CN109949725A - A kind of AOI system image grayscale standardized method and system - Google Patents
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Abstract
The invention discloses a kind of AOI system image grayscale standardized method and systems, specific steps are as follows: establish simulated defect sample image for picture to be checked, it counts and compares the detection of defect sample in picture to be detected under different images grayscale as a result, determining the optimal gray value I of picture to be detected according to detection resultr；The compensating parameter of picture to be checked is calculated using the overall intensity mean value of picture to be checked, optimal sum of the grayscale values pre-set image grey level compensation formula, the gray value of each pixel of picture to be checked is compensated using the compensating parameter of calculating, to realize the grey scale of picture to be checked.
Description
Technical field
The invention belongs to field of image detection, and in particular to a kind of AOI system image grayscale standardized method and system.
Background technique
AOI (Automatic Optic Inspection, automatic optics inspection technology) is widely used in display screen defect
In detection.In the AOI testing process of display screen, display screen successively shows that picture to be detected, visual sensor sync pulse jamming wait for
The picture of detection enters defects detection process after the completion of capture.The consistency of picture quality is automatic optics inspection defect detection
The basis of rate.
However, the consistency of image grayscale is primarily present following two aspects: the one of image grayscale to detection influence factor
The assessment of cause property and the target gray value of picture to be detected.Due to display defect characteristic morphology multiplicity, the wave above and below image grayscale
When dynamic, the contrast and defect area of defect can be affected therewith, directly affect assessment of the system to display screen defect rank；
Simultaneously as display defect characteristic morphology multiplicity, the feature of different gray value of images, defect performance can change.Such as it is faint
Shinny defect can be fainter in high gray image, and faint obfuscation defect is difficult to differentiate in the image of low ash rank.
In display screen automatic production line, display screen gamma curve is the main reason for image gray-scale level fluctuates.Display screen
Gamma value indicate the relationship between display grayscale value and display brightness.Since the gamma curve of different display screens exists
Difference, by taking L48 picture as an example, brightness when showing same grey menu L48 between different liquid crystal modules is inconsistent.Without Gamma
After the liquid crystal module of correction enters automatic optics inspection station, when visual sensor time for exposure and backlight illumination remain unchanged,
The overall intensity unusual fluctuations of image to be checked, assessment of the Interference Detection system to defect rank.Camera automatic exposure can be used,
The hardware adjustments mode such as adaptive backlight makes picture entirety gray value standard to be checked, however this method will increase system time-consuming
And hardware cost is high, while mainly according to scene, debugging provides empirical value repeatedly for the assessment of picture grey values to be checked, thus lack
The appraisal procedure of standard.
Summary of the invention
Aiming at the above defects or improvement requirements of the prior art, the present invention provides a kind of AOI system image grayscale standards
Change method and system, for picture to be checked establish simulated defect sample image so that it is determined that picture to be detected optimal gray value,
And then calculate compensating parameter and the gray value of each pixel of picture to be checked is compensated, to realize picture to be checked
Grey scale.
To achieve the above object, according to one aspect of the present invention, a kind of AOI system image grayscale standardization side is provided
Method, specific steps are as follows:
S1. simulated defect sample image is established for picture to be checked, counts and compares picture to be detected under different images grayscale
The detection of middle defect sample is as a result, determine the optimal gray value Ir of picture to be detected according to detection result；
S2. using the overall intensity mean value of picture to be checked, optimal sum of the grayscale values pre-set image grey level compensation formula calculate to
The compensating parameter for examining picture, compensates the gray value of each pixel of picture to be checked using the compensating parameter of calculating, from
And realize the grey scale of picture to be checked.
As a further improvement of the present invention, step S1 specifically:
S1.1 picture image to be checked specifies the point defect sample of each grayscale of regional simulation, to generate simulation point defect sample
This；
S1.2 acquires the simulation point defect sample image under each picture centre gray average；
S1.3 detects the simulation point defect sample image under each picture centre gray average, counts each picture centre ash
Spend the defective data of mean value Imitating point defect sample image.
