Image denoising algorithm based on the red-black wavelet transform

Sheng Bing Zhou; Guo Qiang Ni; Ting Fa Xu; Qiong Liu

Image denoising algorithm based on the red-black wavelet transform

Sheng Bing Zhou^*
, Guo Qiang Ni
, Ting Fa Xu
, Qiong Liu

^*此作品的通讯作者

光电学院

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

Based on the red-black wavelet transform, a new thresholding for image denoising is proposed. Combing the characteristics of affine and non-negative thresholding, this method can keep a better balance between image details and it's denoising. After simulation on a multitude of images, eight different thresholding weights applied to as many as eight levels of red-black wavelet transforms have been proposed. Simulation proves that by combining the thresholding and the eight weights better results can be achieved.

源语言	英语
页（从-至）	264-267
页数	4
期刊	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
卷	26
期	3
出版状态	已出版 - 3月 2006

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引用此

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title = "Image denoising algorithm based on the red-black wavelet transform",

abstract = "Based on the red-black wavelet transform, a new thresholding for image denoising is proposed. Combing the characteristics of affine and non-negative thresholding, this method can keep a better balance between image details and it's denoising. After simulation on a multitude of images, eight different thresholding weights applied to as many as eight levels of red-black wavelet transforms have been proposed. Simulation proves that by combining the thresholding and the eight weights better results can be achieved.",

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N2 - Based on the red-black wavelet transform, a new thresholding for image denoising is proposed. Combing the characteristics of affine and non-negative thresholding, this method can keep a better balance between image details and it's denoising. After simulation on a multitude of images, eight different thresholding weights applied to as many as eight levels of red-black wavelet transforms have been proposed. Simulation proves that by combining the thresholding and the eight weights better results can be achieved.

AB - Based on the red-black wavelet transform, a new thresholding for image denoising is proposed. Combing the characteristics of affine and non-negative thresholding, this method can keep a better balance between image details and it's denoising. After simulation on a multitude of images, eight different thresholding weights applied to as many as eight levels of red-black wavelet transforms have been proposed. Simulation proves that by combining the thresholding and the eight weights better results can be achieved.

KW - Affine thresholding

KW - Image denoising

KW - Non-negative garrote thresholding

KW - Red-black wavelet

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Image denoising algorithm based on the red-black wavelet transform

摘要

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