Edmund Lam
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Edmund Y. Lam  —  Publications

All keywords:

| AI and deep learning | biomedical microscopy | compressed sensing | computational imaging | computational lithography | digital holography | education technology | electronic imaging | eye imaging | lensless imaging | light field | machine vision and automation | magnetic resonance imaging | metasurface | microplastics | neuromorphic imaging | optical coherence tomography | speckle | super-resolution |

Current keyword: AI and deep learning

Academic Journals:

  1. Jingqian Wu, Peiqi Duan, Zongqiang Wang, Changwei Wang, Boxin Shi, and Edmund Y. Lam, “Dark-EvGS: Event camera as an eye for radiance field in the dark,” IEEE Transactions on Image Processing, vol. 35, pp. 3172–3185, March 2026.
    DOI: 10.1109/TIP.2026.3674360

  2. Xiaoyan Qian, Chang Liu, Xiaojuan Qi, Siewchong Tan, Edmund Y. Lam, and Ngai Wong, “CAT: Enhancing 3D annotations with hierarchical-interleaved encoding and attention-conditioned implicit representation,” International Journal of Computer Vision, vol. 134, pp. 71(1–21), January 2026.
    DOI: 10.1007/s11263-025-02704-z

  3. Jingqian Wu, Shuo Zhu, Chutian Wang, Boxin Shi, and Edmund Y. Lam, “SweepEvGS: Event-based 3D Gaussian splatting for macro and micro radiance field rendering from a single sweep,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 12, pp. 12734–12746, December 2025.
    DOI: 10.1109/TCSVT.2025.3583735

  4. Bozhen Zhou, Zhitao Hao, Zhenbo Ren, Edmund Y. Lam, Jianshe Ma, and Ping Su, “Learning from better simulation: Creating highly realistic synthetic data for deep learning in scattering media,” Advanced Photonics Nexus, vol. 4, no. 5, pp. 056007(1–12), September/October 2025.
    DOI: 10.1117/1.APN.4.5.056007

  5. Zhenyu Yang, Zhongning Jiang, Haisong Lin, Xiaoxue Fan, Changjin Wu, Edmund Y. Lam, Hayden K.H. So, and Ho Cheung Shum, “CeyeHao: AI-driven microfluidic flow programming with hierarchically assembled obstacles and receptive field–augmented neural network,” Science Advances, vol. 11, no. 31, pp. eadx2826(1–14), August 2025.
    DOI: 10.1126/sciadv.adx2826

  6. Yiheng Liu, Xinsheng Li, Ziheng Jin, Wenwu Chen, Edmund Y. Lam, Shijie Feng, Qian Chen, and Chao Zuo, “Multimodal adaptive temporal phase unwrapping using deep learning and physical priors,” APL Photonics, vol. 10, no. 4, pp. 046104(1–13), April 2025.
    DOI: 10.1063/5.0252363

  7. Jingqian Wu, Rongtao Xu, Zach Wood-Doughty, Changwei Wang, Shibiao Xu, and Edmund Y. Lam, “Segment anything model is a good teacher for local feature learning,” IEEE Transactions on Image Processing, vol. 34, pp. 2097–2111, March 2025.
    DOI: 10.1109/TIP.2025.3554033

  8. Kaiqiang Wang and Edmund Y. Lam, “Deep learning phase recovery: data-driven, physics-driven, or a combination of both?” Advanced Photonics Nexus, vol. 3, no. 5, pp. 056006(1–12), September/October 2024.
    DOI: 10.1117/1.APN.3.5.056006
    Editors' Pick and Author Presentation

  9. Yaping Zhao, Jichang Zhao, and Edmund Y. Lam, “House price prediction: A multi-source data fusion perspective,” Big Data Mining and Analytics, vol. 7, no. 3, pp. 603–620, September 2024.
    DOI: 10.26599/BDMA.2024.9020019

  10. Peiyan Guan and Edmund Y. Lam, “Progressive self-supervised pretraining for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 5517713(1–13), May 2024.
    DOI: 10.1109/TGRS.2024.3397740

  11. Yunping Zhang, Xihui Liu, and Edmund Y. Lam, “Single-shot inline holography using a physics-aware diffusion model: errata,” Optics Express, vol. 32, no. 11, pp. 18742–18743, May 2024.
    DOI: 10.1364/OE.525947

