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Correlation analysis method suitable for noisy image

A correlation analysis and image technology, applied in the field of computer science and image processing, can solve problems such as boundary contours and blurred lines, and achieve good results

Active Publication Date: 2020-07-10
JIANGSU UNIV
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Problems solved by technology

[0006] In the process of image formation, transmission, etc., it is inevitable to be disturbed by noise, and some images have very serious noise. The noise in the image is often intertwined with the signal, which will make the details of the image itself, such as boundary contours, lines, etc. indistinct

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  • Correlation analysis method suitable for noisy image
  • Correlation analysis method suitable for noisy image
  • Correlation analysis method suitable for noisy image

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Embodiment Construction

[0030] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0031] Such as figure 1 A correlation analysis method suitable for noisy images is shown, including the following steps:

[0032] Step 1, collect images X(D×N) and Y(D×M) as the training data set used in the experiment, and add noise to the training data set, where N and M represent the dimensions of image X and Y respectively , that is, each column of X and Y represents an attribute of the image, such as color, angle, light perception and other attributes; D represents the number of samples of the image.

[0033] Using cosine similarity to construct sample penalty factor d xi and d yj , iterat...

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Abstract

The invention discloses a correlation analysis method suitable for a noisy image. The method comprises the following steps: constructing a sample penalty factor by using cosine similarity, and furtherconstructing identification matrixes Dx and Dy; adding sample selection and feature selection to the training image data set X and the training image data set Y respectively, and then obtaining an improved correlation analysis model; converting the improved correlation analysis model into a problem of solving a feature value and a feature vector to obtain the feature value and the feature vector;and inputting the feature values and the feature vectors into a k nearest neighbor algorithm to obtain the classification accuracy of the algorithm, and further obtaining the correlation degree between the image data set X and the image data set Y. According to the method, sample selection and feature selection are added to each data point in the image, so that the influence of noise on an imagedata set can be effectively suppressed.

Description

technical field [0001] The invention belongs to the field of computer science and image processing technology, in particular to a correlation analysis method suitable for noise-containing images. Background technique [0002] Association analysis is a well-known family of statistical tools used to analyze associations between variables or sets of variables. Canonical correlation analysis, principal component analysis, and partial least squares are among the more effective correlation analysis methods. Correlation analysis methods are widely used in a wide range of fields, and there are countless studies on algorithms. The following will focus on several correlation analysis algorithms and their applications in image processing. [0003] Canonical correlation analysis was proposed by H.Helling in 1936. For now, its theory has been relatively perfect. But as far as its application is concerned, it is currently mainly used in correlation analysis and predictive analysis amon...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/211G06F18/24147G06F18/214Y02T10/40
Inventor 沈项军强娜
Owner JIANGSU UNIV
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