CN101087365B - A method for filtering image mixed noise - Google Patents
A method for filtering image mixed noise Download PDFInfo
- Publication number
- CN101087365B CN101087365B CN2006100919045A CN200610091904A CN101087365B CN 101087365 B CN101087365 B CN 101087365B CN 2006100919045 A CN2006100919045 A CN 2006100919045A CN 200610091904 A CN200610091904 A CN 200610091904A CN 101087365 B CN101087365 B CN 101087365B
- Authority
- CN
- China
- Prior art keywords
- pixel
- noise
- image
- filtering
- impulsive noise
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000001914 filtration Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000001174 ascending effect Effects 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims 2
- 230000002950 deficient Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003706 image smoothing Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
Images
Landscapes
- Image Processing (AREA)
- Facsimile Image Signal Circuits (AREA)
Abstract
The invention discloses a method of filtering image mixed noise, it includes step 1: selecting input first pixel in original image which includes mixed noise; step 2: judging whether the pixel belongs to pulse noise, if it is, enter step 3. if else enter step 4; step 3: the pixel is median filter processed to filter the pulse noise, then enter step 6; step 4: ensuring the members of the un-pulse noise pixel; step 5: gauss noise is filtered by filter; step 6: checking whether the all pixels are processed, if it is not, the next pixel is fetched, then turn to step 2; if it is, new image whose mixed noise is filtered is output. The method can filter mixed noise in image, at the same time edge and detail information of image can be protected well, and calculation speed is high.
Description
Technical field:
The present invention relates to a kind of method of filtering image mixed noise in the image processing field, relate in particular in the realtime graphic process field filtering picture noise.
Background technology:
In image processing field, it is very important that the filtering of picture noise is handled for successive image.In existing image de-noising method commonly used, the gaussian filtering method is more effective to filtering image Gaussian noise, but damages image edge information easily simultaneously, thereby image is thickened.And the median filtering method impulsive noise of filtering image well, but for filtering image Gaussian noise poor effect then.Both common defectives are that all neighborhood territory pixels in the filter window are all adopted identical processing, thereby can introduce error, cause the damage of image border and detailed information.
Filter method is a kind of image de-noising method that can protect image border and detailed information well on the same group.The main thought of this method is only to find out neighborhood territory pixel close with the center pixel distance feature in the filter window as participating in filtering with the group membership, its process is to carry out the ascending order arrangement according to neighborhood territory pixel in the filter window and former pixel characteristic distance value earlier, differentiate the same group membership who finds out this pixel by Fisher again, replace original pixel characteristic value with the weighted feature value that belongs in the filter window with group membership's pixel then.This shows that filtering on the same group can regard a kind of gaussian filtering that has binary mask as, wherein 1 this filter window neighborhood territory pixel of expression is that the same group membership of former pixel will participate in filtering, and 0 represents it is not participate in filtering with the group membership.Filtering has overcome the drawback that all pixels in the filter window of gaussian filtering in the past all participate in filtering on the same group, thus well filtering the image Gaussian noise, can protect edge of image and detailed information again.But the filtering impulsive noise of filtering image effectively on the same group, its amount of calculation is very big when sorting out the same group membership of each pixel simultaneously, particularly when filter window was chosen greatly, its denoising speed was extremely slow, thereby this method is not suitable for the realtime graphic processing very much.And generally, original image always comprises two kinds of noises the most common, i.e. impulsive noise and Gaussian noise.So the mixed noise of filtering image apace, the image de-noising method that can protect image border and detailed information again becomes the research focus in the present image processing field.
Summary of the invention
The technical problem to be solved in the present invention is to propose a kind of method of filtering image mixed noise; this method fast and effeciently contains the mixed noise of gaussian sum pulse in the filtering image; simultaneously can protect edge of image and detailed information well again, be particularly useful for the processing of realtime graphic.