  12. Truong Thanh Nhat Mai, Edmund Y. Lam, and Chul Lee, “Deep unfolding tensor rank minimization with generalized detail injection for pansharpening,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 5405218(1–18), April 2024.
    DOI: 10.1109/TGRS.2024.3392215

  13. Shansi Zhang and Edmund Y. Lam, “Light field image restoration via latent diffusion and multi-view attention,” IEEE Signal Processing Letters, vol. 31, pp. 1094–1098, April 2024.
    DOI: 10.1109/LSP.2024.3383798

  14. Shansi Zhang, Nan Meng, and Edmund Y. Lam, “Unsupervised light field depth estimation via multi-view feature matching with occlusion prediction,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 4, pp. 2261–2273, April 2024.
    DOI: 10.1109/TCSVT.2023.3305978

  15. Yunping Zhang, Xihui Liu, and Edmund Y. Lam, “Single-shot inline holography using a physics-aware diffusion model,” Optics Express, vol. 32, no. 6, pp. 10444–10460, March 2024.
    DOI: 10.1364/OE.517233

  16. Yanmin Zhu, Yuxing Li, Jianqing Huang, and Edmund Y. Lam, “Smart polarization and spectroscopic holography for real-time microplastics identification,” Communications Engineering, vol. 3, pp. 32(1–8), February 2024.
    DOI: 10.1038/s44172-024-00178-4

  17. Yuxing Li, Yanmin Zhu, Jianqing Huang, Yuen-Wa Ho, James Kar-Hei Fang, and Edmund Y. Lam, “High-throughput microplastic assessment using polarization holographic imaging,” Scientific Reports, vol. 14, pp. 2355(1–11), January 2024.
    DOI: 10.1038/s41598-024-52762-5
    Part of Holography collection

  18. Kaiqiang Wang, Li Song, Chutian Wang, Zhenbo Ren, Guangyuan Zhao, Jiazhen Dou, Jianglei Di, George Barbastathis, Renjie Zhou, Jianlin Zhao, and Edmund Y. Lam, “On the use of deep learning for phase recovery,” Light: Science and Applications, vol. 13, pp. 4(1–46), January 2024.
    DOI: 10.1038/s41377-023-01340-x
    Cover Paper and 2024 Editors' Highlight

    Top Download in Year 2024 and 2025

  19. Pei Zhang, Chutian Wang, and Edmund Y. Lam, “Neuromorphic imaging and classification with graph learning,” Neurocomputing, vol. 565, pp. 127010(1–9), January 2024.
    DOI: 10.1016/j.neucom.2023.127010

  20. Wei Yin, Yuxuan Che, Xinsheng Li, Mingyu Li, Yan Hu, Shijie Feng, Edmund Y. Lam, Qian Chen, and Chao Zuo, “Physics-informed deep learning for fringe pattern analysis,” Opto-Electronic Advances, vol. 7, no. 1, pp. 230034(1–12), January 2024.
    DOI: 10.29026/oea.2024.230034
    Cover Paper

  21. Jianqing Huang, Yanmin Zhu, Yuxing Li, and Edmund Y. Lam, “Snapshot polarization-sensitive holography for detecting microplastics in turbid water,” ACS Photonics, vol. 10, no. 12, pp. 4483–4493, December 2023.
    DOI: 10.1021/acsphotonics.3c01350

  22. Haiyan Ou, Yong Wu, Kun Zhu, Edmund Y. Lam, and Bing-Zhong Wang, “Suppressing defocus noise with U-net in optical scanning holography,” Chinese Optics Letters, vol. 21, no. 8, pp. 080501(1–9), August 2023.
    DOI: 10.3788/COL202321.080501

  23. Shansi Zhang, Nan Meng, and Edmund Y. Lam, “LRT: An efficient low-light restoration transformer for dark light field images,” IEEE Transactions on Image Processing, vol. 32, pp. 4314–4326, July 2023.
    DOI: 10.1109/TIP.2023.3297412

  24. Pei Zhang, Zhou Ge, Li Song, and Edmund Y. Lam, “Neuromorphic imaging with density-based spatiotemporal denoising,” IEEE Transactions on Computational Imaging, vol. 9, pp. 530–541, May 2023.
    DOI: 10.1109/TCI.2023.3281202

