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Image denoising method combining wavelet packet and partial differential equation

A partial differential equation and wavelet packet technology, which is applied in the field of image processing, can solve the problem that the internal texture features and edge corner information cannot fully achieve the denoising effect, etc., achieves the protection of internal information, preserves edge texture information, and has superior performance. Effect

Inactive Publication Date: 2018-11-13
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

[0003] At present, there are many studies on denoising methods, but for some internal texture features and edge corner information, only relying on gradient operators to diffuse cannot fully achieve the ideal denoising effect.

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  • Image denoising method combining wavelet packet and partial differential equation
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  • Image denoising method combining wavelet packet and partial differential equation

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Embodiment Construction

[0024] The technical solutions and beneficial effects of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0025] Such as figure 1 As shown, the present invention provides an image denoising method combining wavelet packets and partial differential equations, comprising the following steps:

[0026] Step 1: Convert the collected original image to grayscale and add noise, as shown in the following equations (1) and (2):

[0027] I(x,y,t)=I 0 *G(x,y,t) (1)

[0028]

[0029] In formula (1) (2), I 0 Represents the original grayscale image, I represents the noised image, G represents the Gaussian kernel function, σ 2 is the random noise variance, σ represents the measurement scale, x, y represent the two-dimensional spatial coordinates of the image domain, and t is the time diffusion scale; preferably, the grayscale conversion uses the rgb2gray function of MATLAB to convert the collected RGB image into a grayscale image ...

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Abstract

The invention discloses an image denoising method combining a wavelet packet and a partial differential equation. The method comprises the following steps of carrying out grayscale conversion on a collected original image and carrying out noise adding processing; constructing a new denoising model combining a PM model and a MCD model, through selecting a weight function, distinguishing and isolating noises, and then establishing a second-order differential operator; establishing the image denoising method based on the wavelet packet and the partial differential equation, using the wavelet packet to carry out coefficient decomposition on a noise image, and centrally processing noise information; according to a wavelet packet decomposition coefficient, using wavelet packet inverse transformation to reconstruct the image; and finally, carrying out smoothing processing on the processed image, carrying out simulation through a semi-implicit additive operator split numerical algorithm, comparing a mean square error, a peak signal to noise ratio and definition, and analyzing the validity and the feasibility of the method. By using the method, the noises are effectively removed, and simultaneously, the edge texture and other details of the image can be protected, and denoising performance is excellent.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image denoising method combining wavelet packets and partial differential equations. Background technique [0002] Research on image denoising and restoration has become an important research topic in image analysis fields such as edge detection, image segmentation, machine vision, and pattern recognition. The edge structure and texture information of the image can reflect the basic characteristics and important information of the image content, while the traditional filtering model always leads to the loss of edge information to a certain extent in the process of image denoising processing, so it is necessary to find an image that can achieve effective The method of denoising effect and preserving edge information is very important. Most of the denoising algorithms at this stage are derived from the fields of probability statistics theory, fuzzy theory, non-parametr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/70
Inventor 周先春吴静王茹蕙伍子锴吴婷
Owner NANJING UNIV OF INFORMATION SCI & TECH
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