As a further improvement of the present invention, step S1.3 specifically: utilize point defect detection method and identical detection
The defect of simulation point defect sample image under the gray average of parameter extraction different images center counts different images center gray scale
Middle detection defect number, defect contrast and the defect area of simulation point defect sample image under mean value；According to defective data
Determine the optimal gray value I of picture to be checkedr。
As a further improvement of the present invention, the overall intensity mean value computation formula of picture to be checked is
In formula, R is picture area to be checked, and the gray value of pixel (x, y) is f (x, y) in R, and A is the face of picture to be checked
Product.
As a further improvement of the present invention, step S2 calculates the compensating parameter of picture to be checked using iterative algorithm, specifically
Are as follows:
Preset initial gray value IsWith the number of iterations N, initial penalty coefficient γ is calculated0=sqrt (Is/Ic)；
Since n=0, γ is enabledn+1=γn+ 1, by γnSubstitute into pre-set image grey level compensation formula In(x, y)=aγn*[f
(x,y)]γn, obtaining picture image coordinate to be checked is the compensated gray value I of (x, y) pixeln(x, y) further calculates to obtain
The overall intensity mean value of the corresponding image of nth iteration, i.e.,
Stopping criterion for iteration is | In-Ir| it is less than preset threshold, γ when iteration endsnAs required gray value penalty coefficient
γ；
Wherein, γnGray value penalty coefficient when for nth iteration, a are the coefficient of pre-set image grey level compensation formula.
To achieve the above object, other side according to the invention provides a kind of AOI system image grayscale standardization
System, the system include simulated defect processing module and grey level compensation processing module,
Simulated defect processing module is used to establish simulated defect sample image for picture to be checked, counts and compares different images
Under grayscale in picture to be detected the detection of defect sample as a result, determining the optimal gray value of picture to be detected according to detection result
Ir；
Grey level compensation processing module is used for overall intensity mean value, optimal gray value Ir and pre-set image using picture to be checked
Grey level compensation formula calculates the compensating parameter of picture to be checked, using the compensating parameter of calculating to each pixel of picture to be checked
Gray value compensates, to realize the grey scale of picture to be checked.
As a further improvement of the present invention, simulated defect processing module includes that sequentially connected simulation point defect generates mould
Block, image capture module and defective data processing module, wherein
Point defect generation module is simulated to be used to specify the point defect sample of each grayscale of regional simulation in picture image to be checked,
To generate simulation point defect sample；
Image capture module is used to acquire the simulation point defect sample image under each picture centre gray average；
Defective data processing module is used to detect the simulation point defect sample image under each picture centre gray average, system
Count the defective data of each picture centre gray average Imitating point defect sample image.
As a further improvement of the present invention, simulated defect processing module utilizes point defect detection method and identical detection
The defect of simulation point defect sample image under the gray average of parameter extraction different images center counts different images center gray scale
Middle detection defect number, defect contrast and the defect area of simulation point defect sample image under mean value；According to defective data
Determine the optimal gray value I of picture to be checkedr。
As a further improvement of the present invention, the overall intensity mean value computation formula of picture to be checked is
In formula, R is picture area to be checked, and the gray value of pixel (x, y) is f (x, y) in R, and A is the face of picture to be checked
Product.
