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: deep learning

Academic Journals:

  1. Nan Meng, Zhou Ge, Tianjiao Zeng, Edmund Y. Lam, “LightGAN: A deep generative model for light field reconstruction,” IEEE Access, vol. 8, pp. 116052–116063, June 2020.
    DOI: 10.1109/ACCESS.2020.3004477

  2. Zhimin Xu, Si Zuo, Edmund Y. Lam, Byoungho Lee, and Ni Chen, “AutoSegNet: An automated neural network for image segmentation,” IEEE Access, vol. 8, pp. 92452–92461, May 2020.
    DOI: 10.1109/ACCESS.2020.2995367

  3. Tianjiao Zeng, Hayden K.-H. So, and Edmund Y. Lam, “RedCap: residual encoder-decoder capsule network for holographic image reconstruction,” Optics Express, vol. 28, no. 4, pp. 4876–4887, February 2020.
    DOI: 10.1364/OE.383350

  4. Zhenbo Ren, Hayden K.-H. So, and Edmund Y. Lam, “Fringe pattern improvement and super-resolution using deep learning in digital holography,” IEEE Transactions on Industrial Informatics, vol. 15, no. 11, pp. 6179–6186, November 2019.
    DOI: 10.1109/TII.2019.2913853

  5. Nan Meng, Edmund Y. Lam, Kevin K. Tsia, and Hayden K.-H. So, “Large-scale multi-class image-based cell classification with deep learning,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 5, pp. 2091–2098, September 2019.
    DOI: 10.1109/JBHI.2018.2878878

  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. Chongguo Li, Nelson H.C. Yung, Xing Sun, and Edmund Y. Lam, “Human arm pose modeling with learned features using joint convolutional neural network,” Machine Vision and Applications, vol. 28, no. 1, pp. 1–14, February 2017.
    DOI: 10.1007/s00138-016-0796-0

Conference Proceedings:

  1. Nan Meng, Xiaofei Wu, Jianzhuang Liu, and Edmund Y. Lam, “High-order residual network for light field super-resolution,” in AAAI Conference on Artificial Intelligence, pp. 11757–11764, February 2020.

  2. Zhenxing Zhou, Vincent Tam, K.S. Lui, Edmund Y. Lam, Allan Yuen, Xiao Hu, and Nancy Law, “Applying deep learning and wearable devices for educational data analytics,” in IEEE International Conference on Tools with Artificial Intelligence, November 2019.
    DOI: 10.1109/ICTAI.2019.00124

  3. Edmund Y. Lam, “Deep learning for digital holography: Imaging, autofocusing, and reconstruction,” in Holography, Diffractive Optics and Applications, volume 11188 of Proceedings of the SPIE, October 2019.
    Invited Paper at the conference

  4. Nan Meng, Tianjiao Zeng, and Edmund Y. Lam, “Spatial and angular reconstruction of light field based on deep generative networks,” in IEEE International Conference on Image Processing, pp. 4659–4663, September 2019.
    DOI: 10.1109/ICIP.2019.8803480

  5. Zhenbo Ren, Tianjiao Zeng, and Edmund Y. Lam, “Digital holographic imaging via deep learning,” in OSA Topical Meeting in Computational Optical Sensing and Imaging, pp. CTu3A.4, June 2019.
    DOI: 10.1364/COSI.2019.CTu3A.4

  6. Nan Meng, Tianjiao Zeng, and Edmund Y. Lam, “Perceptual loss for light field reconstruction in high-dimensional convolutional neural networks,” in OSA Topical Meeting in Computational Optical Sensing and Imaging, pp. CW1A.5, June 2019.
    DOI: 10.1364/COSI.2019.CW1A.5

  7. Tianjiao Zeng, Zhenbo Ren, and Edmund Y. Lam, “Speckle suppression using the convolutional neural network with an exponential linear unit,” in OSA Topical Meeting in Computational Optical Sensing and Imaging, pp. CW5B.3, June 2018.
    DOI: 10.1364/COSI.2018.CW5B.3

  8. Edmund Y. Lam, Nan Meng, and Hayden K.H. So, “Deep convolutional neural network for single-cell image analysis,” in High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, volume 10505 of Proceedings of the SPIE, pp. 105050K, January 2018.
    DOI: 10.1117/12.2295469

  9. Zhenbo Ren, Zhimin Xu, and Edmund Y. Lam, “Autofocusing in digital holography using deep learning,” in Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing, volume 10499 of Proceedings of the SPIE, pp. 104991V, January 2018.
    DOI: 10.1117/12.2289282

  10. Nan Meng, Hayden K.-H. So, and Edmund Y. Lam, “Computational single-cell classification using deep learning on bright-field and phase images,” in IAPR Conference on Machine Vision Applications, pp. 164–167, May 2017.

  11. Xing Sun, Zhimin Xu, Nan Meng, Edmund Y. Lam, and Hayden K.-H. So, “Data-driven light field depth estimation using deep convolutional neural networks,” in IEEE International Joint Conference on Neural Networks, pp. 367–374, July 2016.
    DOI: 10.1109/IJCNN.2016.7727222

  12. Junnan Li and Edmund Y. Lam, “Facial expression recognition using deep neural networks,” in IEEE International Conference on Imaging Systems and Techniques, pp. 263–268, September 2015.
    DOI: 10.1109/IST.2015.7294547

  13. Chongguo Li, Nelson H.C. Yung, and Edmund Y. Lam, “Human arm pose modeling with learned features using joint convolutional neural network,” in IAPR Conference on Machine Vision Applications, pp. 398–401, May 2015.
    DOI: 10.1109/MVA.2015.7153213