Research Article | Open Access | Download Full Text
Volume 2 | Issue 4 | Year 2023 | Article Id: DST-V2I4P101 DOI: https://doi.org/10.59232/DST-V2I4P101
Transforming Noisy Images Wavelet-Based Denoising Methods
Koteswararao Mallaparapu, K.V. Ramarao
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 27 Sep 2023 | 03 Oct 2023 | 14 Nov 2023 | 24 Nov 2023 |
Citation
Koteswararao Mallaparapu, K.V. Ramarao. “Transforming Noisy Images Wavelet-Based Denoising Methods.” DS Journal of Digital Science and Technology, vol. 2, no. 4, pp. 1-8, 2023.
Abstract
This paper presents a novel thresholding function that mixes the soft thresholding functions and Smoothly Clipped Absolute Deviation for denoising images using the decimated wavelet transform technique, which is widely popular in various applications. The proposed method is applied to denoise noisy images contaminated with additive white Gaussian noise, employing the Top rule method. The efficiency of this new thresholding function is also evaluated within the context of the Translation Invariant method. The outcomes are associated with standing methods such as SCAD, soft thresholding, and the Wiener filter-based denoising approach. Parameters such as root mean square error and peak signal to noise ration are employed to assess the quality of denoising.
Keywords
WT, DWT, Image denoising, New thresholding function, Top rule, Wiener filter, Translation invariant method.
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