As a further improvement of the present invention, grey level compensation processing module calculates the compensation of picture to be checked using iterative algorithm
Parameter, specifically:
Preset initial gray value IsWith the number of iterations N, initial penalty coefficient γ is calculated0=sqrt (Is/Ic)；
Since n=0, γ is enabledn+1=γn+ 1, by γnSubstitute into pre-set image grey level compensation formula In(x, y)=aγn*[f
(x,y)]γn, obtaining picture image coordinate to be checked is the compensated gray value I of (x, y) pixeln(x, y) further calculates to obtain
The overall intensity mean value of the corresponding image of nth iteration, i.e.,
Stopping criterion for iteration is | In-Ir| it is less than preset threshold, γ when iteration endsnAs required gray value penalty coefficient
γ；
Wherein, γnGray value penalty coefficient when for nth iteration, a are the coefficient of pre-set image grey level compensation formula.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, have below beneficial to effect
Fruit:
A kind of AOI system image grayscale standardized method of the invention and system, it is poor for gamma curve between liquid crystal display panel
The skimble-scamble problem of testing image caused by different establishes simulated defect sample image so that it is determined that picture to be detected for picture to be checked
The optimal gray value in face, so calculate compensating parameter and the gray value of each pixel of picture to be checked is compensated, from
And realize the grey scale of picture to be checked, equivalent is in adjusting visual sensor exposure, standardized images overall intensity
Later, liquid crystal module defect is expressed more authentic and valid in the picture, easily facilitates the just assessment display screen of AOI detection system
Defect rank.
A kind of AOI system image grayscale standardized method of the invention and system, utilize point defect detection method and phase
Same detection parameters extract the defect of the simulation point defect sample image under the gray average of different images center, are conducive to accurately mention
The information of defect is taken, to further increase the accuracy of the optimal gray value of picture to be checked.
A kind of AOI system image grayscale standardized method of the invention and system calculate picture to be checked using iterative algorithm
The compensating parameter in face compensates the gray value of each pixel of picture to be checked, lacks to be more advantageous to and extract liquid crystal module
Sunken real information, to further increase the assessment defect rank of AOI image detecting system.
Detailed description of the invention
Fig. 1 is a kind of schematic diagram of AOI system image grayscale standardized method of the embodiment of the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
In addition, as long as technical characteristic involved in the various embodiments of the present invention described below is each other not
Constituting conflict can be combined with each other.The present invention is described in more detail With reference to embodiment.
Fig. 1 is a kind of schematic diagram of AOI system image grayscale standardized method of the embodiment of the present invention.As shown in Figure 1, tool
Body step are as follows:
S1. simulated defect sample image is established for picture to be checked, counts and compares picture to be detected under different images grayscale
The detection of middle defect sample is as a result, determine the optimal gray value Ir of picture to be detected according to detection result；
Specifically:
S1.1 picture image to be checked specifies the point defect sample of each grayscale of regional simulation, to generate simulation point defect sample
This；
Specifically: in all picture central areas to be detected, simulation point defect is generated, includes 256 sub-pixels in region
Point defect (display screen minimum unit defect), 256 defect grayscale are 0-255；
S1.2 acquires the simulation point defect sample image under each picture centre gray average；
S1.3 detects the simulation point defect sample image under each picture centre gray average, counts each picture centre ash
Spend the defective data of mean value Imitating point defect sample image.
Specifically: it is extracted under the gray average of different images center using point defect detection method and identical detection parameters
The defect of point defect sample image is simulated, the middle inspection of the simulation point defect sample image under the gray average of different images center is counted
Defect number, defect contrast and defect area out；The optimal gray value of picture to be checked is determined according to defective data.
Wherein, it can be lacked by counting design simulation in the simulation point defect sample image under each picture centre gray average
It falls into the defect sum detected at region and obtains detection defect number；It can be by counting the simulation under each picture centre gray average
The corresponding contrast mean value of defect is detected at design simulation defect area in point defect sample image and variance obtains Defect Comparison
Degree；It can be by being examined at design simulation defect area in the simulation point defect sample image under statistics different images center gray average
The corresponding defect area mean value of defect and variance obtain defect area out；
It include defect number, defect contrast and defect area, preferably each picture centre gray average in defective data
Under the corresponding picture centre gray average of simulation point defect sample image detection the largest number of images of defect be optimal gray scale
It is worth, the simulation point defect sample image detection defect contrast mean value under less preferred each picture centre gray average is highest
The corresponding picture centre gray average of image is optimal gray value, the simulation point under last preferably each picture centre gray average
It is optimal gray value that defect sample image, which detects the corresponding picture centre gray average of the maximum image of defect area mean value,.
