CN102404602A - Camera definition detection method based on definition test card - Google Patents
Camera definition detection method based on definition test card Download PDFInfo
- Publication number
- CN102404602A CN102404602A CN2011102850806A CN201110285080A CN102404602A CN 102404602 A CN102404602 A CN 102404602A CN 2011102850806 A CN2011102850806 A CN 2011102850806A CN 201110285080 A CN201110285080 A CN 201110285080A CN 102404602 A CN102404602 A CN 102404602A
- Authority
- CN
- China
- Prior art keywords
- image
- sharpness
- definition
- gradient
- value
- 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.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 18
- 238000011156 evaluation Methods 0.000 claims abstract description 35
- 238000000034 method Methods 0.000 claims abstract description 12
- 239000004744 fabric Substances 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 4
- 230000007704 transition Effects 0.000 description 5
- 230000035945 sensitivity Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 230000004304 visual acuity Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005755 formation reaction Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Landscapes
- Image Analysis (AREA)
Abstract
一种基于清晰度测试卡的摄像机清晰度检测方法，包括以下步骤：1)选取视频清晰度测试卡，选择不同清晰度等级的摄像机，拍摄清晰度测试卡的图像，测量该清晰度测试卡的清晰度函数值，过程如下：1.1)检测图像水平、垂直和总体梯度；1.2)阈值处理；1.3)计算清晰度评价函数值；1.4)归一化处理，将得到的不同清晰度等级的摄像机拍摄的图像的清晰度函数值连同摄像机的清晰度等级，保存到数据库中；2)用待检测的摄像机拍摄清晰度测试卡的一幅图片，计算得到所述一幅图片的清晰度函数值，将得到的清晰度函数值与数据库中图像的清晰度函数值比较，选择最接近的数值为待检测摄像机的清晰度等级。本发明方便快捷、可靠性良好。A camera sharpness detection method based on a sharpness test card, comprising the following steps: 1) selecting a video sharpness test card, selecting cameras of different sharpness levels, shooting the image of the sharpness test card, and measuring the sharpness of the sharpness test card The sharpness function value, the process is as follows: 1.1) detect the image horizontal, vertical and overall gradient; 1.2) threshold value processing; 1.3) calculate the sharpness evaluation function value; The sharpness function value of the image is saved in the database together with the sharpness level of the camera; 2) take a picture of the sharpness test card with the camera to be detected, calculate the sharpness function value of the picture, and The sharpness function value obtained is compared with the sharpness function value of the image in the database, and the closest value is selected as the sharpness level of the camera to be detected. The invention is convenient and fast, and has good reliability.
Description
Technical field
The present invention relates to a kind of video camera definition detection method.
Background technology
The evaluation method of image definition mainly contains two kinds of subjective assessment detection methods and objective evaluation detection method, and subjective estimate method is exactly the experimenter who organizes a group abundant, through observing the definition of evaluation image.The image that the observer give to estimate provides the certain quality grade, is divided into 5 grades, 6 grades, 7 grades etc. scoring system according to different levels of sharpness, makes even at last all, obtains the levels of sharpness of image.Identification resolution chart in that the definition of China's video camera is taken with video camera is estimated; Identification resolution chart is that guard and alarm system product quality supervision inspection center of the Ministry of Public Security is made; Definition is divided into eight grades; Line number with different representes, the corresponding vertical line seen clearly of high energy is intensive more more for the most intensive line number pairing line numerical value of this line number for seeing clearly on the image of taking at video camera, numerical value.Though the such scoring of subjective evaluation method is wasted time and energy very much, relatively meets reality, at present, international standard all adopts subjective evaluation method.Manpower is saved in the method for objectively evaluating standardization.
