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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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 |
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Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Yeong, Wei Xian Image denoising: who is best? |
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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. |
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Qian Kemao |
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Qian Kemao Yeong, Wei Xian |
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Final Year Project |
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Yeong, Wei Xian |
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Yeong, Wei Xian |
title |
Image denoising: who is best? |
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Image denoising: who is best? |
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Image denoising: who is best? |
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Image denoising: who is best? |
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Image denoising: who is best? |
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image denoising: who is best? |
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Nanyang Technological University |
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2023 |
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https://hdl.handle.net/10356/165995 |
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