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Image denoising method integrated with structure tensor and non-local total variation

A structure tensor, non-local technology, applied in the field of image processing, can solve the problem of not taking into account the image structure information, easily blurred image details, etc.

Active Publication Date: 2016-05-25
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, the filtering method that only considers local information does not take into account the structural information of the image, so it is easy to blur the details of the image

Method used

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  • Image denoising method integrated with structure tensor and non-local total variation
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  • Image denoising method integrated with structure tensor and non-local total variation

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Embodiment Construction

[0054] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] figure 1 It is a schematic flow chart of the method of the present invention. As shown in the figure, the method includes the following steps: 1) performing N*N block on the noisy image f, and the value of N can be N=3,5,7. .., is an odd number of 3-2M+1 (M is a natural number) in the present embodiment; 2) image denoising is modeled, and an objective function of fusion structure tensor and non-local total variation is set up; 3) utilize The split Bregman algorithm is used to solve the objective function to obtain the denoised image.

[0056] In the present embodiment, the detailed steps of setting up the objective function in step 2) are:

[0057] 21) Establish an image denoising model based on full variational regularization

[0058]

[0059] Where f is a noisy image, and u is a denoised image to be restored. The purpose of i...

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Abstract

The invention relates to an image denoising method integrated with a structure tensor and non-local total variation, and belongs to the technical field of image processing. The method comprises the following steps: 1, performing N*N portioning on an image f with noise, wherein the value of N can be 3, 5, 7...; 2, performing denoising modeling on the image to establish an object function integrated with the structure tensor and the non-local total variation; and 3, obtaining a denoised image by solving the object function by use of a split Bregman algorithm. The method provided by the invention has the following advantages: texture features and geometric structure features of the image are maintained while noise is effectively eliminated, the algorithm is rapid, and at the same time, a step effect can be reduced.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image denoising method that combines structure tensor and non-local total variation. Background technique [0002] Image denoising is a basic problem in the field of image processing, which aims to remove the noise in the image while retaining the original image information as much as possible, such as edges, textures, and fine image structures. The essence of image denoising is the inverse process of image degradation. The classic denoising methods include wavelet method for frequency domain processing, Gaussian filter and median filter for spatial domain processing. The operation achieves a better smoothing effect, but at the cost of losing details and texture information. In order to alleviate this contradiction, Yaroslavsky proposed neighborhood filtering, which uses the pixel gray similarity as the weight basis between neighborhoods, and the bilateral filter furth...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/70
Inventor 王诗言
Owner CHONGQING UNIV OF POSTS & TELECOMM
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