Professor Edmund Lam Delivers Keynote on Computational Neuromorphic Imaging at Optica Imaging Congress
At the recent Optica Imaging Congress, Professor Edmund Lam from the University of Hong Kong delivered an insightful plenary talk on the advances in computational neuromorphic imaging. His presentation highlighted the use of event sensors to capture rapid changes in pixel intensities, which are crucial for applications ranging from environmental monitoring to biomedicine. The session provided a deep dive into both the technical developments and historical evolution of imaging technologies, showcasing the collaborative and international efforts in this dynamic field.
Contributors: Edmund Lam
Event Name: Optica Imaging Congress (OIC)
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Exciting Breakthrough in Optical Image Encryption
In our latest publication in Advanced Photonics Nexus, we introduce a revolutionary approach to optical image encryption. This novel method, developed by Professor Edmund Y. Lam and his team, seamlessly combines computational neuromorphic imaging (CNI) with speckle correlography to enhance the security and complexity of encrypted data. This breakthrough not only bolsters data protection technologies but also sets a new standard in the encryption field, promising a wide range of applications from national defense to personal data security.
Contributors: Shuo Zhu, Chutian Wang, Jianqing Huang, Pei Zhang, Jing Han, Edmund Y. Lam
Event Name: Advanced Photonics Nexus (APN)
Event Date: 2024-07-17
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Prof. Edmund Lam’s Team Revolutionizes eSports Broadcasting with Event Cameras
The rapid growth of eSports has created a demand for more dynamic and engaging broadcasting techniques. Traditional RGB cameras often struggle to capture the fast, precise hand movements of professional gamers, leading to gaps in what viewers can experience during live streams. Addressing these challenges, our research introduces the use of event cameras in eSports broadcasting. Unlike conventional cameras, event cameras capture brightness changes through asynchronous events, offering high temporal resolution and a wide dynamic range without motion blur. This innovative approach enhances the visual quality of broadcasts by focusing on the critical, rapid actions of players, improving both viewer engagement and the overall broadcast experience.
Contributors: Yaping Zhao, Rongzhou Chen, Chutian Wang, and Prof. Edmund Y. Lam
Event Name: International Conference on Neural Information Processing (ICONIP)
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Congratulations to ISL’s PhD Candidate for Winning Third Prize in the 10th Hong Kong University Student Innovation and Entrepreneurship Competition
We are proud to announce that Yaping Zhao, a PhD candidate at our Imaging Systems Laboratory supervised by Prof. Edmund Y. Lam, has been awarded Third Prize at the 10th Hong Kong University Student Innovation and Entrepreneurship Competition (HKUSIEC), organized by the Hong Kong New Generation Cultural Association (HKNGCA). Her project, “Compressive Imaging Systems for Efficient Visual Intelligence,” showcases the innovative work being done right here at ISL.
Contributors: Yaping Zhao, Edmund Lam
Event Name: Hong Kong University Student Innovation and Entrepreneurship Competition (HKUSIEC)
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Jingyan Chen Discusses Microplastics Identification at Applied Industrial Spectroscopy Session
Jingyan Chen from Hong Kong University recently presented a talk on innovative methods for identifying microplastics using spectral reconstruction from RGB images. This presentation was part of the Applied Industrial Spectroscopy session focused on Agriphotonics, Food and Water Safety, held at the conference on Wednesday.
Contributors: Yuxing Li, Jianqing Huang, Jingyan Chen, Edmund Lam
Event Name: Computational Optical Sensing and Imaging (COSI)
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University of Hong Kong’s Imaging Systems Laboratory Participates in COSI Optica Imaging Congress
The Imaging Systems Laboratory from the University of Hong Kong recently participated in the COSI Optica Imaging Congress, where they presented research on advancements in computational optical sensing and imaging. The session included discussions on enhancing quantum sensing, improving measurement accuracy in dynamic optical environments, and synthesizing spectra for deeper biological analysis.
Contributors: Chutian Wang, Shuo Zhu, Pei Zhang, Rongzhou Chen, Edmund Lam
Event Name: Computational Optical Sensing and Imaging (COSI)
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Imaging Systems Laboratory Hosts Workshop on Microplastics Pollution at HKU
On June 11, 2024, the Imaging Systems Laboratory at the University of Hong Kong hosted a workshop focused on leveraging optical and computational technologies to address the challenge of microplastics and nanoplastics pollution. The event brought together leading experts from various academic institutions to discuss innovative approaches and share their latest research findings in the field.
Contributors: Prof. Patrick Lee (CityU), Prof. Edmund Lam (HKU), Prof. Kevin Tsia (HKU), Dr. Wa Tat Yan (HKMEA), Dr. Jianqing Huang (HKU), Dr. Derek Ho (PolyU), Najia Sharmin (HKU), Shaopeng Xu (CityU)
Event Name: Workshop on Optical and Computational Technologies to Combat Microplastics and Nanoplastics Pollution (WOCT)
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ISL Team Led by Prof. Edmund Y. Lam Wins Best Paper Award at IEEE MLSP 2024
The Imaging Systems Laboratory (ISL) at HKU, led by Prof. Edmund Y. Lam, is at the forefront of computational imaging research. The lab's latest achievement with Ev-GS demonstrates the potential of event-based cameras and 3D Gaussian splatting for highly efficient and accurate radiance field rendering. ISL’s work contributes to advancing CNI and its applications in both academia and industry.
Contributors: Jingqian Wu, Shuo Zhu, Chutian Wang, Edmund Y. Lam
Event Name: Machine Learning for Signal Processing (MLSP)
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Neuromorphic imaging and classification with graph learning
Bio-inspired neuromorphic cameras asynchronously record pixel brightness changes and generate sparse event streams. They can capture dynamic scenes with little motion blur and more details in extreme illumination conditions. Due to the multidimensional address-event structure, most existing vision algorithms cannot properly handle asynchronous event streams. While several event representations and processing methods have been developed to address such an issue, they are typically driven by a large number of events, leading to substantial overheads in runtime and memory. In this paper, we propose a new graph representation of the event data and couple it with a Graph Transformer to perform accurate neuromorphic classification. Extensive experiments show that our approach leads to better results and excels at the challenging realistic situations where only a small number of events and limited computational resources are available, paving the way for neuromorphic applications embedded into mobile facilities.
Contributors: Pei Zhang, Chutian Wang, Edmund Y. Lam
Event Name: Neurocomputing (Neurocomputing)
Event Date: 2023-11-10
Learn MoreImaging Systems Laboratory at the University of Hong Kong, led by Prof. Edmund Lam, is dedicated to advanced research in computational imaging, combining aspects of electronic engineering, computer vision, and optical engineering. Its primary interests lie in the development of novel algorithms for unconventional imaging systems and leveraging AI in imaging applications. The lab offers various opportunities, including roles for Post-doctoral Fellows, Principal/Senior Researchers, and Graduate Students, catering to individuals with a strong academic background and a passion for innovative research in imaging technologies.