Neighborhood adaptive Bayes shrinkage image denoising method based on dual-tree complex wavelet domain

A dual-tree complex wavelet and self-adaptive technology, applied in the field of image denoising, can solve the problems of poor self-adaptation and poor denoising performance, and achieve the effect of high-efficiency image denoising, good visual effect and high peak signal-to-noise ratio.

Inactive Publication Date: 2012-11-28
ZHEJIANG UNIV
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[0008] In order to overcome the disadvantages of poor denoising performance and poor self-adaptation of the existing image denoising methods, the present invention provides a neighborhood self-adaptive method in the dual-tree complex wavelet domain with excellent denoising performance and good self-adaptability Bayesian shrinkage image denoising method

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  • Neighborhood adaptive Bayes shrinkage image denoising method based on dual-tree complex wavelet domain
  • Neighborhood adaptive Bayes shrinkage image denoising method based on dual-tree complex wavelet domain
  • Neighborhood adaptive Bayes shrinkage image denoising method based on dual-tree complex wavelet domain

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[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] refer to figure 1 with figure 2 , a neighborhood adaptive Bayesian shrinkage image denoising method in the dual-tree complex wavelet domain, comprising the following steps:

[0036] 1) Perform dual-tree complex wavelet transform on the noisy image, and obtain K+1 subband coefficients after three-level decomposition;

[0037] 2) Estimate the noise variance with a robust median: use Y ij Represents the real part of the coefficient of the i-th row and j-column in the sub-band of the first-level decomposition, using Represents the noise variance of the image, which is estimated using a robust median estimator as σ ^ n = Median ( | Y ij | ) / 0.6745 ...

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Abstract

The invention discloses a neighborhood adaptive Bayes shrinkage image denoising method based on a dual-tree complex wavelet domain. The method comprises the following steps: 1) performing dual-tree complex wavelet transform on a noisy image, and performing three-level decomposition to obtain multiple sub-band coefficients; 2) estimating the noise variance by use of a robust median device; 3) processing each sub-band coefficient except the low-pass sub-band coefficient in the following steps: a) calculating the variance of the noisy image in corresponding neighborhood window for each DT-CWT (dual-tree complex wavelet transform) coefficient; b) averaging the variances of the noisy image corresponding to all the coefficients to estimate the neighborhood variance of the noisy image of the sub-band; and c) assuming that a statistical model of the DT-CWT coefficients of the image obeys a GGD (general Gaussian distribution) model, estimating the optimal threshold through a minimal Bayes risk function, and softening the wavelet coefficient in the sub-band; and 4) performing dual-tree complex wavelet inverse transform reconstruction on the wavelet coefficient to obtain the denoised image. The method disclosed by the invention has perfect denoising performance and good adaptivity.

Description

technical field [0001] The invention relates to image denoising technology, in particular to an image denoising method. Background technique [0002] In the process of image acquisition and transmission, certain noise is often introduced to affect the quality of the image. The model of image interference by Gaussian white noise is as follows: [0003] y=x+n [0004] Among them, y is a noisy image, x is a noise-free image, and n is additive white Gaussian noise. [0005] How to effectively restore the real image from the noisy image, eliminate the influence of noise as much as possible and retain important signal features is a research hotspot in the field of digital image processing. Due to the compressibility of signal and the incompressibility of noise, denoising technology based on wavelet transform has attracted more and more attention in recent years. [0006] The main processing process of the wavelet denoising algorithm includes: 1) Perform wavelet decomposition on...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 丁勇张稳稳王亚雄段克峰蒋一帆邢天玮李浙鲁
Owner ZHEJIANG UNIV
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