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Cluster Computing

Erschienen in:

02.02.2018

A novel algorithm for threshold image denoising based on wavelet construction

verfasst von: Zhang Jianhua, Zhu Qiang, Zhang Jinrong, Song Lin, Wang Jilong

Erschienen in: Cluster Computing | Sonderheft 5/2019

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Abstract

By analyzing characteristics of wavelet-based image threshold denoising, a biorthogonal wavelet of even symmetry at the zero point with (13-3) filters length and 2/4/6-order vanishing moments is constructed using a filter parameterization method. In light of the disadvantages of global threshold, the self-adaptive hierarchical threshold denoising algorithm is proposed, where the noise decay rate in detail coefficients (detcoef, for short) of wavelet decomposition was employed to calculate hierarchical threshold value. The simulation test verifies that the constructed wavelet has favorable denoising capacity such that image details can be preserved more completely. When combined with the self-adaptive hierarchical threshold denoising algorithm, the wavelet can improve image quality and SNR significantly.

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Titel: A novel algorithm for threshold image denoising based on wavelet construction
verfasst von: Zhang Jianhua
Zhu Qiang
Zhang Jinrong
Song Lin
Wang Jilong
Publikationsdatum: 02.02.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 5/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1655-0

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