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Natural image denoising method based on non-local mean value of shearlet region

A non-local mean, natural image technology, applied in the field of image processing, which can solve the problems of scratches, visual blur of natural images, etc.

Active Publication Date: 2010-12-29
XIDIAN UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to address the deficiencies in the above-mentioned prior art, and propose a natural image denoising method based on non-local means in the shearlet domain, so as to overcome the visual blurring of the natural image in the case of high noise in the existing method. The problem of severe scratch phenomenon, improve the denoising effect

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[0021] refer to figure 1 , the implementation steps of the present invention are as follows:

[0022] Step 1: Select a test image and add Gaussian white noise with a standard deviation of 50 to it.

[0023] Step 2: Perform Laplacian pyramid decomposition on the test image added with Gaussian white noise with a standard deviation of 50, decompose the test image into three layers, and apply the shearlet generated by the shearlet basis function to the second layer and the third layer respectively. The filter bank performs directional filtering, that is, the number of filters in the shearlet filter bank is designated as four, and four sets of shearlet coefficients are obtained respectively; step 5 is performed on the first layer.

[0024] The shearlet basis function described in step 2, the specific formula is as follows:

[0025]

[0026] in f ( ω ) = ( 1 / ...

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Abstract

The invention discloses a natural image denoising method based on a non-local mean value of a shearlet region, which mainly solves the problem that the traditional non-local mean value method has poor denoising effect of a natural image corroded by high noise. The method comprises the following implementation steps of: inputting a test image, and adding gaussian white noise with the noise standard deviation of 50; decomposing the image into three layers by utilizing a Laplacian pyramid method, wherein denoising treatment is carried out on the first layer by using a non-local mean value method, the second layer and the third layer are respectively decomposed into four groups of shearlet coefficients by using a shearlet directional filter group firstly, then estimation of a beta value is carried out on each group of shearlet coefficients, and then the denoising treatment of the non-local mean value method under a general Gauss model is carried out on each group of shearlet coefficients; and reconstructing a denoising result to obtain a final denoising result. The invention has the advantages of favorable denoising effect for the natural image corroded by high noise, can restore the original characteristics of the image and be used for variation detection and pretreatment of the image when an object is identified.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to denoising of natural images under high noise conditions, and can be used for forest resource investigation, land use, cover change research, environmental disaster assessment, urban planning, national defense and military situation monitoring, medical imaging and astronomical imaging, etc. Preprocessing techniques often used in domain image processing. Background technique [0002] The rapid development of computer science and technology has had a huge impact on the field of digital image processing. Image denoising is an important branch of image processing. The most basic method in processing is to estimate the noise image as: v(i)=u(i )+n(i), v(i) represents the noise image, u(i) represents the original image, and n(i) represents the noise. The purpose of processing the image is to remove n(i) and estimate u( i), image denoising is nothing more than the two major direction...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 张小华焦李成张强王爽王然侯彪钟桦尚荣华
Owner XIDIAN UNIV
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