Edmund Y. Lam  —  Publications

All keywords:

| 3D imaging | biomedical imaging | compressed sensing | computational imaging | computational lithography | deep learning | digital holography | education technology | electronic imaging | environment | high dynamic range imaging | light field | machine learning | machine vision and automation | magnetic resonance imaging | microscopy | optical coherence tomography | speckle | super-resolution | ulfrafast imaging |

Current keyword: machine learning

Academic Journals:

  1. Kevin C. Tse, Hon-Chim Chiu, Man-Yin Tsang, Yiliang Li, and Edmund Y. Lam, “An unsupervised learning approach to study synchroneity of past events in the South China Sea,” Frontiers of Earth Science, vol. 13, no. 3, pp. 628–640, September 2019.
    DOI: 10.1007/s11707-019-0748-x

  2. Richard Du, Victor H. Lee, Hui Yuan, Ka-On Lam, Herbert H. Pang, Yu Chen, Edmund Y. Lam, Pek-Lan Khong, Anne W. Lee, Dora L. Kwong, and Varut Vardhanabhuti, “Radiomics model to predict early progression of nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy: A multicenter study,” Radiology: Artificial Intelligence, vol. 1, no. 4, pp. e180075(1–11), July 2019.
    DOI: 10.1148/ryai.2019180075

  3. Tianjiao Zeng, Hayden K.-H. So, and Edmund Y. Lam, “Computational image speckle suppression using block matching and machine learning,” Applied Optics, vol. 58, no. 7, pp. B39–B45, March 2019.
    DOI: 10.1364/AO.58.000B39

  4. Kevin C. Tse, Hon-Chim Chiu, Man-Yin Tsang, Yiliang Li, and Edmund Y. Lam, “Unsupervised learning on scientific ocean drilling datasets from the South China Sea,” Frontiers of Earth Science, vol. 13, no. 1, pp. 180–190, March 2019.
    DOI: 10.1007/s11707-018-0704-1

  5. Nan Meng, Xing Sun, Hayden K.-H. So, and Edmund Y. Lam, “Computational light field generation using deep nonparametric Bayesian learning,” IEEE Access, vol. 7, pp. 24990–25000, February 2019.
    DOI: 10.1109/ACCESS.2019.2900153

  6. Zhenbo Ren, Zhimin Xu, and Edmund Y. Lam, “End-to-end deep learning framework for digital holographic reconstruction,” Advanced Photonics, vol. 1, no. 1, pp. 016004(1–12), January 2019.
    DOI: 10.1117/1.AP.1.1.016004
    Highlighted as Editors' Pick in this first issue of the journal | Top Download in 2019 | SPIE news feature | Top Cited Article on Imaging and Sensing

  7. Zhenbo Ren, Zhimin Xu, and Edmund Y. Lam, “Learning-based nonparametric autofocusing for digital holography,” Optica, vol. 5, no. 4, pp. 337–344, April 2018.
    DOI: 10.1364/OPTICA.5.000337

  8. Xing Sun, Nelson H.C. Yung, Edmund Y. Lam, and Hayden K.-H. So, “Computationally efficient hyperspectral data learning based on the doubly stochastic Dirichlet process,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 1, pp. 363–374, January 2017.
    DOI: 10.1109/TGRS.2016.2606575

  9. Xing Sun, Nelson H.C. Yung, and Edmund Y. Lam, “Unsupervised tracking with the doubly stochastic Dirichlet process mixture model,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 9, pp. 2594–2599, September 2016.
    DOI: 10.1109/TITS.2016.2518212

  10. Ningning Jia and Edmund Y. Lam, “Machine learning for inverse lithography: Using stochastic gradient descent for robust photomask synthesis,” Journal of Optics, vol. 12, no. 4, pp. 045601(1–9), April 2010.
    DOI: 10.10882040-8978124045601

Conference Proceedings:

  1. Edmund Y. Lam and Tianjiao Zeng, “Computational imaging in digital holographic reconstruction with machine learning,” in IEEE International Conference on Computational Electromagnetics, pp. 77–78, August 2020.
    DOI: 10.1109/ICCEM47450.2020.9219395
    Invited Paper at the conference

  2. Edmund Y. Lam, “Holographic imaging and reconstruction using machine learning,” in Advances in Optoelectronics and Micro/nano-optics, pp. InF21, October 2018.
    DOI: 10.3390/s18113711
    Invited Paper at the conference

  3. Zhimin Xu, Si Zuo, and Edmund Y. Lam, “End-to-end learning for digital hologram reconstruction,” in High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, volume 10505 of Proceedings of the SPIE, pp. 1050510, January 2018.
    DOI: 10.1117/12.2288141

  4. Richard Du, W.H.K. Chiu, E.Y.P. Lee, Herbert Pang, Edmund Y. Lam, and Varut Vardhanabhuti, “Inter-session reproducibility and consistency of radiomic features after preprocessing as methods for quality control in MRI quantitative radiomics,” in 7th Joint Scientific Meeting of The Royal College of Radiologists and Hong Kong College of Radiologists and 25th Annual Scientific Meeting of Hong Kong College of Radiologists, pp. PP-RAD C8, November 2017.
    DOI: 10.1364/COSI.2018.CTh3C.1

  5. Xing Sun, Nan Meng, Zhimin Xu, Edmund Y. Lam, and Hayden K.-H. So, “Sparse hierarchical nonparametric Bayesian learning for light field representation and denoising,” in IEEE International Joint Conference on Neural Networks, pp. 3272–3279, July 2016.
    DOI: 10.1109/IJCNN.2016.7727617

  6. Xing Sun, Nelson H.C. Yung, Edmund Y. Lam, and Hayden K.-H. So, “Unsupervised tracking with a low computational cost using the doubly stochastic Dirichlet process mixture model,” in Image Processing: Machine Vision Applications, pp. IPMVA-381, February 2016.
    DOI: 10.2352/ISSN.2470-1173.2016.14.IPMVA-381

  7. C.H. Tse, Yi-liang Li, and Edmund Y. Lam, “Geological applications of machine learning on hyperspectral remote sensing data,” in Image Processing: Machine Vision Applications, volume 9405 of Proceedings of the SPIE, pp. 940512, February 2015.
    DOI: 10.1117/12.2178400

  8. Ningning Jia and Edmund Y. Lam, “Stochastic gradient descent for robust inverse photomask synthesis in optical lithography,” in IEEE International Conference on Image Processing, pp. 4173–4176, September 2010.
    DOI: 10.1109/ICIP.2010.5653690