Image denoising: who is best?

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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書目詳細資料
主要作者: Yeong, Wei Xian
其他作者: Qian Kemao
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2023
主題:
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
在線閱讀:https://hdl.handle.net/10356/165995
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總結: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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