Method of filtering image mixed noise of the present invention comprises:
Step 1: the first pixel in original image that contains mixed noise of choosing input;
Step 2: differentiate this pixel and whether belong to impulsive noise,, then enter step 3, otherwise directly enter step 4 if this pixel belongs to impulsive noise;
Step 3: this pixel is carried out medium filtering handle, this impulsive noise is got rid of, directly enter step 6 then;
Step 4: the same group membership who determines this non-pulse noise pixel;
Step 5: by filtering filtering Gaussian noise on the same group;
Step 6: whether all pixels of check image dispose, and then obtain the next pixel of image if be untreated, and step 2 is returned in redirect then; If handle the new images of then exporting behind the filtering mixed noise;
Wherein, the described classification with the group membership of described step 4 further comprises: calculate this pixel grey scale second differnce in the horizontal direction; Calculate this pixel grey scale second differnce in vertical direction; Calculate the two-dimentional deviation branch of this pixel grey scale; Calculate the direction of this pixel grey scale changing features minimum; In this pixel filter window, obtain the same group membership that neighborhood territory pixel on this minimum direction constitutes this pixel.
Compare with existing method, adopting the method for the invention is to judge whether pending pixel belongs to impulsive noise earlier, if impulsive noise is then got rid of by medium filtering, otherwise carry out same group membership's classification of this pixel, carry out filtering on the same group then and filter out Gaussian noise.The inventive method is the mixed noise in the filtering image effectively, can protect edge of image and detailed information well simultaneously, and the arithmetic speed of its denoising is also higher.
Description of drawings:
Fig. 1 is the overview flow chart of the filtering image mixed noise method that proposes of the present invention.
Fig. 2 is the present invention differentiates impulsive noise by calculating pixel local energy value a flow chart.
Fig. 3 is the flow chart that the present invention sorts out the member method on the same group of pixel.
Embodiment:
Below in conjunction with accompanying drawing and embodiment method of the present invention is described, Fig. 1 is the overview flow chart of the filtering image mixed noise method that proposes of the present invention.
Step 1: the first pixel in original image that contains mixed noise of choosing input.
Step 2: differentiate this pixel and whether belong to impulsive noise.The method of differentiating usually can by calculate this pixel level, vertically or the feature difference methods such as (as gray scale, rgb values etc.) on the gradient direction differentiate impulsive noise, also can judge whether this pixel is impulsive noise according to the local energy feature of pixel.In the present embodiment, to judge according to the local energy feature of pixel whether this pixel is that impulsive noise is that example describes.The local energy feature of pixel has mainly been portrayed the local difference information of pixel characteristic value on level, a plurality of directions such as vertical, and the local energy value of impulsive noise respective pixel is often obviously than the local energy value height of image smoothing zone or fringe region pixel usually.Therefore can effectively judge impulsive noise by the local energy value of calculating pixel, and calculate simply fast, protect edge of image and detailed information again well.
If this pixel belongs to impulsive noise, then enter following step 3, otherwise directly enter step 4.
Step 3: this pixel is carried out medium filtering handle, this impulsive noise is got rid of, directly enter step 6 then.
Step 4: the same group membership who determines this non-pulse noise pixel.Before using the Gaussian noise of filtering filtering image on the same group, a step of most critical is that the neighborhood territory pixel in this pixel filter window is carried out sorting out with the group membership, promptly determines the same group membership of this pixel.Here so-calledly be meant one group of close with the center pixel feature in filter window neighborhood territory pixel with the group membership.Determine that pixel has multiple with group membership's method, relatively Chang Yong method is to calculate earlier the distance of the characteristic vector of each neighborhood territory pixel and center pixel in filter window, then these distance values are carried out ascending order and arrange, adopt Fisher to differentiate at last and come definite member on the same group.Also having a kind of method practical, the same group membership of definite this pixel quickly is directly will be classified as the same group membership of this pixel at the neighborhood territory pixel on this pixel characteristic gradient vertical direction (being the minimum direction of changing features) in filter window, and wherein the minimum direction of the changing features of pixel can be extracted exactly by the feature difference information method of calculating this pixel.The amount of calculation of this method is very little, can save the classification time with the group membership greatly.
Step 5: by filtering filtering Gaussian noise on the same group.After the same group membership of this pixel determines, can come the filtering Gaussian noise by filtering on the same group.The main processing procedure of filtering is the same group membership's of pixel weighted feature value to be substituted the original characteristic value of former this pixel on the same group, thereby reaches the purpose of removing noise.
Step 6: whether all pixels of check image dispose.Then obtain the next pixel of image if be untreated, step 2 is returned in redirect then; If handle the new images of then exporting behind the filtering mixed noise.
