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X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Hong_Kong:20250703T100000
DTEND;TZID=Asia/Hong_Kong:20250703T110000
DTSTAMP:20260511T205658
CREATED:20250627T100829Z
LAST-MODIFIED:20250627T100829Z
UID:112470-1751536800-1751540400@ece.hku.hk
SUMMARY:LUT-based Approaches for Low-level Image Processing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/9174230511?omn=91474809260 \nAbstract\nIn recent years\, deep neural networks (DNNs) have achieved remarkable success across a wide range of applications\, especially for low-level image processing. However\, many of these approaches rely on complicated and deeply stacked architectures to attain high performance\, which lead to significant challenges for deployment on edge devices with limited computing resources. Therefore\, research has been motivated to design efficient pipelines to enable practical and resource-friendly AI applications without compromising accuracy. We explore the efficient architecture design of low-level image processing. To avoid the floating-point operations introduced by convolutional neural networks (CNNs) and use as little space as possible\, we design various pure Lookup Tables (LUTs) framework for three widely applied tasks\, covering the single image super resolution\, color enhancement\, and 3D reconstruction.\n \nSpeaker\nBinxiao Huang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nBinxiao Huang received his B.Eng. Degree and master’s degree from Beihang University. He is currently pursuing a PhD degree in the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His research focuses on efficient deep learning methods for image processing and 3D reconstruction. \nAll are welcome!
URL:https://ece.hku.hk/events/20250703-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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