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。