  25. Xinming Guo, Yixuan Li, Jiaming Qian, Yuxuan Che, Chao Zuo, Qian Chen, Edmund Y. Lam, Huai Wang, and Shijie Feng, “Unifying temporal phase unwrapping framework using deep learning,” Optics Express, vol. 31, no. 10, pp. 16659–16675, May 2023.
    DOI: 10.1364/OE.488597

  26. Li Song and Edmund Y. Lam, “Phase retrieval with a dual recursive scheme,” Optics Express, vol. 31, no. 6, pp. 10386–10400, March 2023.
    DOI: 10.1364/OE.484649

  27. Boyi Huang, Jia Li, Bowen Yao, Zhigang Yang, Edmund Y. Lam, Jia Zhang, Wei Yan, and Junle Qu, “Enhancing image resolution of confocal fluorescence microscopy with deep learning,” PhotoniX, vol. 4, pp. 2(1–22), January 2023.
    DOI: 10.1186/s43074-022-00077-x
    Highlighted in the article ‘‘Microscopy's Cutting Edge'' in the April 2023 issue of Optics and Photonics News

  28. Zhenxing Zhou, Vincent W.L. Tam, and Edmund Y. Lam, “A cross-attention BERT-based framework for continuous sign language recognition,” IEEE Signal Processing Letters, vol. 29, pp. 1818–1822, August 2022.
    DOI: 10.1109/LSP.2022.3199665

  29. Yanmin Zhu, Hau Kwan Abby Lo, Chok Hang Yeung, and Edmund Y. Lam, “Microplastic pollution assessment with digital holography and zero-shot learning,” APL Photonics, vol. 7, no. 7, pp. 076102(1–10), July 2022.
    DOI: 10.1063/5.0093439

  30. Li Song and Edmund Y. Lam, “Iterative phase retrieval with a sensor mask,” Optics Express, vol. 30, no. 14, pp. 25788–25802, July 2022.
    DOI: 10.1364/OE.461367

  31. Peiyan Guan and Edmund Y. Lam, “Cross-domain contrastive learning for hyperspectral image classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 5528913(1–13), May 2022.
    DOI: 10.1109/TGRS.2022.3176637

  32. Li Song and Edmund Y. Lam, “Fast and robust phase retrieval for masked coherent diffractive imaging,” Photonics Research, vol. 10, no. 3, pp. 758–768, March 2022.
    DOI: 10.1364/PRJ.447862

  33. Zhenxing Zhou, Vincent W.L. Tam, and Edmund Y. Lam, “A portable sign language collection and translation platform with smart watches using a BLSTM-based multi-feature framework,” Micromachines, vol. 13, no. 2, pp. 333(1–15), February 2022.
    DOI: 10.3390/mi13020333

  34. Yunping Zhang, Yanmin Zhu, and Edmund Y. Lam, “Holographic 3D particle reconstruction using a one-stage network,” Applied Optics, vol. 61, no. 5, pp. B111–B120, February 2022.
    DOI: 10.1364/AO.444856

  35. Peiyan Guan and Edmund Y. Lam, “Multistage dual-attention guided fusion network for hyperspectral pansharpening,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 5515214(1–14), January 2022.
    DOI: 10.1109/TGRS.2021.3114552

  36. Zhenxing Zhou, Vincent W.L. Tam, and Edmund Y. Lam, “SignBERT: A BERT-based deep learning framework for continuous sign language recognition,” IEEE Access, vol. 9, pp. 161669–161682, December 2021.
    DOI: 10.1109/ACCESS.2021.3132668

  37. Yanmin Zhu, Tianjiao Zeng, Kewei Liu, Zhenbo Ren, and Edmund Y. Lam, “Full scene underwater imaging with polarization and an untrained network,” Optics Express, vol. 29, no. 25, pp. 41865–41881, December 2021.
    DOI: 10.1364/OE.444755
    Top Downloads in Optics Express (Dec 21)

  38. Shansi Zhang and Edmund Y. Lam, “An effective decomposition-enhancement method to restore light field images captured in the dark,” Signal Processing, vol. 189, pp. 108279(1–11), December 2021.
    DOI: 10.1016/j.sigpro.2021.108279

  39. Tianjiao Zeng, Yanmin Zhu, and Edmund Y. Lam, “Deep learning for digital holography: a review,” Optics Express, vol. 29, no. 24, pp. 40572–40593, November 2021.
    DOI: 10.1364/OE.443367
    Top Downloads in Optics Express (Nov 21)