Below in conjunction with Fig. 2 and Fig. 3, coming filtering one size with method of the present invention is that pulse and the Gaussian Mixture noise that contains in the gray level image (with m=256) of m*m is that example further describes, and its concrete steps are as follows:
1 according to image from left to right, from top to bottom scanning sequency obtains the first pixel in original image that contains mixed noise;
Whether 2 differentiate this pixel belongs to impulsive noise.Fig. 2 is a flow chart of differentiating impulsive noise by calculating pixel local energy value, and its specific implementation process is as follows:
(coordinate is (x to 2-1 with this test point pixel, y)) be the center, choose the filter window of a n*n (n=9 herein) size, this window size choose complexity and the accuracy that is related to calculating, window is big more, and calculation times will increase but accuracy also can increase;
Pixel grey scale average μ in the 2-2 calculation of filtered window:
(x is that image coordinate is (x, y) gray value of pixel y) to p in the formula;
Formula came the local energy of calculating pixel below 2-3 adopted:
E(x，y)＝|2*(p(x，y)-μ)
2-(p(x-1，y)-μ)*(p(x+1，y)-μ)
-(p(x，y-1)-μ)*(p(x，y+1)-μ)| (2)
The local energy average that 2-4 chooses all pixels of image is used as the local energy threshold value, and this threshold value Th is calculated as follows:
E in the formula
nThe local energy value of n pixel in the presentation video;
2-5 judges according to the local energy threshold value (whether x is impulsive noise y), even to this pixel at last
E(x，y)＞Th (4)
Judge that then it is an impulsive noise, the 3rd step below carrying out.Otherwise be not impulsive noise, directly carry out next the 4th step.
3 paired pulses noise pixels carry out medium filtering to be handled, and the filter window size is still chosen 9*9, carries out for the 6th step then.
4 pairs of non-pulse noise pixels carry out sorting out with the group membership, and Fig. 3 is for sorting out the flow chart of this pixel with the group membership, and it is implemented as follows:
4-1 calculates this pixel grey scale second differnce in the horizontal direction earlier, that is:
Wherein:
Δ
xp(x，y)＝p(x+1，y)-p(x，y) (6)
4-2 calculates this pixel grey scale second differnce in vertical direction again, that is:
Wherein:
Δ
yp(x，y)＝p(x，y+1)-p(x，y) (8)
4-3 calculates the two-dimentional deviation branch of this pixel grey scale then, that is:
Δ
xyp(x，y)＝Δ
xp(x，y+1)-Δ
xp(x，y) (9)
4-4 calculates the direction ξ of this pixel grey scale changing features minimum again, that is:
4-5 obtains the same group membership that neighborhood territory pixel on this minimum direction ξ constitutes this pixel at last in this pixel filter window.
5 uses are carried out filtering on the same group with the group membership and are come the filtering Gaussian noise.After the same group membership of this pixel determines, substitute the gray feature value of former pixel with regard to available with the weighting gray feature value of group membership's pixel, that is:
P wherein
i(x y) belongs to the same group membership's of this detection pixel gray feature vector, w
iBe the weight coefficient of corresponding gaussian filtering, s is the number with the group membership.
Whether all pixels of 6 check image dispose by above-mentioned steps, if be untreated then still according to image from left to right, from top to bottom scanning sequency obtains image next one pixel, step 2 is returned in redirect then; If handle the new images of then exporting behind the filtering mixed noise.
Claims (6)
1. method of filtering image mixed noise is characterized in that may further comprise the steps:
Step 1: the first pixel in original image that contains mixed noise of choosing input;
Step 2: differentiate this pixel and whether belong to impulsive noise,, then enter step 3, otherwise directly enter step 4 if this pixel belongs to impulsive noise;
Step 3: this pixel is carried out medium filtering handle, this impulsive noise is got rid of, directly enter step 6 then;
Step 4: the same group membership who determines this non-pulse noise pixel;
Step 5: by filtering filtering Gaussian noise on the same group;
Step 6: whether all pixels of check image dispose, and then obtain the next pixel of image if be untreated, and step 2 is returned in redirect then; If handle the new images of then exporting behind the filtering mixed noise;
Wherein, the described classification with the group membership of described step 4 further comprises:
Calculate this pixel grey scale second differnce in the horizontal direction;
Calculate this pixel grey scale second differnce in vertical direction;
Calculate the two-dimentional deviation branch of this pixel grey scale;
Calculate the direction of this pixel grey scale changing features minimum;
In this pixel filter window, obtain the same group membership that neighborhood territory pixel on this minimum direction constitutes this pixel.
