. Advances in neural information processing systems, 27.

72. Yuan, X., Llull, P., Liao, X., Yang, J., Brady, D. J., Sapiro, G., & Carin, L. (2014). Low-cost compressive sensing for color video and depth. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3318-3325).

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。