  40. Shansi Zhang and Edmund Y. Lam, “Learning to restore light fields under low-light imaging,” Neurocomputing, vol. 456, pp. 76–87, October 2021.
    DOI: 10.1016/j.neucom.2021.05.074

  41. Tianjiao Zeng and Edmund Y. Lam, “Robust reconstruction with deep learning to handle model mismatch in lensless imaging,” IEEE Transactions on Computational Imaging, vol. 7, pp. 1080–1092, September 2021.
    DOI: 10.1109/TCI.2021.3114542

  42. Pei Zhang and Edmund Y. Lam, “From local to global: Efficient dual attention mechanism for single image super-resolution,” IEEE Access, vol. 9, pp. 114957–114964, August 2021.
    DOI: 10.1109/ACCESS.2021.3105726

  43. Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam, “Microplastic pollution monitoring with holographic classification and deep learning,” Journal of Physics: Photonics, vol. 3, no. 2, pp. 024013(1–12), April 2021.
    DOI: 10.1088/2515-7647/abf250
    Invited Paper for the Focus on Computational Imaging and Artificial Intelligence

  44. Nan Meng, Kai Li, Jianzhuang Liu, and Edmund Y. Lam, “Light field view synthesis via aperture disparity and warping confidence map,” IEEE Transactions on Image Processing, vol. 30, pp. 3908–3921, March 2021.
    DOI: 10.1109/TIP.2021.3066293

  45. Nan Meng, Hayden K.-H. So, Xing Sun, and Edmund Y. Lam, “High-dimensional dense residual convolutional neural network for light field reconstruction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 3, pp. 873–886, March 2021.
    DOI: 10.1109/TPAMI.2019.2945027

  46. 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
    Among 15 most cited articles in the journal between 2020 and 2022

  47. 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

  48. 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

  49. 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

  50. 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
    A top-downloaded article on deep learning in Optics Express in 2020

  51. 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

  52. 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

  53. 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

  54. 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

  55. 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

  56. 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

  57. 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

  58. 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

  59. 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

  60. 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

  61. 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

  62. 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

  63. 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: 10.1088/2040-8978/12/4/045601

Conference Proceedings:

  1. Juntao Qiu, Yaping Zhao, and Edmund Y. Lam, “ColorGPT: Automatic colorization with generative prompts and transformer,” in IEEE International Conference on Image Processing, pp. 1528–1533, September 2025.
    DOI: 10.1109/ICIP55913.2025.11084307

  2. Rongzhou Chen, Yaping Zhao, Hanghang Liu, Haohan Xu, Shaohua Ma, and Edmund Y. Lam, “Eigen-component analysis: A quantum theory-inspired linear model,” in IEEE International Symposium on Circuits and Systems, pp. 1–5, May 2025.
    DOI: 10.1109/ISCAS56072.2025.11044249

  3. Kaiqiang Wang and Edmund Y. Lam, “Deep learning phase recovery: Data-driven or physics-driven?” in Photonics and Electromagnetics Research Symposium, pp. 1–4, April 2024.
    DOI: 10.1109/PIERS62282.2024.10618233
    Invited Paper at the conference

  4. Yunping Zhang, Xihui Liu, and Edmund Y. Lam, “Single-shot digital holography with improved twin-image noise suppression using a diffusion-based generative model,” in Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, volume 12857 of Proceedings of the SPIE, pp. 128570E, January 2024.
    DOI: 10.1117/12.3000660

  5. Chang Liu, Xiaoguang Li, Lifeng Shang, Xin Jiang, Qun Liu, Edmund Y. Lam, and Ngai Wong, “Gradually excavating external knowledge for implicit complex question answering,” in Findings of the Association for Computational Linguistics: Conference on Empirical Methods in Natural Language Processing, pp. 14405–14417, December 2023.
    DOI: 10.18653/v1/2023.findings-emnlp.961

  6. Jason C. Li, Rui Lin, Jiajun Zhou, Edmund Y. Lam, and Ngai Wong, “A unifying tensor view for lightweight CNNs,” in IEEE International Conference on ASIC (ASICON), pp. 1–4, October 2023.
    DOI: 10.1109/ASICON58565.2023.10396098

  7. Zhen Yuen Chong, Yaping Zhao, Zhongrui Wang, and Edmund Y. Lam, “Solving inverse problems in compressive imaging with score-based generative models,” in IEEE International Conference on Data Science and Advanced Analytics, pp. 1–10, October 2023.
    DOI: 10.1109/DSAA60987.2023.10302579

