Deep task-driven video denoising

The main contribution of this research is two folds. First, this research work explores the vast domain of video denoising, analyze challenges in designing video denoising algorithm, and study previously successful state-of-the-art video denoising techniques. Secondly, this research aims to optimize...

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Main Author: Kurniadi, Daniel
Other Authors: Wen Bihan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/138731
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1387312023-07-07T18:19:24Z Deep task-driven video denoising Kurniadi, Daniel Wen Bihan School of Electrical and Electronic Engineering Wen Bihan bihan.wen@ntu.edu.sg Engineering::Electrical and electronic engineering The main contribution of this research is two folds. First, this research work explores the vast domain of video denoising, analyze challenges in designing video denoising algorithm, and study previously successful state-of-the-art video denoising techniques. Secondly, this research aims to optimize video denoising algorithm when the result is supplied for high-level task behind it. We propose a method that features high-level information guided video denoising that capable of achieving comparable result with the state-of-the-art denoising while preserving semantic-aware details for high-level vision tasks. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-12T04:58:08Z 2020-05-12T04:58:08Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138731 en A3272-191 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Kurniadi, Daniel
Deep task-driven video denoising
description The main contribution of this research is two folds. First, this research work explores the vast domain of video denoising, analyze challenges in designing video denoising algorithm, and study previously successful state-of-the-art video denoising techniques. Secondly, this research aims to optimize video denoising algorithm when the result is supplied for high-level task behind it. We propose a method that features high-level information guided video denoising that capable of achieving comparable result with the state-of-the-art denoising while preserving semantic-aware details for high-level vision tasks.
author2 Wen Bihan
author_facet Wen Bihan
Kurniadi, Daniel
format Final Year Project
author Kurniadi, Daniel
author_sort Kurniadi, Daniel
title Deep task-driven video denoising
title_short Deep task-driven video denoising
title_full Deep task-driven video denoising
title_fullStr Deep task-driven video denoising
title_full_unstemmed Deep task-driven video denoising
title_sort deep task-driven video denoising
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/138731
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