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Image denoising method based on evidence theory
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A technology of evidence theory and image, which is applied in the field of image denoising based on evidence theory, and can solve problems such as can not be used for random processes with non-stationary noise, inconvenient application of vector cases, and difficult to meet the conditions of observation data.
Inactive Publication Date: 2014-02-19
HEBEI NORMAL UNIV
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The disadvantage of the Wiener filter is that it is difficult to meet the conditions for obtaining all observation data in a semi-infinite time interval. At the same time, it cannot be used in the case of a non-stationary random process with noise, and it is inconvenient to apply to the vector case.
Therefore, Wiener filtering is rarely used in practical problems
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Embodiment 1
[0069] Embodiment 1 (using a 3×3 square template, see figure 2 ):
[0070] The denoising steps when using a 3×3 square template are as follows:
[0071] (1) Read in a frame of image, let the first pixel be the current pixel;
[0072] (2) Determine whether the current pixel is a noise point, if so, proceed according to the following steps, if not, set the next pixel as the current pixel, and then judge until a noise point is encountered or the algorithm ends; the above judgment noise The point method adopts the interval method, that is, the gray value between 0-5 or 250-255 is a noise point, otherwise it is a signal point;
[0073] (3) With the noise point as the center, construct a 3 x 3 A square template that contains both the horizontal and vertical axes 3 pixels (see figure 2 ,exist figure 2 Marked in is the grayscale evidence of each pixel);
[0074] If the pixel on the image boundary is noise, then for the centered pixel 3 x 3 The part of the square templat...
Embodiment 2
[0103] Embodiment 2 (using a 5×5 square template, see image 3 ):
[0104] The difference between embodiment 2 and embodiment 1 is as follows:
[0105] Calculate the standard gray value of the current pixel :
[0106] According to the following formula (1), calculate the sum of the row gray values of the pixels in the 1st-2nd row and 4-5th row of the 5×5 square template :
[0107] (1)
[0108] in: subscript i = 1,2,3,5;
[0109] i = -2,-1, 0, 1, 2;
[0110] j = -2,-1, 0, 1, 2;
[0111] is the pixel in the 5×5 square template ( i,j ) grayscale;
[0112] Calculate the gray level evidence of each pixel in the first, second, fourth, and fifth lines according to the following formula , , , :
[0118] a. The first round of grayscale evidence synthesis:
[0119] First, the grayscale ev...
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Abstract
The invention relates to an image denoising method based on an evidence theory. The image denoising method based on the evidence theory comprises the steps that (1) a frame image is read, and a first pixel serves as a current pixel; (2) whether the current pixel is a noise point is judged; (3) with the noise point as the center, an n*n square template is constructed on the image for a target pixel; (4) the standard gray value of the current pixel in the n*n square template is calculated by utilizing an evidence synthesis formula; (5) the absolute value of the difference value between the gray level of the current pixel and the standard gray value of the current pixel is calculated according to the formula in the step (4); (6) the threshold value of the gray value is preset; (7) noise is removed, and when the current pixel is defined as the noise, correction is carried out by using the standard gray value of the current pixel to replace the gray level of the current pixel. According to the denoising method, based on gray evidences of the pixel calculated through the evidence theory, gray evidence synthesis is carried out by utilizing the evidence synthesis formula, calculation complexity can be effectively reduced, and a more accurate denoising result is obtained.
Description
technical field [0001] The invention relates to the technical field of image processing, in particular to an image denoising method based on evidence theory. Background technique [0002] With the popularity of various digital instruments and digital products, images and videos have become the most commonly used information carriers in human activities. They contain a lot of information about objects and become the main way for people to obtain original information from the outside world. However, in the acquisition of images, In the process of transmission and storage, it is often disturbed and affected by various noises, which will degrade the image quality, and the quality of the image preprocessing algorithm is directly related to the effect of subsequent image processing, such as image segmentation, target recognition, edge extraction, etc. , so in order to obtain a high-quality digital image, it is necessary to denoise the image, while maintaining the integrity of the ...
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