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Review of wavelet denoising algorithms

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Sciences, University of Yaounde I, PO Box 337, Yaounde, Cameroon

    Aminou Halidou

  2. Department of Mechanical and Industrial Engineering Technology (DMIET), University of Johannesburg, P.O. Box 524, Johannesburg, South Africa

    Aminou Halidou

  3. LASE, University Institute of Technology, University of Ngaoundere, P.O Box 454, Ngaoundéré, Cameroon

    Youssoufa Mohamadou

  4. BEEMo Lab, ISST, Universite des Montagnes, P.O. Box 208, Bangangte, Cameroon

    Youssoufa Mohamadou

  5. Department of Computer Science, University of Maroua, PO Box 814, Maroua, Cameroon

    Ado Adamou Abba Ari & Edinio Jocelyn Gbadoubissa Zacko

  6. DAVID Lab, Université Paris-Saclay, University of Versailles Saint-Quentin-en-Yvelines, 45 Avenue des États-Unis, 78000, Versailles, France

    Ado Adamou Abba Ari

  7. African Institute for Mathematical Sciences (AIMS-Cameroon), P.O. Box 608, Limbé, Cameroon

    Edinio Jocelyn Gbadoubissa Zacko

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  1. Aminou Halidou
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Halidou, A., Mohamadou, Y., Ari, A.A.A. et al. Review of wavelet denoising algorithms. Multimed Tools Appl 82, 41539–41569 (2023). https://doi.org/10.1007/s11042-023-15127-0

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