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. Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam, “Digital holographic imaging and classification of microplastics using deep transfer learning,” Applied Optics, vol. 60, no. 4, pp. A38–A47, February 2021.
    DOI: 10.1364/AO.403366

  2. Zhenbo Ren, Edmund Y. Lam, and Jianlin Zhao, “Real-time target detection in visual sensing environments using deep transfer learning and improved anchor box generation,” IEEE Access, vol. 8, pp. 193512–193522, October 2020.
    DOI: 10.1109/ACCESS.2020.3032955

  3. Nan Meng, Zhou Ge, Tianjiao Zeng, and 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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. Li Song and Edmund Y. Lam, “MBD-GAN: Model-based image deblurring with a generative adversarial network,” in International Conference on Pattern Recognition, January 2021.

  2. Zhenxing Zhou, King-Shan Lui, Vincent W.L. Tam, andEdmund Y. Lam, “Applying (3+2+1)D residual neural network with frame selection for Hong Kong sign language recognition,” in International Conference on Pattern Recognition, January 2021.

  3. Zhou Ge, Li Song, and Edmund Y. Lam, “Light field image restoration in low-light environment,” in SPIE Future Sensing Technologies, volume 11525 of Proceedings of the SPIE, pp. 11525xx, November 2020.

  4. Zhenxing Zhou, King-Shan Lui, Vincent W.L. Tam, and Edmund Y. Lam, “Applying (2+1)D residual neural network with non-consecutive frames for the recognition of Hong Kong sign language,” in International Conference on Image Processing Theory, Tools and Applications, November 2020.

  5. Zhenxing Zhou, Yisiang Neo, King-Shan Lui, Vincent W.L. Tam, Edmund Y. Lam, and Ngai Wong, “A portable Hong Kong sign language translation platform with deep learning and Jetson Nano,” in International ACM SIGACCESS Conference on Computers and Accessibility, pp. xxx–xxx, October 2020.
    DOI: 10.1109/xxxxxxx

  6. Edmund Y. Lam, “Identification and quantification of microplastics using digital holography and deep learning,” in International Workshop on Precision Optics and Artificial Intelligence, October 2020.
    Invited Paper at the conference

  7. Tianjiao Zeng and Edmund Y. Lam, “Model-based network architecture for image reconstruction in lensless imaging,” in Holography, Diffractive Optics and Applications, volume 11551 of Proceedings of the SPIE, pp. 115510B, October 2020.
    DOI: 10.1117/12.2575205

  8. Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam, “Digital holography with deep learning and generative adversarial networks for automatic microplastics classification,” in Holography, Diffractive Optics and Applications, volume 11551 of Proceedings of the SPIE, pp. 115510A, October 2020.
    DOI: 10.1117/12.2575115

  9. Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam, “Holographic classifier: Deep learning in digital holography for automatic micro-objects classification,” in IEEE International Conference on Industrial Informatics, pp. xxx–xxx, July 2020.
    DOI: 10.1109/xxxxxxx

  10. Linxia Zhang, Jun Ke, and Edmund Y. Lam, “A deep learning approach for reconstruction in temporal compressed imaging,” in OSA Topical Meeting in Computational Optical Sensing and Imaging, pp. CW4B.3, June 2020.
    DOI: 10.1364/COSI.2020.CW4B.3

  11. Gangping Liu, Jun Ke, and Edmund Y. Lam, “CNN-based super-resolution full-waveform LiDAR,” in OSA Topical Meeting in Computational Optical Sensing and Imaging, pp. JW2A.29, June 2020.
    DOI: 10.1364/COSI.2020.JW2A.29

  12. Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam, “Automatic detection of microplastics by deep learning enabled digital holography,” in OSA Topical Meeting in Digital Holography and Three-Dimensional Imaging, pp. HTu5B.1, June 2020.
    DOI: 10.1364/DH.2020.HTu5B.1
    Invited Paper at the conference

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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.

  23. 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

  24. 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

  25. 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