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Bivariate nonlocal average filtering de-noising method for X-ray image

A non-local averaging, X-ray technology, applied in the field of X-ray image denoising, can solve problems such as algorithm application, and achieve the effects of good convergence, fast denoising, and fast processing speed

Active Publication Date: 2012-07-25
YUN NAN ELECTRIC TEST & RES INST GRP CO LTD ELECTRIC INST +1
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Problems solved by technology

But so far, no one has applied this algorithm to non-local mean optimization

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  • Bivariate nonlocal average filtering de-noising method for X-ray image
  • Bivariate nonlocal average filtering de-noising method for X-ray image

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Embodiment Construction

[0071] Bivariate non-local average filter X-ray image denoising method, the method of the present invention is,

[0072] 1). Selection method of fuzzy denoising window

[0073] The non-local average filtering algorithm has a premise assumption: the local space where the sampling data is located is linear, that is, each sampling point has a similar relationship with its neighbors, which is linearly represented by the weight contribution value;

[0074] The learning goal of this algorithm is to make full use of the redundant relationship between pixels in the low-dimensional space, and set the weight in each neighborhood according to the similarity of the gray distribution, that is, it is assumed that the embedded mapping window is locally linear Under the condition, the irrelevant pixels are minimized and the original image is reconstructed;

[0075] Let c(x, y) be the X-ray scanning image function, r(x, y) be the ideal image function, n(x, y) be the noise image function, x, y...

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Abstract

The invention provides a bivariate nonlocal average filtering de-noising method for an X-ray image. The method is characterized by comprising the following steps: 1) a selecting method of a fuzzy de-noising window; and 2) a bivariate fuzzy adaptive nonlocal average filtering algorithm. The method has the beneficial effects that in order to preferably remove the influence caused by the unknown quantum noise existing in an industrial X-ray scan image, the invention provides the bivariate nonlocal fuzzy adaptive non-linear average filtering de-noising method for the X-ray image, in the method, a quantum noise model which is hard to process is converted into a common white gaussian noise model, the size of a window of a filter is selected by virtue of fuzzy computation, and a relevant weight matrix enabling an error function to be minimum is searched. A particle swarm optimization filtering parameter is introduced in the method, so that the weight matrix can be locally rebuilt, the influence of the local relevancy on the sample data can be reduced, the algorithm convergence rate can be improved, and the de-noising speed and precision for the industrial X-ray scan image can be improved, so that the method is suitable for processing the X-ray scan image with an uncertain noise model.

Description

technical field [0001] The invention belongs to the field of X-ray image denoising, and in particular relates to a method for denoising X-ray images using a fuzzy adaptive parameter-adjusted bivariate non-local mean filter (Fuzzy Adaptive Non Local means, abbreviated as FANL means) algorithm. Background technique [0002] With the continuous development of industrial X-ray flaw detection technology, more and more requirements are put forward for the quality of X-ray scanning images, which requires effective elimination of noise information generated in the real-time detection process. Due to the narrow gray scale range of X-ray scanning images, blurred defect edges, high image noise, and sometimes submerged defect features, it affects the analysis and evaluation of the inspected workpiece based on X-ray images. With the continuous update of X-ray acquisition equipment, the new X-ray scanning machine can more comprehensively and accurately describe the information of industri...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 魏杰王达达王妍玮于虹王磊赵现平吴章勤梁洪闫文斌李金郭涛涛
Owner YUN NAN ELECTRIC TEST & RES INST GRP CO LTD ELECTRIC INST
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