CN103116879A - Neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method - Google Patents

Neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method Download PDF

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CN103116879A
CN103116879A CN2013100839229A CN201310083922A CN103116879A CN 103116879 A CN103116879 A CN 103116879A CN 2013100839229 A CN2013100839229 A CN 2013100839229A CN 201310083922 A CN201310083922 A CN 201310083922A CN 103116879 A CN103116879 A CN 103116879A
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王珏
罗姗
邹永宁
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Chongqing University
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Abstract

The invention discloses a neighborhood windowing based non-local mean value CT (Computed Tomography) imaging de-noising method for mainly improving original CT projected image data by adopting a search region and weight calculation in a non-local mean value technology. The main implementation process of the neighborhood windowing based non-local mean value CT imaging de-noising method comprises: (1) setting a mean square error of a Gaussian window for a collected original CT projected image; (2) finding all the similar blocks in the search region; (3) calculating a Gaussian Euclidean distance between the similar blocks and a block where a current spot locates; (4) calculating a similarity weight by utilizing a negative index function, wherein the level of similarity increases along with the increase of the weight; (5) obtaining a product of the similarity weight and a distance weight to obtain a mixed weight; (6) performing weighted average on all the pixel point values in the search region by using the mixed weight to obtain a corrected pixel point value; and (7) reconstructing de-noised projection image data to form a final chromatographic X-ray image. By the adoption of the neighborhood windowing based non-local mean value CT imaging de-noising method disclosed by the invention, the purpose of restoring the original image better and improving the de-noising performances for the image can be realized.

