Review of wavelet denoising algorithms

• Published in: Multimedia tools and applications Vol. 82; no. 27; pp. 41539 - 41569
• Main Authors: Halidou, Aminou, Mohamadou, Youssoufa, Ari, Ado Adamou Abba, Zacko, Edinio Jocelyn Gbadoubissa
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2023; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Cameramen; Coins; Communications systems; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Discrete Wavelet Transform; Multimedia Information Systems; Noise reduction; Signal to noise ratio; Special Purpose and Application-Based Systems; Wavelet transforms; Discrete wavelet transform (DWT); Thresholding; Peak signal to noise ratio (PSNR); Block matching and 3D filtering (BM3D); Denoising; Block matching and 3D filtering (BM3D); Denoising; Discrete wavelet transform (DWT); Peak signal to noise ratio (PSNR); Thresholding
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-15127-0

Although there has been a lot of progress in the general area of signal denoising, noise removal remains a very challenging problem in real-world communication systems. Denoising algorithms are typically used during the image preprocessing phase and are chosen based on the type of image, as a specific algorithm may work for a given noise but not for another one. Moreover, an algorithm can sometimes consider crucial information as being noise and eliminate it, hence the importance of careful selection and tuning of denoising algorithms. Denoising algorithms built on discrete wavelet transform decomposes signals into different frequency resolution levels. Thresholding is then applied to higher frequency components which generally correspond to noise to eliminate this one. In this paper, we review wavelet-based denoising methods and compare their performance based on metrics such as peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM). This work aims to find the best wavelet denoising algorithm using Peak these metrics. The common Matlab images such as cameraman, barbara, coins, and eight are used for our test. From these tests, the BM3DM_DWT method was found to be the simplest and most efficient for denoising.