Summary of the invention
Existing video camera definition detection method wastes time and energy in order to overcome, the deficiency that reliability is relatively poor, and the present invention provides a kind of convenient and swift, video camera definition detection method based on identification resolution chart that reliability is good.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of video camera definition detection method based on identification resolution chart, said video camera definition detection method may further comprise the steps:
1) video camera of the different levels of sharpness of selection; The image of shooting clear degree test card; Measure the definition functional value of this identification resolution chart, and the corresponding relation of definition functional value and levels of sharpness is saved in the database, definition functional value computational process is following:
1.1) gradient of detected image:
With I
sAmong the representative image I with (x, y) for the center 8 face the territory subgraph, with two width of cloth mask convolution images, obtain the gradient image of image level and vertical direction, then the gradient calculation formula of x direction and y direction is:
In the formula, S
xBe the gradient of the x direction of image, S
yBe the gradient of the y direction of image, T
xBe the x direction template; T
yBe the y direction template;
The gradient image addition of both direction is obtained the total gradient image S of image each point any direction;
S＝S
x+S
y (2)
Wherein, S is total gradient image, S
xBe the gradient image of horizontal direction, i.e. the gradient image of x direction, S
yBe the gradient image of vertical direction, i.e. the gradient image of y direction;
1.2) threshold process is following:
In the formula, (x is gradient image S in that ((x is y) for judging (whether x, gray value y) are the sign functions of effective gradient value to S, and T is a preset threshold for n for x, gray value y) y) to S;
1.3) to calculate the sharpness evaluation function value following:
In the formula, f is a definition values, and (x is that the gradient image of x direction is at (x, gray value y), S y) to S
y(x is that the gradient image of y direction is at (x, gray value y) y);
1.4) normalization handles as follows:
In the formula, F is the definition normalized value, and m is the length of image, and n is the wide of image;
2) with a width of cloth picture of camera shooting clear degree test card to be detected; Calculate the definition functional value of this picture; With the definition functional value of preserving in definition functional value and the database relatively; Obtain immediate numerical value, the levels of sharpness that said immediate numerical value is corresponding is the levels of sharpness of video camera to be detected.
Further, the value of having preserved through video camera sharpness evaluation function value and lane database compares, and uses the levels of sharpness of the levels of sharpness of the nearest value correspondence that finds as video camera to be detected.
Further again, be applied to the sharpness evaluation function that is used for the video camera automatic focusing system definition evaluation of video camera.
Technical conceive of the present invention is: the video definition test card has standardization, normalized characteristics, and the definition that detects video camera with it helps standardization, and handled image has identical textural characteristics, the influence of avoiding change of background to bring.
Detect video camera level and vertical two orthogonal direction gradient values,, obtain the gradient image of image again with the addition of two values; Edge image is carried out thresholding to be handled; Calculate the sharpness evaluation function value again, clearly then the Grad on the edge is just big for image edge clear, and the fuzzy soft edge then Grad on the edge is just little; Sharpness evaluation function based on edge gradient is directly proportional to edge gradient; Under the identical situation of background (in this invention is identification resolution chart), utilize this characteristic of sharpness evaluation function, can distinguish the image of different definition.
At present, people have carried out extensive studies aspect the Image Definition that is used for the image processing method, and desirable sharpness evaluation function should have following character.
1. unbiasedness.During planes, the focusing evaluation function should be obtained extreme value in object plane and focusing, is not taken in to focus on to obtain extreme value when inaccurate.
2. highly sensitive.Be meant the focusing function curve in effective focusing range, particularly near burnt zone, slope ratio is bigger, and slope is sensitiveer more greatly, and the evaluation of definition is just accurate more.
3. the dull scope of curve.The dull scope of curve is meant the range size that is dull downward trend at the peak point place of curve to the extension of a certain side; This index has directly determined promptly to have determined the levels of sharpness that can detect according to the range size of this focusing characteristic curve effective focusing that can realize.
4. unimodality.Evaluation function has and only has an extreme value, and extreme value other local extremums can not occur corresponding to distinct image.Cause focusing function appraisal curve some Interference Peaks to occur in the part,, cause than mistake promptly for the evaluation of definition if Interference Peaks near main peak, will cause than mistake to the focusing result.Therefore, desirable focusing curve should smoother no local extremum.
Propose many Image Definition at present, can be divided into 4 big types basically:
1) statistics function
2) informatics function
3) frequency-domain function
4) based on the function of image border gradient
Wherein based on the sharpness evaluation function of image border gradient, antijamming capability is strong, calculates fast, and effect is better.
Select the video camera of different levels of sharpness, the photo of shooting clear degree test card calculates the sharpness evaluation function value of every photos, and sharpness evaluation function value levels of sharpness of used video camera when taking this image is preserved as a record together.When detecting the definition of other video camera; Width of cloth picture with camera shooting clear degree test card to be detected; Sharpness evaluation function value through captured picture compares with the value of having preserved; Obtain immediate data, use the corresponding levels of sharpness of this numerical value as the foundation of confirming video camera levels of sharpness to be detected.
Beneficial effect of the present invention mainly shows: convenient and swift, reliability is good.
Embodiment
Further describe in the face of the present invention down.
