demonstration

Edge-assisted deep video denoising and super-resolution for real-time surveillance at night

Authors:

Liming Ge,

Wei Bao,

Dong Yuan,

Bing B. ZhouAuthors Info & Claims

MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

Pages 783 - 785

https://doi.org/10.1145/3495243.3558754

Published: 14 October 2022 Publication History

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Abstract

Video surveillance cameras have been extensively deployed over the last few years. In case of incidents such as natural disaster rescue, it provides vital guidance in real-time. However, due to the limited camera hardware and network bandwidth, noise are observed especially at night and the resolution is low. To tackle these two issues, we design and implement EADV, an Edge-Assisted Deep Video denoising and super-resolution system for real-time surveillance at night. We demonstrate the video quality enhancement using a camera, a displayer, and an edge server. The low-quality video captured by the camera is enhanced by the server and shown on the displayer. The enhanced real-time video is smooth and the performance uplift is observable.

References

[1]

Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. 2021. BasicVSR: The search for essential components in video super-resolution and beyond. In IEEE Conference on Computer Vision and Pattern Recognition. 4947--4956.

Crossref

Google Scholar

[2]

Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. 2021. BasicVSR++: Improving video super-resolution with enhanced propagation and alignment. arXiv preprint arXiv:2104.13371.

Google Scholar

[3]

Michele Claus and Jan van Gemert. 2019. Videnn: Deep blind video denoising. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.

Crossref

Google Scholar

[4]

Ryan D Gow, David Renshaw, Keith Findlater, Lindsay Grant, Stuart J McLeod, John Hart, and Robert L Nicol. 2007. A comprehensive tool for modeling CMOS image-sensor-noise performance. IEEE Transactions on Electron Devices. 54, 6, 1321--1329.

Crossref

Google Scholar

[5]

Takashi Isobe, Fang Zhu, Xu Jia, and Shengjin Wang. 2020. Revisiting temporal modeling for video super-resolution. arXiv preprint arXiv:2008.05765.

Google Scholar

[6]

Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, and Radu Timofte. 2022. Video super-resolution based on deep learning: a comprehensive survey. Artificial Intelligence Review (2022), 1--55.

Google Scholar

[7]

Matias Tassano, Julie Delon, and Thomas Veit. 2019. DVDnet: A fast network for deep video denoising. In IEEE International Conference on Image Processing. IEEE, 1805--1809.

Crossref

Google Scholar

[8]

Matias Tassano, Julie Delon, and Thomas Veit. 2020. FastDVDnet: Towards real-time deep video denoising without flow estimation. In IEEE Conference on Computer Vision and Pattern Recognition. 1354--1363.

Crossref

Google Scholar

[9]

Gregory Vaksman, Michael Elad, and Peyman Milanfar. 2021. Patch Craft: Video Denoising by Deep Modeling and Patch Matching. In IEEE International Conference on Computer Vision. 2157--2166.

Crossref

Google Scholar

[10]

Jing Wang, Yitong Yu, Songtao Wu, Chang Lei, and Kuanhong Xu. 2021. Rethinking Noise Modeling in Extreme Low-Light Environments. In IEEE International Conference on Multimedia and Expo. IEEE, 1--6.

Google Scholar

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Ge LBao WSheng XYuan DZhou BWang Z(2024)Joint Video Denoising and Super-Resolution Network for IoT CamerasIEEE Internet of Things Journal10.1109/JIOT.2024.340262211:17(28526-28538)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3402622
Xu RRazavi SZheng R(2023)Edge Video Analytics: A Survey on Applications, Systems and Enabling TechniquesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332309125:4(2951-2982)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3323091

Index Terms

Edge-assisted deep video denoising and super-resolution for real-time surveillance at night
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

October 2022

932 pages

ISBN:9781450391818

DOI:10.1145/3495243

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 14 October 2022

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ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking

October 17 - 21, 2022

NSW, Sydney, Australia

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Ge LBao WSheng XYuan DZhou BWang Z(2024)Joint Video Denoising and Super-Resolution Network for IoT CamerasIEEE Internet of Things Journal10.1109/JIOT.2024.340262211:17(28526-28538)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3402622
Xu RRazavi SZheng R(2023)Edge Video Analytics: A Survey on Applications, Systems and Enabling TechniquesIEEE Communications Surveys & Tutorials10.1109/COMST.2023.332309125:4(2951-2982)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3323091

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Real-EVE: Real-Time Edge-Assist Video Enhancement for Joint Denoising and Super-Resolution

A Proposed Intelligent Denoising Technique for Spatial Video Denoising for Real-Time Applications

Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey

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Real-EVE: Real-Time Edge-Assist Video Enhancement for Joint Denoising and Super-Resolution

A Proposed Intelligent Denoising Technique for Spatial Video Denoising for Real-Time Applications

Arbitrary-scale Super-resolution via Deep Learning: A Comprehensive Survey

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