S2. using the overall intensity mean value of picture to be checked, optimal sum of the grayscale values pre-set image grey level compensation formula calculate to
The compensating parameter for examining picture, compensates the gray value of each pixel of picture to be checked using the compensating parameter of calculating, from
And realize the grey scale of picture to be checked.
The overall intensity mean value of picture to be checked is calculated specifically, acquiring picture to be checked, i.e. measured panel enters detection microscope carrier
And picture to be checked is shown one by one, visual sensor shoots the picture to be checked of Display panel；Picture to be checked is calculated after acquiring picture to be checked
The overall intensity mean value I in facec, note R be picture area to be checked, then in R pixel (x, y) gray value be f (x, y), A be to
The area of picture is examined, the overall intensity mean value computation formula of picture to be checked is
Joined using the compensation that optimal sum of the grayscale values pre-set image grey level compensation formula calculates each sub-pixel point of picture to be checked
Number specifically: pre-set image grey level compensation formula is I (x, y)~aγ*[f(x,y)]γ, in formula, I (x, y) is for image coordinate
The compensated gray value of (x, y) pixel, f (x, y) are the actual grey value that image coordinate is (x, y) pixel, and γ is gray scale
It is worth penalty coefficient, a is the coefficient of pre-set image grey level compensation formula.
The overall gray value standardization of picture to be checked refers to the equal primary system of overall intensity of the same picture to be checked between each panel
One is the optimal gray value I of picture to be checkedr.Using L48 picture image to be checked as example, the L48 picture to be checked of each display screen to be checked
Image has the optimal gray value I of unified picture to be checked after the pre-treatmentr。
For reach unified gray average it needs to be determined that picture to be checked grey level compensation coefficient gamma, calculated using iterative algorithm true
Detailed process is as follows for the grey level compensation coefficient gamma of fixed picture to be checked:
Preset initial gray value IsWith the number of iterations N, initial penalty coefficient γ is calculated0=sqrt (Is/Ic)；From n=
0 starts, and enables γn+1=γn+ 1, by γnSubstitute into pre-set image grey level compensation formula In(x, y)=aγn*[f(x,y)]γn, obtain to
Inspection picture image coordinate is the compensated gray value I of (x, y) pixeln(x, y) further calculates to obtain nth iteration correspondence
Image overall intensity mean value, i.e.,
Stopping criterion for iteration is | In-Ir| it is less than preset threshold, γ when iteration endsnAs required gray value penalty coefficient
γ。
After gray value of image standardization to be checked, into defects detection process.
A kind of AOI system image grayscale standardized system, the system include at simulated defect processing module and grey level compensation
Manage module, wherein
Simulated defect processing module is used to establish simulated defect sample image for picture to be checked, counts and compares different images
Under grayscale in picture to be detected the detection of defect sample as a result, determining the optimal gray value of picture to be detected according to detection result
Ir；
Simulated defect processing module includes sequentially connected simulation point defect generation module, image capture module and defect number
According to processing module, wherein
Point defect generation module is simulated to be used to specify the point defect sample of each grayscale of regional simulation in picture image to be checked,
To generate simulation point defect sample image.In all picture central areas to be detected, simulation point defect is generated, includes in region
256 sub- pixel point defects (display screen minimum unit defect), 256 defect grayscale are 0-255.
Image capture module is used to acquire the simulation point defect sample image under each picture centre gray average；
Defective data processing module is used to detect the simulation point defect sample image under each picture centre gray average, system
The defective data of each picture centre gray average Imitating point defect sample image is counted, determines picture to be checked according to defective data
Optimal gray value Ir。
Specifically: it is extracted under the gray average of different images center using point defect detection method and identical detection parameters
The defect of point defect sample image is simulated, the middle inspection of the simulation point defect sample image under the gray average of different images center is counted
Defect number, defect contrast and defect area out；
Wherein, it can be lacked by counting design simulation in the simulation point defect sample image under each picture centre gray average
It falls into the defect sum detected at region and obtains detection defect number；It can be by counting the simulation under each picture centre gray average
The corresponding contrast mean value of defect is detected at design simulation defect area in point defect sample image and variance obtains Defect Comparison
Degree；It can be by being examined at design simulation defect area in the simulation point defect sample image under statistics different images center gray average
The corresponding defect area mean value of defect and variance obtain defect area out；
It include defect number, defect contrast and defect area, preferably each picture centre gray average in defective data
Under the corresponding picture centre gray average of simulation point defect sample image detection the largest number of images of defect be optimal gray scale
It is worth, the simulation point defect sample image detection defect contrast mean value under less preferred each picture centre gray average is highest
The corresponding picture centre gray average of image is optimal gray value, the simulation point under last preferably each picture centre gray average
It is optimal gray value that defect sample image, which detects the corresponding picture centre gray average of the maximum image of defect area mean value,.
