Non-local mean image denoising method combined with structure information

A non-local mean and structural information technology, applied in the field of image processing, can solve the problems of image pseudo-texture, lack of consistency in gray value, poor edge preservation effect, etc., to achieve the elimination of pseudo-texture, improve the edge preservation effect, and clear edges Effect

Active Publication Date: 2011-07-06
XIDIAN UNIV
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Problems solved by technology

Obviously this is counterintuitive, each pixel has a similar gray value to its surrounding neighbors, but the non-local mean method does not take advantage of this, making the gray value of the pixel and its surrounding pixels lack consistency, resulting in denoising Pseudo-texture phenomenon appears in the smooth part of the fi

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  • Non-local mean image denoising method combined with structure information
  • Non-local mean image denoising method combined with structure information

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[0023] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0024] Step 1, input the test image, add Gaussian white noise to it, and get the noise image.

[0025] The input image is figure 2 The eight grayscale images shown are: the lena image shown in 2(a), the barbara image shown in 2(b), the boat image shown in 2(c), and the boat image shown in 2(d) The peppers diagram shown, the flinstone diagram shown in 2(e), the zelda diagram shown in 2(f), the lighthouse diagram shown in 2(g), the couple diagram shown in 2(h), The size of each image is 512×512, and the gray level is 256. Three experiments are performed on each image, and the standard deviations of the added Gaussian white noise are σ=20, σ=35, and σ=50.

[0026] by figure 2 The experiment of the lena image shown in (a) is taken as an example. Gaussian white noise with standard deviation σ=35 is added to the image to obtain the noise image 4(b) of the lena image.

[002...

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Abstract

The invention discloses a non-local mean image denoising method combined with structure information, and the method provided by the invention is mainly used for solving the problem of pseudo image traces generated after the non-local means deonising. The non-local mean image denoising method comprises the following steps: (1) inputting an image to be denoised; (2) carrying out two-dimensional stationary wavelet transformation and inverse transformation on the image to obtain a reconstructed image; (3) extracting the structure information of the image by means of primal sketch to obtain a sideridge sketch of the image, and dividing the reconstructed image into a smooth region and a structure region; (4) forming a square window with a pixel as the center in the smooth region so as to search similar pixels, and calculating the similarity weights to re-estimate all of the pixels in the window; (5) forming a window with a pixel as a center along the structure direction of the structure region to search the similar pixels, and calculating the similarity weights to re-estimate all of the pixels in the window; and (6) combining the re-estimation results of the pixels in the smooth regionand the structure region to obtain a final denoised image. The method can be used for natural image denoising.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an image denoising method, and can be used for denoising natural images. Background technique [0002] Image denoising has always been an important problem in the image processing community. Due to the imperfection of image acquisition equipment, problems in the process of acquisition and transmission, and the interference of some unavoidable natural phenomena, the image data will be polluted by noise. Therefore, image denoising has become a commonly used image preprocessing method in order to improve image quality and image recognizability. Image denoising methods are mainly divided into spatial domain and frequency domain methods. The former does not need to transform the image, and directly denoises the image pixels, such as the classic mean filter, anisotropic filter and bilateral filter. The latter needs to transform the image into the frequency domain first, and then ...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 刘芳郝红侠焦李成王爽侯彪戚玉涛尚荣华武杰马文萍王伟伟于昕
Owner XIDIAN UNIV
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