A kind of video camera definition detection method based on the video definition test card, said video camera definition detection method may further comprise the steps:
1) video camera of the different levels of sharpness of selection; The image of shooting clear degree test card; Measure the definition functional value of this resolving power test card graphic, and the corresponding relation of definition functional value and levels of sharpness is saved in the database, definition functional value computational process is following:
1.1) gradient of detected image:
The gradient of detection level and vertical direction: the notion of at first introducing neighborhood; With pixel a (i; J) be the center; The set of 4 formations in upper and lower, left and right of pixel is called the neighbours territory of pixel a, and the neighbours territory point of pixel a adds that the set that four points on the diagonal constitute is called eight neighborhoods, and is as follows:
The template of the gradient of detection level and vertical direction is following; The convolution template that these two templates are the Sobel operator; Parameter in the template is represented the weighted value of respective pixel, the weighted difference of the template representation x direction of x direction, the weighted difference of the template representation y direction of y direction; With the pixel weighted value of the nearest position, neighbours territory of central point be 2; Pixel weighted value on the eight neighborhood diagonal slightly a little further is 1, and the size of parameter has just been represented the size of weights, and parameter distributions and symbol have just been represented the direction of gradient.With template respectively convolved image obtain the gradient image of image x direction, y direction, then the gradient calculation formula of x direction and y direction as shown in the formula:
Compute gradient: the gradient calculation image of the gentle vertical both direction of water is put total gradient at this:
S＝S
x+S
y (2)
In the formula, S is total gradient image, S
xBe the gradient image of the x direction of image, S
yBe the gradient image of the y direction of image, T
xTemplate for the x direction; T
yTemplate for the y direction;
1.2) threshold process
The distinct image edge contour is clear, and the narrower variation of transition band is violent, and then the image border gray scale is on the contrary for blurred picture; Excessively be with broad, grey scale change is slow, makes the pixel count of little Grad increase; The little gradient pixel count of blurred picture is more; This characteristics are made that though the Grad of certain point is less in the transition band zone, transition band inside gradient value with not necessarily little or be more or less the same with comparing than its distinct image, this makes that the sensitivity of sharpness evaluation function is not high; In order to make sharpness evaluation function better sensitivity arranged in the peak value both sides; Must reduce transition band to gradient and influence, therefore need to suppress transition band shared proportion in the intensity level of image border, can adopt the method for gradient image being carried out threshold process; Remove the less edge gradient value of image, the sensitivity of the evaluation function that promotes clearness and accuracy.Another effect that thresholding is handled is a pixel value of eliminating non-edge; 1.1) in the gradient image that detects to have much be not that the pixel at edge also can calculate Grad; But Grad is very little; These little gradient pixels have occupied the significant proportion of gradient sum of all pixels in the image on the edge of, and to reject these be not the influence of the pixel of marginal point through threshold value is set, and improves the sensitivity that detects.Specific practice is, if the gray value of certain pixel, just can think that it has represented edge of image greater than pre-set threshold; If gray value, thinks then that it is not a marginal point, gives up this point less than threshold value; Thereby detect real original image edge gradient, be shown below:
In the formula, S (x y) is the gradient image of image, and T is a preset threshold, n (x, y) for judge S (x, whether gray value y) is the sign function of effective gradient value;
1.3) calculate the value of sharpness evaluation function:
Sharpness evaluation function is defined as:
In the formula, f is a definition values, S
x(x y) is the gradient image S of x direction
x(x, gray value y), S
y(x y) is the gradient image S of y direction
y(x, gray value y).
1.4) normalization handles: with the above-mentioned sharpness evaluation function value that calculates might occur resolution big, faintly the sharpness evaluation function value that goes out of image calculation than resolution little, than the last width of cloth image calculation big situation of functional value of coming out clearly; For the clear degree evaluation of estimate that the yardstick pictures different is calculated clearly can compare; The method that adopts normalization to handle; Specific practice is exactly with the value of the Image Definition whole number of pixels divided by image, is shown below:
In the formula, F is the definition normalized value, and m is the length of image, and n is the wide of image.
2) confirm the levels of sharpness of image:
After obtaining the definition functional value of the resolving power test card graphic that the video camera of different levels of sharpness takes, with these sharpness evaluation function values and the levels of sharpness of taking the used video camera of this image as a recorded and stored in database.
Detect the levels of sharpness of video camera; Width of cloth picture with camera shooting clear degree test card to be detected; According to above-mentioned steps 1.1)～1.4) calculate the sharpness evaluation function value of picture, search database then is with already present definition functional value comparison in this definition function currency and the database; Select immediate numerical value; Select immediate numerical value, be worth the levels of sharpness of corresponding levels of sharpness, think that this levels of sharpness is exactly the levels of sharpness of video camera to be detected as image to be detected with this.
