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A Noise Removal Method of Shock Noise Image

A technology that impacts noise and images, applied in image enhancement, image data processing, instruments, etc., can solve the problems of reducing image signal-to-noise ratio, image blurring, etc., to improve the restoration of image signal-to-noise ratio, increase calculation speed, and improve denoising speed effect

Active Publication Date: 2019-01-15
DALIAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this kind of selective filtering method based on the median, mean, and extremum (maximum or minimum) in the local window, when performing high-density salt and pepper noise image restoration, the larger filtering window is likely to blur the image, thereby reducing the Image SNR after restoration

Method used

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  • A Noise Removal Method of Shock Noise Image
  • A Noise Removal Method of Shock Noise Image
  • A Noise Removal Method of Shock Noise Image

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0089] This embodiment is used to compare and illustrate the effect of the image grid processing adopted in the present invention. To this end, two comparative experiments were set up: Experiment 1 without gridding processing, and Experiment 2 with gridding processing, and the image noise density in the experiment was set to 99%.

[0090] 1) In Experiment 1, set the grid size s=512, a=0, which is equivalent to not performing grid processing, and the experimental results are as follows image 3 shown. It can be seen from the experimental results that choosing the least squares filter SLSF effectively repairs the salt and pepper noise pollution pixels, but the processing time is 220 seconds.

[0091] 2) In Experiment 2, set grid size s=256, a=0, the experimental results are as follows Figure 4 As shown, the processing time was reduced to 40 seconds, from Figure 4 As a result, it can be seen that compared image 3 There is no obvious difference in the results of Experiment ...

Embodiment 2

[0094] This embodiment is used to illustrate the effect of using different grid sizes s for different noise pollution densities in the present invention. Four experiments were carried out, namely experiments 3-6.

[0095] Experiment 3: The noise pollution density is 99%, the image block size is a multiple of 16*16, the smallest image block is 16*16, and the largest is 512*512. The relationship between image grid size s and denoising image quality PSNR is as follows: Figure 5 As shown, where the ordinate is PSNR, and the abscissa is the image grid size s. The experimental results show that when the noise density is 99%, when the image grid size is 32, the PSNR reaches a high value. Before the image block is 176, the PSNR of the image changes significantly, and after exceeding 176, the change is relatively slow .

[0096] Experiment 4: The noise pollution density is 95%, the image block size is a multiple of 8*8, the smallest image block is 8*8, and the largest is 232*232. ...

Embodiment 3

[0101] This embodiment is used to illustrate the effect of using the extended image block to eliminate the block effect of the denoising image.

[0102] Experiment 7: The noise density is 90%, the image grid size s=40, a=0, the experimental results are as follows Figure 9 As shown, it can be seen that there are obvious block effects in the denoising results.

[0103] Experiment 8: The noise density is 90%, the image grid size s=40, a=2, the experimental results are as follows Figure 10 shown, it can be seen Figure 9 The blocking effect phenomenon in the system has been well suppressed.

[0104] Experiment 9:

[0105] Taking the Lena image as an example, the simulation test is carried out, and the comparison and analysis results with the existing salt and pepper noise denoising algorithm are as follows: figure 2 shown. exist figure 2 In , the existing algorithm and the effect diagram of noise reduction of the algorithm of the present invention under the condition of ...

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Abstract

The invention discloses a method for denoising an impact noise image, which has the following steps: establishing a marking matrix F of the noise contaminated image I, and dividing the image I and the marking matrix F into M respectively according to the impact noise pollution density ρ. *N grids; respectively extract the image block Tm, n and the mark block Lm, n composed of the pixels in the (m, n) grid in the image I and the matrix F; by traversing the mark block Lm, n elements in the image block Tm, establish the contaminated pixel set E and the non-polluted pixel set P in the image block Tm, n; obtain the linear prediction system parameter Ψ; calculate the contaminated pixel value pair according to the linear prediction system parameter Ψ and the Euclidean distance matrix De The contaminated pixel value is subjected to a matrix transposition operation to obtain the noise-removed pixel value E; the denoised image block Tm,n is obtained; the image block Tm,n is written back to the image to replace the image in the (m,n)th grid Pixels; traverse all grids in image I.

Description

technical field [0001] The invention relates to a method for removing impact noise in an image. Involving patent classification number G06 Calculation; Calculation; Counting G06T General image data processing or generation G06T5 / 00 Enhancement or restoration of images, such as building a similar figure from bitmap to bitmap. Background technique [0002] Digital imaging sensors such as CCD or CMOS are widely used in industry, entertainment, civil and other fields. In the actual use process, affected by factors such as manufacturing defects, device aging, and transmission errors, there is impact noise pollution in the obtained imaging images. Impact noise includes: salt and pepper noise and random impact noise. Among them, salt and pepper noise often manifests as constant extremely bright or extremely dark pixels. Taking a grayscale image with a value range of 0-255 as an example, the value of salt and pepper noise pixels is usually 255 or 0. Such as figure 1 As shown, a i...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T5/70
Inventor 刘文龙王阳王心怡
Owner DALIAN UNIV OF TECH
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