Grey level compensation processing module is used for the overall intensity mean value using picture to be checked, optimal sum of the grayscale values pre-set image ash
Degree compensation formula calculates the compensating parameter of picture to be checked, using the compensating parameter of calculating to the ash of each pixel of picture to be checked
Angle value compensates, to realize the grey scale of picture to be checked.
Grey level compensation processing module calculates the grey level compensation coefficient gamma for determining picture to be checked using iterative algorithm specifically:
Preset initial gray value IsWith the number of iterations N, initial penalty coefficient γ is calculated0=sqrt (Is/Ic)；From n=
0 starts, and enables γn+1=γn+ 1, by γnSubstitute into pre-set image grey level compensation formula In(x, y)=aγn*[f(x,y)]γn, obtain to
Inspection picture image coordinate is the compensated gray value of (x, y) pixel, further calculates to obtain the corresponding image of nth iteration
Overall intensity mean value, i.e.,
Stopping criterion for iteration is | In-Ir| it is less than preset threshold, γ when iteration endsnAs required gray value penalty coefficient
γ。
After gray value of image standardization to be checked, into defects detection process.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (10)
1. a kind of AOI system image grayscale standardized method, which is characterized in that specific steps are as follows:
S1. simulated defect sample image is established for picture to be checked, count and compare and lack in picture to be detected under different images grayscale
The detection of sample is fallen into as a result, determining the optimal gray value I of picture to be detected according to detection resultr；
S2. picture to be checked is calculated using the overall intensity mean value of picture to be checked, optimal sum of the grayscale values pre-set image grey level compensation formula
The compensating parameter in face compensates the gray value of each pixel of picture to be checked using the compensating parameter of calculating, thus real
The grey scale of existing picture to be checked.
2. a kind of AOI system image grayscale standardized method according to claim 1, which is characterized in that step S1 is specific
Are as follows:
S1.1 picture image to be checked specifies the point defect sample of each grayscale of regional simulation, to generate simulation point defect sample；
S1.2 acquires the simulation point defect sample image under each picture centre gray average；
S1.3 detects the simulation point defect sample image under each picture centre gray average, counts each picture centre gray scale
The defective data of mean value Imitating point defect sample image.
3. a kind of AOI system image grayscale standardized method according to claim 2, which is characterized in that step S1.3 tool
Body are as follows: the simulation point defect under the gray average of different images center is extracted using point defect detection method and identical detection parameters
The defect of sample image counts the middle detection defect of the simulation point defect sample image under the gray average of different images center
Number, defect contrast and defect area；The optimal gray value I of picture to be checked is determined according to defective datar。
4. a kind of AOI system image grayscale standardized method according to any one of claim 1-3, which is characterized in that
The overall intensity mean value computation formula of picture to be checked is
In formula, R is picture area to be checked, and the gray value of pixel (x, y) is f (x, y) in R, and A is the area of picture to be checked.