2. method according to claim 1 is characterized in that whether this pixel of differentiation in the described step 2 belongs to impulsive noise and further comprise: differentiate impulsive noise by calculating the feature difference of this pixel on level, vertical or gradient direction.
3. method according to claim 1 is characterized in that whether this pixel of differentiation in the described step 2 belongs to impulsive noise and further comprise: judge according to the local energy feature of pixel whether this pixel is impulsive noise.
4. method according to claim 3, described local energy feature are judged and can be comprised:
With this test point pixel is the center, chooses the filter window of a n*n size;
Pixel grey scale average in the calculation of filtered window;
The local energy of calculating pixel;
The local energy average of choosing all pixels of image is used as the local energy threshold value;
If the local energy value of this pixel is judged that then it is an impulsive noise, otherwise is not impulsive noise greater than the local energy threshold value.
5. according to the described method of one of claim 1-4, it is characterized in that described step 4 further comprises: the distance of calculating earlier the characteristic vector of each neighborhood territory pixel and center pixel in filter window, then these distance values are carried out ascending order and arrange, adopt Fisher to differentiate at last and come definite member on the same group.
6. according to the described method of one of claim 1-4, it is characterized in that described step 4 further comprises: in filter window, directly will be classified as the same group membership of this pixel at the neighborhood territory pixel on this pixel characteristic gradient vertical direction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2006100919045A CN101087365B (en) | 2006-06-10 | 2006-06-10 | A method for filtering image mixed noise |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2006100919045A CN101087365B (en) | 2006-06-10 | 2006-06-10 | A method for filtering image mixed noise |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101087365A CN101087365A (en) | 2007-12-12 |
| CN101087365B true CN101087365B (en) | 2010-08-18 |
Family
ID=38938099
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2006100919045A Expired - Fee Related CN101087365B (en) | 2006-06-10 | 2006-06-10 | A method for filtering image mixed noise |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101087365B (en) |
Families Citing this family (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI456982B (en) | 2010-03-30 | 2014-10-11 | Realtek Semiconductor Corp | Image processing device and spatial noise reducing method |
| CN101860667B (en) * | 2010-05-06 | 2012-11-07 | 中国科学院西安光学精密机械研究所 | Method for quickly removing mixed noise in image |
| CN102447817B (en) * | 2010-09-30 | 2014-11-26 | 瑞昱半导体股份有限公司 | Image processing device and space image noise eliminating method |
| CN105657216B (en) * | 2010-11-19 | 2019-07-05 | 美国亚德诺半导体公司 | Component for low smooth noise reduction filters |
| TWI488494B (en) | 2011-04-28 | 2015-06-11 | Altek Corp | Method of multi-frame image noise reduction |
| CN102413338B (en) * | 2011-11-21 | 2014-04-30 | 晶门科技（深圳）有限公司 | Image processing method and device of spotted noise |
| CN104095640A (en) * | 2013-04-03 | 2014-10-15 | 达尔生技股份有限公司 | Oxyhemoglobin saturation detecting method and device |
| CN104240188B (en) * | 2013-06-14 | 2017-09-12 | 华为技术有限公司 | A kind of method and device for filtering out noise in pixel |
| CN105096263B (en) * | 2014-05-22 | 2018-10-09 | 安凯(广州)微电子技术有限公司 | image filtering method and device |
| CN104599252B (en) * | 2015-01-29 | 2017-06-16 | 清华大学深圳研究生院 | A kind of processing method for removing image mixed noise |
| CN105787902B (en) * | 2016-03-22 | 2018-11-27 | 天津大学 | Utilize the image denoising method of block sorting detection noise |
| KR102690986B1 (en) | 2016-03-23 | 2024-08-02 | 삼성디스플레이 주식회사 | Head-mounted device and display device |
| CN106373098B (en) * | 2016-08-30 | 2019-04-23 | 天津大学 | Method for suppressing random impulsive noise based on non-similar pixel statistics |
| CN109003247B (en) * | 2018-07-26 | 2021-06-15 | 吉林大学 | Method for removing color image mixed noise |
