Elliptical search window and parameter self-adaption non-local mean value denoising method

A non-local mean, search window technology, applied in the field of image processing, can solve the problem of the degradation of the ability to retain the details of the image, the distinction and analysis of pixels that cannot have similar features, etc.

Active Publication Date: 2018-11-06
CHENGDU UNIV OF INFORMATION TECH
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AI Technical Summary

Problems solved by technology

As follows: (1) Although NLM has a certain denoising effect, with the increase of the noise level, the ability to retain the image details is seriously degraded, and the details contain a lot of important image information; (2) the traditional NLM algorithm converts the image Each pixel in the image is treated equally, and it cannot effectively distinguish pixels with similar characteristics in the image for analysis

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  • Elliptical search window and parameter self-adaption non-local mean value denoising method
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  • Elliptical search window and parameter self-adaption non-local mean value denoising method

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Embodiment

[0033] An elliptical search window and parameter adaptive non-local mean denoising method provided in this embodiment specifically includes the following steps, see figure 1 :

[0034] (1) Input a 512×512 Monarch noise image I, and the noise level is σ=20.

[0035] (2) Traverse the pixel point i in the noise image I point by point. In this example, the pixel point at the image (357,354) is selected as

[0036] For an example, see figure 2 , get the local neighborhood Ω centered on the pixel i , the neighborhood size is 11×11.

[0037] (3) Calculate the square local neighborhood Ω by the following formula 1 i Gradient image G along horizontal and vertical directions i :

[0038]

[0039] Then, for the gradient image G i Perform singular value decomposition to get the eigenvalue S i =(S i,1 ,S i , 2 ) and the eigenvector V i =(V i,1 ,V i , 2 ),which is

[0040] The structure tensor C is calculated by the following formula 2 i :

[0041]

[0042] in, ...

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Abstract

The invention relates to an image denoising processing method based on a non-local mean value frame and relates to the image processing technology. The method comprises the steps: employing an elliptical search window which is consistent with an image local region structure, carrying out the adaptive adjustment of the size of the elliptical search window and the internal smoothing parameter valuesof a denoising algorithm according to a local structure of an image, so as to achieve the better estimation of the gray scale value of a to-be-denoised pixel. The method has better robustness for thedenoising effect in different noise environments. Through the analysis of the histogram information and the image matrix information of an image local area, the method achieves the image block size self-adaption, smoothing parameter value self-adaption and search window shape self-adaption based on the non-local mean value algorithm, thereby achieving the effective noise inhibition of the detailparts of the image and maintaining the texture information of the detail parts as much as possible, and achieving the improvement of a conventional non-local mean value algorithm. An experiment resultindicates that the denoising effect and texture part of the improved algorithm are remarkably improved.

Description

[0001] technology neighborhood [0002] The invention relates to the technical field of image processing, in particular to an image denoising processing method, which can be used to process Gaussian noise existing in natural images. Background technique [0003] In the direction of noise suppression and denoising of digital images, the non-local mean denoising algorithm (NLM) uses the property of Gaussian white noise with zero mean value and the characteristics of other similar image blocks in the image, by searching for other similar image blocks in the local neighborhood. Similar pixels are weighted and averaged to estimate the true value of the target pixel. This algorithm has a good denoising effect and the ability to preserve image details. Since it was proposed, many improvement methods have been proposed to improve the performance of NLM. The improved factors include: computational efficiency, the shape of the search window, The size of the image block is adaptive, the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T5/40
CPCG06T5/002G06T5/40
Inventor 胡靖萧澍吴锡周激流
Owner CHENGDU UNIV OF INFORMATION TECH
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