  8. Yaping Zhao, Haitian Zheng, Jiebo Luo, and Edmund Y. Lam, “Improving video colorization by test-time tuning,” in IEEE International Conference on Image Processing, pp. 166–170, October 2023.
    DOI: 10.1109/ICIP49359.2023.10222579

  9. Yaping Zhao, Haitian Zheng, Zhongrui Wang, Jiebo Luo, and Edmund Y. Lam, “Point cloud denoising via momentum ascent in gradient fields,” in IEEE International Conference on Image Processing, pp. 161–165, October 2023.
    DOI: 10.1109/ICIP49359.2023.10222122

  10. Peiyan Guan, Renjing Pei, Bin Shao, Jianzhuang Liu, Weimian Li, Jiaxi Gu, Hang Xu, Songcen Xu, Youliang Yan, and Edmund Y. Lam, “PIDRo: Parallel isomeric attention with dynamic routing for text-video retrieval,” in International Conference on Computer Vision, pp. 11164–11173, October 2023.
    DOI: 10.1109/ICCV51070.2023.01025

  11. Yanmin Zhu, Yuxing Li, Jianqing Huang, Yunping Zhang, and Edmund Y. Lam, “Holographic and polarization features analysis for microplastics characterization and water monitoring,” in Multimodal Sensing and Artificial Intelligence: Technologies and Applications, volume 12621 of Proceedings of the SPIE, pp. 126210X, June 2023.
    DOI: 10.1117/12.2678293
    Invited Paper at the conference

  12. Li Song and Edmund Y. Lam, “Masked coherent diffractive imaging with ADMM-based phase retrieval,” in Biomedical Imaging and Sensing Conference, volume 12608 of Proceedings of the SPIE, pp. 126080B, April 2023.
    DOI: 10.1117/12.3007506

  13. Xiaoyan Qian, Chang Liu, Xiaojuan Qi, Siew-Chong Tan, Edmund Y. Lam, and Ngai Wong, “Context-aware transformer for 3D point cloud automatic annotation,” in AAAI Conference on Artificial Intelligence, pp. 2082–2090, February 2023.
    DOI: 10.1609/aaai.v37i2.25301

  14. Yaping Zhao, Shuhui Shi, Ramgopal Ravi, Zhongrui Wang, Edmund Y. Lam, and Jichang Zhao, “H4M: Heterogeneous, multi-source, multi-modal, multi-view and multi-distributional dataset for socioeconomic analytics in the case of Beijing,” in IEEE International Conference on Data Science and Advanced Analytics, pp. 1–10, October 2022.
    DOI: 10.1109/DSAA54385.2022.10032424

  15. Yaping Zhao, Ramgopal Ravi, Shuhui Shi, Zhongrui Wang, Edmund Y. Lam, and Jichang Zhao, “PATE: Property, amenities, traffic and emotions coming together for real estate price prediction,” in IEEE International Conference on Data Science and Advanced Analytics, pp. 1–10, October 2022.
    DOI: 10.1109/DSAA54385.2022.10032416

  16. Yaping Zhao, Zhongrui Wang, and Edmund Y. Lam, “Improving source localization by perturbing graph diffusion,” in IEEE International Conference on Data Science and Advanced Analytics, pp. 1–9, October 2022.
    DOI: 10.1109/DSAA54385.2022.10032349

  17. Chang Liu, Xiaoyan Qian, Binxiao Huang, Xiaojuan Qi, Edmund Lam, Siew-Chong Tan, and Ngai Wong, “Multimodal transformer for automatic 3D annotation and object detection,” in European Conference on Computer Vision, pp. 657–673, October 2022.
    DOI: 10.1007/978-3-031-19839-7_38

  18. Yaping Zhao, Haitian Zheng, Zhongrui Wang, Jiebo Luo, and Edmund Y. Lam, “MANet: Improving video denoising with a multi-alignment network,” in IEEE International Conference on Image Processing, pp. 2036–2040, October 2022.
    DOI: 10.1109/ICIP46576.2022.9898028

  19. Shansi Zhang and Edmund Y. Lam, “A deep retinex framework for light field restoration under low-light conditions,” in International Conference on Pattern Recognition, pp. 2042–2048, August 2022.
    DOI: 10.1109/ICPR56361.2022.9956107

