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Issue title: Fuzzy model for human autonomous computing in extreme surveillance and it’s applications
Guest editors: Varatharajan Ramachandran
Article type: Research Article
Authors: Li, Shufang; * | Juan, Wang
Affiliations: Wuhan Technical College of Communications, Wuhan, Hubei, China
Correspondence: [*] Corresponding author. Shufang Li, Wuhan Technical College of Communications, Wuhan, Hubei, China, E-mail: [email protected].
Abstract: For the English classroom teaching video denoising algorithm, it is not only necessary to consider whether the noise removal of the output video is thorough, but also to consider the actual operating efficiency and robustness of the algorithm. In the process of the thesis research, after reading a large number of internal and external documents on video denoising algorithms and analyzing the pros and cons of various denoising algorithms, this paper proposes a new video denoising algorithm, which uses the recently proposed grid flow motion model based on camera motion compensation to generate denoised video. Compared with the current advanced video denoising schemes, our method processes noisy frames faster and has good robustness. In addition, this article improves the algorithm framework so that the algorithm can not only deal with offline video denoising, but also deal with online video denoising.
Keywords: Motion compensation, grid flow, smart classroom, English video, clarity
DOI: 10.3233/JIFS-189528
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6985-6995, 2021
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