5. a kind of AOI system image grayscale standardized method according to claim 4, which is characterized in that step S2 is utilized
Iterative algorithm calculates the compensating parameter of picture to be checked, specifically:
Preset initial gray value IsWith the number of iterations N, initial penalty coefficient γ is calculated0=sqrt (Is/Ic)；
Since n=0, γ is enabledn+1=γn+ 1, by γnSubstitute into pre-set image grey level compensation formula Obtaining picture image coordinate to be checked is the compensated gray value I of (x, y) pixeln(x, y) is further calculated
To the overall intensity mean value of the corresponding image of nth iteration, i.e.,
Stopping criterion for iteration is | In-Ir| it is less than preset threshold, γ when iteration endsnAs required gray value penalty coefficient γ；
Wherein, γnGray value penalty coefficient when for nth iteration, a are the coefficient of pre-set image grey level compensation formula.
6. a kind of AOI system image grayscale standardized system, which includes simulated defect processing module and grey level compensation processing
Module, which is characterized in that
The simulated defect processing module is used to establish simulated defect sample image for picture to be checked, counts and compares different images
Under grayscale in picture to be detected the detection of defect sample as a result, determining the optimal gray value of picture to be detected according to detection result
Ir；
The grey level compensation processing module is used for the overall intensity mean value using picture to be checked, optimal gray value IrAnd pre-set image
Grey level compensation formula calculates the compensating parameter of picture to be checked, using the compensating parameter of calculating to each pixel of picture to be checked
Gray value compensates, to realize the grey scale of picture to be checked.
7. a kind of AOI system image grayscale standardized system according to claim 6, which is characterized in that the simulation lacks
Sunken processing module includes sequentially connected simulation point defect generation module, image capture module and defective data processing module,
In,
The simulation point defect generation module is used to specify the point defect sample of each grayscale of regional simulation in picture image to be checked,
To generate simulation point defect sample；
Described image acquisition module is used to acquire the simulation point defect sample image under each picture centre gray average；
The defective data processing module is used to detect the simulation point defect sample image under each picture centre gray average, system
Count the defective data of each picture centre gray average Imitating point defect sample image.
8. a kind of AOI system image grayscale standardized system according to claim 6 or 7, which is characterized in that the simulation
Defect processing module extracts the mould under the gray average of different images center using point defect detection method and identical detection parameters
The defect of quasi- point defect sample image, counts the middle detection of the simulation point defect sample image under the gray average of different images center
Defect number, defect contrast and defect area；The optimal gray value I of picture to be checked is determined according to defective datar。
9. a kind of AOI system image grayscale standardized system a method according to any one of claims 6-8, which is characterized in that
The overall intensity mean value computation formula of picture to be checked is
In formula, R is picture area to be checked, and the gray value of pixel (x, y) is f (x, y) in R, and A is the area of picture to be checked.
10. a kind of AOI system image grayscale standardized system according to claim 9, which is characterized in that the gray scale is mended
The compensating parameter that processing module calculates picture to be checked using iterative algorithm is repaid, specifically:
Preset initial gray value IsWith the number of iterations N, initial penalty coefficient γ is calculated0=sqrt (Is/Ic)；
Since n=0, γ is enabledn+1=γn+ 1, by γnSubstitute into pre-set image grey level compensation formula Obtaining picture image coordinate to be checked is the compensated gray value I of (x, y) pixeln(x, y) is further calculated
To the overall intensity mean value of the corresponding image of nth iteration, i.e.,
Stopping criterion for iteration is | In-Ir| it is less than preset threshold, γ when iteration endsnAs required gray value penalty coefficient γ；
Wherein, γnGray value penalty coefficient when for nth iteration, a are the coefficient of pre-set image grey level compensation formula.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111798420A (en) * | 2020-06-28 | 2020-10-20 | 苏州精濑光电有限公司 | Point screen position positioning method, device, equipment and storage medium |
| CN112130355A (en) * | 2020-09-21 | 2020-12-25 | 深圳同兴达科技股份有限公司 | Method for efficiently acquiring defective liquid crystal display module |
| CN114519714A (en) * | 2022-04-20 | 2022-05-20 | 中导光电设备股份有限公司 | Method and system for judging smudgy defect of display screen |
| CN116718353A (en) * | 2023-06-01 | 2023-09-08 | 信利光电股份有限公司 | Automatic optical detection method and device for display module |
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