Image denoising: who is best?

Image denoising is a critical task in image processing, particularly in applications where image quality is crucial. In this paper, we compared the performance of five denoising techniques: TV, NLM, BM3D, DnCNN and FFDNet, on grayscale images corrupted with additive white Gaussian noise (AWGN). The...

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Main Author: Yeong, Wei Xian
Other Authors: Qian Kemao
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/165995
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1659952023-04-21T15:37:25Z Image denoising: who is best? Yeong, Wei Xian Qian Kemao School of Computer Science and Engineering MKMQian@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Image denoising is a critical task in image processing, particularly in applications where image quality is crucial. In this paper, we compared the performance of five denoising techniques: TV, NLM, BM3D, DnCNN and FFDNet, on grayscale images corrupted with additive white Gaussian noise (AWGN). The comparison was based on both quantitative and qualitative evaluation of the various methods. The findings revealed that CNN-based methods outperformed the traditional methods significantly, with FFDNet demonstrating better trade-off between denoising performance and computational complexity. Additionally, several directions for future research were discussed. Bachelor of Engineering (Computer Science) 2023-04-18T01:44:55Z 2023-04-18T01:44:55Z 2023 Final Year Project (FYP) Yeong, W. X. (2023). Image denoising: who is best?. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/165995 https://hdl.handle.net/10356/165995 en SCSE22-0353 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::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Yeong, Wei Xian
Image denoising: who is best?
description Image denoising is a critical task in image processing, particularly in applications where image quality is crucial. In this paper, we compared the performance of five denoising techniques: TV, NLM, BM3D, DnCNN and FFDNet, on grayscale images corrupted with additive white Gaussian noise (AWGN). The comparison was based on both quantitative and qualitative evaluation of the various methods. The findings revealed that CNN-based methods outperformed the traditional methods significantly, with FFDNet demonstrating better trade-off between denoising performance and computational complexity. Additionally, several directions for future research were discussed.
author2 Qian Kemao
author_facet Qian Kemao
Yeong, Wei Xian
format Final Year Project
author Yeong, Wei Xian
author_sort Yeong, Wei Xian
title Image denoising: who is best?
title_short Image denoising: who is best?
title_full Image denoising: who is best?
title_fullStr Image denoising: who is best?
title_full_unstemmed Image denoising: who is best?
title_sort image denoising: who is best?
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/165995
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