Description

A kind of non-local mean CT imaging denoising method based on neighborhood window addition
Technical field
The invention belongs to the CT technical field of imaging, relate to a kind of non-local mean CT imaging denoising method based on neighborhood window addition.
Background technology
Along with the development of science and technology, the application of CT technology becomes more and more universal, and CT detects and plays an important role in daily life and research and production.But in the CT imaging process, tend to be subject to inevitable noise, irregular because noise is random, Gaussian noise is to occur maximum noises in imaging process usually.
CT imaging denoising can be carried out also can carrying out after rebuilding before rebuilding, and before rebuilding, denoising can make noise spread without reconstruction, and denoising before the CT imaging is carried out denoising to data for projection exactly.
Multiple known noise-removed technology is arranged in modern technologies, and traditional image de-noising method has gaussian filtering, mean filter.They are all the hypothesis of directly utilizing image space continuity or piecewise continuity, think that the noise at the pixel in the neighborhood centered by certain pixel and center pixel in image place is obeyed identical distribution.These methods are carried out the smoothing processing of same type to level and smooth district, marginarium and the texture area in image, so the detailed structure of meeting blurred picture, the resolution of image after the reduction denoising.More classical filtering and noise reduction method had appearred again afterwards, such as: medium filtering, its denoising effect is better, but the meeting Inhibitory signal; Field filtering, it can effectively solve " fuzzy " phenomenon in Gaussian smoothing, but can introduce false edge and staircase effect; Total variation method (TV) can be gone noise more thoroughly, but resolution is descended.
The non-local mean method that is proposed by Buades in recent years is one of very outstanding method in image denoising field, its basic thought be if in image two pixels around field structure similar, these two pixels are also similar so; These two pixels may be arranged in any position of image, so, search such pixel and also should launch in whole image range.In the non-local mean method, for the current denoising pixel for the treatment of, all pixels similar to this dot structure of search in image, as weights, the gradation of image value after denoising is obtained by these pixel weightings with the similarity of block structure.The contribution of the method for non-local mean is that the regional area pixel that in the past method thinks that substantially all the pixel of image only is adjacent is similar, it thinks that pixel and the pixel in its non-conterminous zone also may be similar, as long as these two pixels have similar field structure.But it also has the following disadvantages: 1. weights calculate not accurate enough; 2. processing speed is slow; 3. image resolution ratio is not high.
Summary of the invention
In view of this, technical matters to be solved by this invention is to have proposed a kind of non-local mean CT imaging denoising method based on neighborhood window addition, added the impact of distance on similarity on the basis of originally only considering the gray scale similarity, thereby the accuracy to the image block similarity measurement is improved, and has improved the effect of image denoising.
The object of the present invention is achieved like this:
A kind of non-local mean CT imaging denoising method based on neighborhood window addition provided by the invention comprises the following steps:
S1: obtain the original CT projecting image data;
S2: parameter is carried out initialization;
S3: calculate the similarity weights, adopt in non-local mean method search projected image and the current dot structure piece for the treatment of that the denoising dot structure is similar, calculate the similarity of similar dot structure piece and as the current similarity weights for the treatment of the denoising pixel;
S4: the similarity weights are obtained revising weights by adding the correction of Gauss's window function;
S5: treat that to current the denoising pixel is weighted on average according to revising weights, obtain the current correction gray-scale value for the treatment of the denoising pixel:
S6: utilize and revise the gray-scale value that gray-scale value replaces pixel in the original CT projecting image data of inputting, obtain the denoising perspective view.
Further, described parameter comprises dot structure block size t, the Gaussian window parameter of positive pixel to be repaired
Figure 2013100839229100002DEST_PATH_IMAGE001
, the rate of decay
Figure 173603DEST_PATH_IMAGE002
, the described rate of decay
Figure 970658DEST_PATH_IMAGE002
Standard deviation according to Gaussian noise
Figure 2013100839229100002DEST_PATH_IMAGE003
Determine.
Further, described calculating similarity weights are realized by following steps:
S31: determine the region of search
Figure 641810DEST_PATH_IMAGE004
S32: utilize following formula to noisy data for projection should in erect image vegetarian refreshments i to be repaired and region of search
Figure 815303DEST_PATH_IMAGE004
The similarity of interior pixel j is calculated, and obtains the region of search
Figure 888301DEST_PATH_IMAGE004
The weights of interior all pixels:
Figure 2013100839229100002DEST_PATH_IMAGE005
；
Wherein,
Figure 918574DEST_PATH_IMAGE006
Normalization coefficient,
Figure 2013100839229100002DEST_PATH_IMAGE007
,
Figure 14706DEST_PATH_IMAGE008
In the difference presentation video, with i, the size centered by j is The gray-scale value of image block, the rate of decay of parameter h control characteristic function; Weights
Figure 788627DEST_PATH_IMAGE010
Satisfy
Figure 2013100839229100002DEST_PATH_IMAGE011
And
Figure 716132DEST_PATH_IMAGE012
Further, described correction weights are realized by following steps:
S41: to the similarity weights by adding the correction of Gauss's window function:
Figure 2013100839229100002DEST_PATH_IMAGE013
Wherein, x is i, the Euclidean distance between j.
Further, describedly treat that to current it is on average to realize by following steps that the denoising pixel is weighted:
According to the weights of all pixels in the region of search that calculates, to all pixel weighted means in the region of search, obtain the revised gray-scale value of pixel:
Figure 917306DEST_PATH_IMAGE014
；
Wherein,
Figure 2013100839229100002DEST_PATH_IMAGE015
Be the set of search box pixel point,
Figure 563051DEST_PATH_IMAGE016
Gray-scale value for pixel in the region of search.
Further, described region of search
Figure 812767DEST_PATH_IMAGE004
For length is the square region of 6 times of mean square deviations, wherein, the region of search
Figure 860357DEST_PATH_IMAGE004
Size by the mean square deviation of Gauss function
Figure 170116DEST_PATH_IMAGE001
Determine.
The invention has the advantages that: the present invention adopts the non-local mean denoising method of neighborhood window addition that the projecting image data of CT imaging is rebuild, and has the following advantages:
