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: speckle

Academic Journals:

  1. Zhenhua Zhou, Edmund Y. Lam, and Chul Lee, “Nonlocal means filtering based speckle removal utilizing the maximum a posteriori estimation and the total variation image prior,” IEEE Access, vol. 7, pp. 99231–99243, December 2019.
    DOI: 10.1109/ACCESS.2019.2929364

  2. 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

  3. Jianbing Xu, Haiyan Ou, Cuiru Sun, Po Ching Chui, Victor X.D. Yang, Edmund Y. Lam, and Kenneth K.Y. Wong, “Wavelet domain compounding for speckle reduction in optical coherence tomography,” Journal of Biomedical Optics, vol. 18, no. 9, pp. 096002(1–7), September 2013.
    DOI: 10.1117/1.JBO.18.9.096002

  4. Kyle H.Y. Cheng, Edmund Y. Lam, Beau A. Standish, and Victor X.D. Yang, “Speckle reduction of endovascular optical coherence tomography using a generalized divergence measure,” Optics Letters, vol. 37, no. 14, pp. 2871–2873, July 2012.
    DOI: 10.1364/OL.37.002871

Conference Proceedings:

  1. 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