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Secondary de-noising image processing method

An image processing and image technology, applied in the field of image processing, can solve problems such as inability to achieve results, loss of image information, and inability to overcome morphological deficiencies

Inactive Publication Date: 2012-08-01
JIANGSU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The structural element used in the prior art is a non-zero square matrix structural element with a size of n*n, and the image is processed by morphology. Although the initial value of n can be set according to experience, the image processing the effect is not good
Although the existing morphological filtering methods can improve the image processing effect to varying degrees, they cannot overcome the inherent shortcomings of morphology.
Due to the inherent operational characteristics of morphological core operations erosion and dilation, that is, morphological erosion and dilation are both extreme operations. This extreme operation is likely to cause image information loss while removing noise, especially for images with low signal-to-noise ratio. Denoising, can not achieve satisfactory results
[0004] It is easy to cause loss of image information, especially for image denoising with low signal-to-noise ratio, which cannot achieve satisfactory results

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0102] An image processing method for secondary denoising, comprising the steps of:

[0103] ① input pixel size is the image f of W*H; define the unit structure element SE of zero square matrix, its size is n*n;

[0104] ②Set the number of particles as m, the space dimension as D, and the position of the i-th particle is defined by the D-dimensional vector X i =(X i1 , X iD ) indicates that the flying speed of the i-th particle is represented by the D-dimensional vector V i =(V i1 , V iD ) means; the initial position and initial velocity of the particle are random numbers between (0, 1); the size of the unit structural element SE is obtained according to the initial position, that is, the initial value of n is obtained; that is, for all The initial value of n is set, n≤W and n≤H;

[0105] ③ using n as the initial value of the unit structure element SE to perform morphological image operations on the image f to calculate the intermediate output image and its peak signal-t...

Embodiment 2

[0111] On the basis of Example 1, the balanced morphological operation includes:

[0112] The equilibrium erosion operation is defined using the unit structure element SE:

[0113] , that is, the collection The median value of the inner gray value is used as the gray value of the pixel point (i, j) of the image f; wherein, the value range of i is [0, W-n], the value range of j is [0, H-n], and h takes The value range is [0,n-1], and the value range of k is [0,n-1];

[0114] The equilibrium expansion operation is defined using the unit structure element SE:

[0115] , that is, the collection The median value of the inner gray value is used as the gray value of the pixel point (i, j) of the image f; wherein, the value range of i is [0, W+n-2], and the value range of j is [0, H +n-2], the value range of h is [0,n-1], and the value range of k is [0,n-1];

[0116] Using n as the unit structural element SE of the initial value to perform the balanced corrosion operation o...

Embodiment 3

[0119] On the basis of Example 1, the binary morphology includes:

[0120] 1. Parameter Description of Binary Morphology

[0121] ① Binary opening and closing

[0122] Use image B to perform open operation on image A, using the symbol , which is defined as:

[0123] (1.4)

[0124] In fact, the opening operation is the result of erosion first and then dilation.

[0125] Use image B to perform closed operation on image A, using the symbol , which is defined as:

[0126] (1.5)

[0127] In fact, the closing operation is the result of first dilation and then erosion.

[0128] ②Binary Morphological Operation Properties

[0129] Property 1 Expansion operation has commutative and associative laws

[0130] (1.6)

[0131] Property 2 Erosion and dilation operations satisfy the distribution law

[0132] (1.7)

[0133] (1.8) (1.9)

[0134] (1.10)

[01...

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Abstract

The invention relates to a secondary de-noising image processing method. The method comprises the following steps of: firstly, defining a zero matrix unit structure element, and processing an image f through morphology operation to obtain a corresponding peak signal to noise ratio (PSNR); then updating the speed and position of particles through a particle swarm optimization technology by taking the peak signal to noise ratio as a cost function, and taking a transformed value of the optimized particle position as the size of the structure element; and finally performing morphology operation on the image f by using the structure element so as to obtain an optimal output image. By the method, the shortcoming caused by adopting extremum operation in the conventional morphology is overcome; the size of the structure element can be self-adaptively acquired according to the noise pollution degree of the image; impulse noise in the image can be effectively removed; and the method can be widely used in the fields of image processing, license information extracting and edge detecting.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image processing method for secondary denoising. Background technique [0002] In the process of image generation and transmission, various noises are often introduced. These noises not only destroy the real information of the image, but also seriously affect the visual effect of the image. Images disturbed by noise can be filtered out by linear or nonlinear filtering methods. Since image details are reflected as high-frequency components in the frequency domain, they are easily confused with high-frequency noise. Therefore, how to maintain image details and effectively filter noise has always been a key issue in image processing. Morphological filtering belongs to nonlinear filtering, which is a representative and promising filter at present, and its theoretical basis is mathematical morphology. [0003] The structural element used in the prior art is a non-zero sq...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/30
Inventor 朱幼莲黄成程钦倪福银刘舒祺许致火
Owner JIANGSU UNIV OF TECH
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