1. the present invention can calculate the similarity between pixel in noisy perspective view more accurately, and then can calculate more accurately the gray-scale value of erect image vegetarian refreshments to be repaired;
2. the present invention can more accurately calculate the gray-scale value of erect image vegetarian refreshments to be repaired, and then can better keep image resolution preferably in denoising.
Adopt the inventive method, the projecting image data after denoising is reconstructed into final chromatographical X-ray image, can reach and recover better original image, improves the purpose to the Denoising performance.
Description of drawings
In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with accompanying drawing, wherein:
Fig. 1 is process flow diagram of the present invention;
Fig. 2 is the projected image that the present invention uses;
Fig. 3 is the reconstructed image that the present invention uses;
Fig. 4 is the noisy projected image that the present invention uses;
Fig. 5 is the noisy reconstruction from projections imaging that the present invention uses;
Fig. 6 is with the reconstructed image after present non-local mean filtering method denoising;
Fig. 7 is with the reconstructed image after the inventive method denoising.
Embodiment
Below with reference to accompanying drawing, the preferred embodiments of the present invention are described in detail; Should be appreciated that preferred embodiment only for the present invention is described, rather than in order to limit protection scope of the present invention.
Embodiment 1
Fig. 1 is process flow diagram of the present invention, Fig. 2 is the projected image that the present invention uses, Fig. 3 is the reconstructed image that the present invention uses, Fig. 4 is the noisy projected image that the present invention uses, Fig. 5 is the noisy reconstruction from projections imaging that the present invention uses, and Fig. 6 is with the reconstructed image after present non-local mean filtering method denoising, and Fig. 7 is with the reconstructed image after the inventive method denoising, as shown in the figure: noisy reconstruction from projections imaging provided by the invention neighborhood window addition non-local mean denoising method comprises the following steps:
Step 1. is obtained the original CT projecting image data, and the noise in image is all usually take Gauss's additive white noise as main;
Step 2. pair parametric t (positive pixel tile size to be repaired),
Figure 320735DEST_PATH_IMAGE001
(Gaussian window parameter),
Figure 374141DEST_PATH_IMAGE002
(rate of decay) carries out initialization, gets respectively
Figure 2013100839229100002DEST_PATH_IMAGE017
, 1.67,0.1,
Figure 276238DEST_PATH_IMAGE002
Standard deviation by Gaussian noise Determine,
Figure 111656DEST_PATH_IMAGE018
Step 3. is determined the region of search , length is that the square region of 6 times of mean square deviations is the region of search, wherein
Figure 725357DEST_PATH_IMAGE004
Size by the mean square deviation of Gauss function Determine,
Figure 2013100839229100002DEST_PATH_IMAGE019
Step 4. utilize following formula to noisy data for projection should in erect image vegetarian refreshments i to be repaired and region of search the similarity of pixel j calculate, obtain the weights of all pixels in the region of search;
Figure 484552DEST_PATH_IMAGE020
Wherein,
Figure 942078DEST_PATH_IMAGE006
Normalization coefficient,
Figure 490871DEST_PATH_IMAGE007
, In the difference presentation video, with i, the size centered by j is
Figure 908263DEST_PATH_IMAGE009
The gray-scale value of image block, the rate of decay of parameter h control characteristic function.
Weights
Figure 107163DEST_PATH_IMAGE010
Satisfy
Figure 572779DEST_PATH_IMAGE011
And
Step 5. pair similarity weights are by adding the correction of Gauss's window function;
Figure 2013100839229100002DEST_PATH_IMAGE021
Wherein, x is i, the Euclidean distance between j.
Step 6. to all pixel weighted means in the region of search, obtains the revised gray-scale value of pixel according to the weights of all pixels in the region of search that calculates:
Figure 648369DEST_PATH_IMAGE014
Wherein,
Figure 385381DEST_PATH_IMAGE015
Be the set of search box pixel point,
Figure 971083DEST_PATH_IMAGE016
Gray-scale value for pixel in the region of search;
The gray-scale value of pixel after step 7. is used and revised
Figure 400927DEST_PATH_IMAGE022
The gray-scale value that replaces pixel in the noisy projected image of input obtains the perspective view after denoising;
Step 8. is rebuild final image to realize global de-noising by the projecting image data after denoising.
Embodiment 2
The difference of the present embodiment and embodiment 1 only is:
The present embodiment illustrates the non-local mean CT imaging denoising method based on neighborhood window addition provided by the invention to the processing procedure of projecting image data:
Obtain original CT projecting image data such as Fig. 2, Fig. 4 is that to add noise criteria poor to Fig. 2 be 0.1 noisy projecting image data.Fig. 3 is the image after Fig. 2 is rebuild, and Fig. 5 is the image after Fig. 4 is rebuild.
Under above-mentioned experiment condition, use directly respectively and rebuild, present non-local mean filtering and noise reduction is rebuild and the inventive method is rebuild the data for projection denoising.
Experimental result is to have shown noisy CT data for projection is directly rebuild effect as shown in Figure 5.
With rebuilding effect as shown in Figure 6 after present non-local mean filtering and noise reduction, wherein
Figure 439290DEST_PATH_IMAGE018
,
Figure 979993DEST_PATH_IMAGE003
Poor for the contained noise criteria of image, the size of search window is , similar window size is
Figure 685781DEST_PATH_IMAGE017
, as can be seen from Figure 6: the method noise inhibiting ability is relatively good, but can not well keep the edge of image.
Denoising result of the present invention as shown in Figure 7, as can be seen from Figure 7: its denoising effect is better than traditional non-local mean filtering method, has kept the edge of image in fine denoising, has strengthened resolution;
It is 0.01 Gaussian noise that test projected image in Fig. 2 is added variance, with the evaluation index of MSE as denoising effect, and with noisy, the non-local mean denoising, after denoising of the present invention, reconstructed image compares, in MSE value such as table 1.
Table 1 is denoising not, non-local mean denoising, reconstructed results contrast after denoising of the present invention.
Table 1
Method Not denoising NLM The present invention
MSE 0.0137 0.0090 0.0040
The above is only the preferred embodiments of the present invention, is not limited to the present invention, and obviously, those skilled in the art can carry out various changes and modification and not break away from the spirit and scope of the present invention the present invention.Like this, if within of the present invention these are revised and modification belongs to the scope of claim of the present invention and equivalent technologies thereof, the present invention also is intended to comprise these changes and modification interior.