  20. Chang Liu, Xiaoyan Qian, Xiaojuan Qi, Edmund Y. Lam, Siew-Chong Tan, and Ngai Wong, “MAP-Gen: An automated 3D-box annotation flow with multimodal attention point generator,” in International Conference on Pattern Recognition, pp. 1148–1155, August 2022.
    DOI: 10.1109/ICPR56361.2022.9956415

  21. Yanmin Zhu, Hau Kwan Abby Lo, Chok Hang Yeung, and Edmund Y. Lam, “Zero-shot learning for holographic context analysis in microplastics probing,” in Optica Topical Meeting in Digital Holography and Three-Dimensional Imaging, pp. W5A.41, August 2022.
    DOI: 10.1364/DH.2022.W5A.41

  22. Peiyan Guan and Edmund Y. Lam, “Spatial-spectral contrastive learning for hyperspectral image classification,” in IEEE International Geoscience and Remote Sensing Symposium, pp. 1372–1375, July 2022.
    DOI: 10.1109/IGARSS46834.2022.9883226

  23. Peiyan Guan and Edmund Y. Lam, “Three-branch multilevel attentive fusion network for hyperspectral pansharpening,” in IEEE International Geoscience and Remote Sensing Symposium, pp. 1087–1090, July 2022.
    DOI: 10.1109/IGARSS46834.2022.9883218

  24. Dingaoyu Zhao, Edmund Y. Lam, and Jun Ke, “Spatial-temporal compressive imaging using an unfolding network,” in Optica Topical Meeting in Computational Optical Sensing and Imaging, pp. CW1B.5, July 2022.
    DOI: 10.1364/COSI.2022.CW1B.5

  25. Truong Thanh Nhat Mai, Edmund Y. Lam, and Chul Lee, “Ghost-free HDR imaging via unrolling low-rank matrix completion,” in IEEE International Conference on Image Processing, pp. 2928–2932, September 2021.
    DOI: 10.1109/ICIP42928.2021.9506201

  26. Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam, “Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning,” in 12th International Conference on Information Optics and Photonics, volume 12057 of Proceedings of the SPIE, pp. 120573G, July 2021.
    DOI: 10.1117/12.2606532
    Invited Paper at the conference

  27. Yanmin Zhu, Chok Hang Yeung, and Edmund Y. Lam, “Underwater holographic descattering with synthetic polarization,” in OSA Topical Meeting in Digital Holography and Three-Dimensional Imaging, pp. DTu6H.6, July 2021.
    DOI: 10.1364/DH.2021.DTu6H.6

  28. Zhenbo Ren, Edmund Y. Lam, and Jianlin Zhao, “Learning-based cell detection in digital pathology,” in Conference on Lasers and Electro-Optics (CLEO), pp. JW1A.184, May 2021.
    DOI: 10.1364/CLEO_AT.2021.JW1A.184

  29. Michelle C.K. Lo, Kelvin C.M. Lee, Dickson M.D. Siu, Edmund Y. Lam, and Kevin K. Tsia, “Augmented multiplexed asymmetric-detection time-stretch optical microscopy by generative deep learning,” in High-Speed Biomedical Imaging and Spectroscopy, volume 11654 of Proceedings of the SPIE, pp. 1165410, March 2021.
    DOI: 10.1117/12.2582985

  30. Li Song and Edmund Y. Lam, “MBD-GAN: Model-based image deblurring with a generative adversarial network,” in International Conference on Pattern Recognition, pp. 7306–7313, January 2021.
    DOI: 10.1109/ICPR48806.2021.9411979

  31. Zhenxing Zhou, King-Shan Lui, Vincent W.L. Tam, and Edmund 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, pp. 4296–4302, January 2021.
    DOI: 10.1109/ICPR48806.2021.9412075

  32. 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. 115251H, November 2020.
    DOI: 10.1117/12.2580033

  33. 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. 89(1–4), October 2020.
    DOI: 10.1145/3373625.3418046

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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. 515–520, July 2020.
    DOI: 10.1109/INDIN45582.2020.9442146

  39. 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

  40. 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

  41. 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

  42. 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.
    DOI: 10.1609/aaai.v34i07.6847

  43. 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

  44. 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

  45. 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

  46. 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

  47. 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

  48. 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

  49. 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

  50. 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

  51. 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

  52. 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

  53. 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

  54. 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.

  55. 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

  56. 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

  57. 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

  58. 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

  59. 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

  60. 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

  61. 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