| CN109146816B (en) * | 2018-08-22 | 2020-08-04 | 浙江大华技术股份有限公司 | Image filtering method and device, electronic equipment and storage medium |
| CN109471014B (en) * | 2018-10-30 | 2021-01-19 | 江苏赛诺格兰医疗科技有限公司 | Detection signal simulation forming circuit and detector board card test platform |
| CN112861817A (en) * | 2021-03-31 | 2021-05-28 | 国网上海市电力公司 | Instrument noise image processing method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1350747A (en) * | 2000-01-13 | 2002-05-22 | 皇家菲利浦电子有限公司 | Noise reduction |
| CN1383673A (en) * | 2000-06-15 | 2002-12-04 | 皇家菲利浦电子有限公司 | Noise filtering image sequence |
| CN1405734A (en) * | 2002-10-28 | 2003-03-26 | 武汉大学 | Method for reinforcing edge of medical picture |
-
2006
- 2006-06-10 CN CN2006100919045A patent/CN101087365B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1350747A (en) * | 2000-01-13 | 2002-05-22 | 皇家菲利浦电子有限公司 | Noise reduction |
| CN1383673A (en) * | 2000-06-15 | 2002-12-04 | 皇家菲利浦电子有限公司 | Noise filtering image sequence |
| CN1405734A (en) * | 2002-10-28 | 2003-03-26 | 武汉大学 | Method for reinforcing edge of medical picture |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101087365A (en) | 2007-12-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101087365B (en) | A method for filtering image mixed noise | |
| CN105787902B (en) | Utilize the image denoising method of block sorting detection noise | |
| US8300949B2 (en) | Edge detection technique having improved feature visibility | |
| CN106373098B (en) | Method for suppressing random impulsive noise based on non-similar pixel statistics | |
| CN101930597B (en) | Mathematical morphology-based image edge detection method | |
| CN102782706A (en) | Text enhancement of a textual image undergoing optical character recognition | |
| Shenbagavalli et al. | Classification of soil textures based on laws features extracted from preprocessing images on sequential and random windows | |
| US11703477B2 (en) | Nondestructive testing method and device for detecting and distinguishing internal defect and external defect of wire rope | |
| CN112085734A (en) | GAN-based image restoration defect detection method | |
| CN103337053A (en) | Switching non-local total variation based filtering method for image polluted by salt and pepper noise | |
| CN107742291A (en) | A kind of the defects of photovoltaic glass detection method and device | |
| CN104318586B (en) | Adaptive morphological filtering-based motion blur direction estimation method and device | |
| CN109146905A (en) | For the CANNY operator edge detection algorithm of low-light level environment | |
| CN106254723B (en) | A kind of method of real-time monitoring video noise interference | |
| CN101853496A (en) | Hybrid image filtering method based on target scale | |
| CN106780545A (en) | A kind of Weak target energy accumulation Enhancement Method of combination time-space domain | |
| Vignesh et al. | Performance and Analysis of Edge detection using FPGA Implementation | |
| Zhao et al. | Analysis of image edge checking algorithms for the estimation of pear size | |
| Niu et al. | Application of CEM algorithm in the field of tunnel crack identification | |
| CN106548460B (en) | Laplce's detection and local similarity color image pulse noise filtering method | |
| CN118229568B (en) | Image edge de-pseudo noise reduction method and device, equipment and storage medium | |
| Chudasama et al. | Survey on Various Edge Detection Techniques on Noisy Images | |
| CN109919853B (en) | PIV image preprocessing method based on particle feature retrieval | |
| CN114663451B (en) | Edge detection method and system capable of processing noise and texture | |
| CN114511591B (en) | Track tracking method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20171205 Address after: 065200 Langfang city of Hebei province Sanhe Yanjiao Development Zone Chaobai people two No. 18 Building 2 unit 303 room Patentee after: Cheng Guihua Address before: 518057, Nanshan District high tech Industrial Park, Guangdong province Shenzhen science and technology south road Zhongxing building A block 5 Patentee before: ZTE Corporation |
|
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Cheng Guihua Inventor before: Cao Gang Inventor before: Xu Lifeng Inventor before: Luo Hongyu |
|
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20100818 Termination date: 20180610 |