Claims (6)

1. non-local mean CT imaging denoising method based on neighborhood window addition is characterized in that: comprise the following steps:
S1: obtain the original CT projecting image data;
S2: parameter is carried out initialization;
S3: calculate the similarity weights, adopt in non-local mean method search projected image and the current dot structure piece for the treatment of that the denoising dot structure is similar, calculate the similarity of similar dot structure piece and as the current similarity weights for the treatment of the denoising pixel;
S4: the similarity weights are obtained revising weights by adding the correction of Gauss's window function;
S5: treat that to current the denoising pixel is weighted on average according to revising weights, obtain the current correction gray-scale value for the treatment of the denoising pixel:
S6: utilize and revise the gray-scale value that gray-scale value replaces pixel in the original CT projecting image data of inputting, obtain the denoising perspective view.
2. the non-local mean CT imaging denoising method based on neighborhood window addition according to claim 1, it is characterized in that: described parameter comprises dot structure block size t, the Gaussian window parameter of positive pixel to be repaired
Figure 2013100839229100001DEST_PATH_IMAGE001
, the rate of decay
Figure 221910DEST_PATH_IMAGE002
, the described rate of decay
Figure 345724DEST_PATH_IMAGE002
Standard deviation according to Gaussian noise
Figure 2013100839229100001DEST_PATH_IMAGE003
Determine.
3. the non-local mean CT imaging denoising method based on neighborhood window addition according to claim 1, it is characterized in that: described calculating similarity weights are realized by following steps:
S31: determine the region of search
Figure 692392DEST_PATH_IMAGE004
S32: utilize following formula to noisy data for projection should in erect image vegetarian refreshments i to be repaired and region of search
Figure 167236DEST_PATH_IMAGE004
The similarity of interior pixel j is calculated, and obtains the region of search
Figure 257551DEST_PATH_IMAGE004
The weights of interior all pixels:
Figure 2013100839229100001DEST_PATH_IMAGE005
；
Wherein,
Figure 626085DEST_PATH_IMAGE006
Normalization coefficient, ,
Figure 878075DEST_PATH_IMAGE008
In the difference presentation video, with i, the size centered by j is
Figure 2013100839229100001DEST_PATH_IMAGE009
The gray-scale value of image block, the rate of decay of parameter h control characteristic function; Weights
Figure 840214DEST_PATH_IMAGE010
Satisfy
Figure 2013100839229100001DEST_PATH_IMAGE011
And
Figure 536818DEST_PATH_IMAGE012
4. the non-local mean CT imaging denoising method based on neighborhood window addition according to claim 1, it is characterized in that: described correction weights are realized by following steps:
S41: to the similarity weights by adding the correction of Gauss's window function:
Figure 2013100839229100001DEST_PATH_IMAGE013
Wherein, x is i, the Euclidean distance between j.
5. the non-local mean CT imaging denoising method based on neighborhood window addition according to claim 1 is characterized in that: describedly treat that to current it is on average to realize by following steps that the denoising pixel is weighted:
According to the weights of all pixels in the region of search that calculates, to all pixel weighted means in the region of search, obtain the revised gray-scale value of pixel:
Figure 900803DEST_PATH_IMAGE014
；
Wherein,
Figure 2013100839229100001DEST_PATH_IMAGE015
Be the set of search box pixel point,
Figure 323694DEST_PATH_IMAGE016
Gray-scale value for pixel in the region of search.
6. the non-local mean CT imaging denoising method based on neighborhood window addition according to claim 1, is characterized in that: described region of search
Figure 773130DEST_PATH_IMAGE004
For length is the square region of 6 times of mean square deviations, wherein, the region of search Size by the mean square deviation of Gauss function Determine.
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CN111724325A (en) * 2020-06-24 2020-09-29 湖南国科微电子股份有限公司 Trilateral filtering image processing method and device
CN111724325B (en) * 2020-06-24 2023-10-31 湖南国科微电子股份有限公司 Trilateral filtering image processing method and trilateral filtering image processing device
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CN116912102B (en) * 2023-05-11 2024-04-09 上海宇勘科技有限公司 Edge-preserving image denoising method and system based on non-local mean value
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CN117788570B (en) * 2024-02-26 2024-05-07 山东济矿鲁能煤电股份有限公司阳城煤矿 Bucket wheel machine positioning method and system based on machine vision

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Application publication date: 20130522