Claims (3)
1. video camera definition detection method based on identification resolution chart, it is characterized in that: said video camera definition detection method may further comprise the steps:
1) video camera of the different levels of sharpness of selection; The image of shooting clear degree test card; Measure the definition functional value of this identification resolution chart, and the corresponding relation of definition functional value and levels of sharpness is saved in the database, definition functional value computational process is following:
1.1) gradient of detected image:
With I
sAmong the representative image I with (x, y) for the center 8 face the territory subgraph, with two width of cloth mask convolution images, obtain the gradient image of image level and vertical direction, then the gradient calculation formula of x direction and y direction is:
Wherein, s
xBe the gradient of the x direction of image, s
yBe the gradient of the y direction of image, T
xTemplate for the x direction; T
yTemplate for the y direction;
The gradient image addition of both direction is obtained total gradient image S of image each point any direction;
S＝S
x+S
y (2)
Wherein, S is total gradient image, S
xBe the gradient image of horizontal direction, i.e. the gradient image of x direction, S
yBe the gradient image of vertical direction, i.e. the gradient image of y direction;
1.2) threshold process is following:
In the formula, S is a gradient image, and (x is y) for judging (whether x, gray value y) are the sign functions of effective gradient to S, and T is a preset threshold for n;
1.3) to calculate the sharpness evaluation function value following:
In the formula, f is a definition values, S
x(x y) is the gradient image S of x direction
xAt (x, gray value y), S
y(x y) is the gradient image S of y direction
yAt (x, gray value y);
1.4) normalization handles as follows:
In the formula, F is the definition normalized value, and m is the length of image, and n is the wide of image;
2) with a width of cloth picture of camera shooting clear degree test card to be detected; Calculate the definition functional value of this picture; With the definition functional value of preserving in definition functional value and the database relatively; Obtain immediate numerical value, the levels of sharpness that said immediate numerical value is corresponding is the levels of sharpness of video camera to be detected.
2. the video camera definition detection method based on the video definition test card as claimed in claim 1; It is characterized in that: the value of having preserved through video camera sharpness evaluation function value and lane database compares, and uses the levels of sharpness of the levels of sharpness of the nearest value correspondence that finds as video camera to be detected.
3. the video camera definition detection method based on identification resolution chart as claimed in claim 1 is characterized in that: be applied to the definition judgment of video camera being applied in sharpness evaluation function in the video camera automatic focusing system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011102850806A CN102404602A (en) | 2011-09-23 | 2011-09-23 | Camera definition detection method based on definition test card |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011102850806A CN102404602A (en) | 2011-09-23 | 2011-09-23 | Camera definition detection method based on definition test card |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102404602A true CN102404602A (en) | 2012-04-04 |
Family
ID=45886309
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011102850806A Pending CN102404602A (en) | 2011-09-23 | 2011-09-23 | Camera definition detection method based on definition test card |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102404602A (en) |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102780847A (en) * | 2012-08-14 | 2012-11-14 | 北京汉邦高科数字技术股份有限公司 | Camera automatic focusing control method focused on moving target |
| CN103596001A (en) * | 2013-11-27 | 2014-02-19 | 天津大学 | Method for objectively evaluating quality of parallel macrophotography of stereo camera |
| CN103686148A (en) * | 2013-12-05 | 2014-03-26 | 北京华戎京盾科技有限公司 | Automatic video image resolution detecting method based on image processing |
| CN103927749A (en) * | 2014-04-14 | 2014-07-16 | 深圳市华星光电技术有限公司 | Image processing method and device and automatic optical detector |
| CN104637064A (en) * | 2015-02-28 | 2015-05-20 | 中国科学院光电技术研究所 | Defocus blurred image definition detection method based on edge intensity weight |
| CN105763871A (en) * | 2014-12-18 | 2016-07-13 | 深圳市同为数码科技股份有限公司 | Real time detection system and detection method for camera definition |
| CN105809686A (en) * | 2016-03-08 | 2016-07-27 | 上海敏达网络科技有限公司 | Method for realizing image definition detection in computer system |
| CN105915896A (en) * | 2016-05-20 | 2016-08-31 | 信利光电股份有限公司 | Wide angle camera module definition testing system and testing method |
| CN106067020A (en) * | 2016-06-02 | 2016-11-02 | 广东工业大学 | The system and method for quick obtaining effective image under real-time scene |
| CN108206944A (en) * | 2016-12-20 | 2018-06-26 | 浙江舜宇智能光学技术有限公司 | Evaluate the method and system of the projection clarity of the diverging light formula speckle projector |
| CN108227232A (en) * | 2016-12-14 | 2018-06-29 | 浙江舜宇智能光学技术有限公司 | The diverging light formula speckle projector and its focus adjustment method and three-dimensional reconstruction system |
| CN109005368A (en) * | 2018-10-15 | 2018-12-14 | Oppo广东移动通信有限公司 | High dynamic range image generation method, mobile terminal and storage medium |
| CN109587477A (en) * | 2018-08-09 | 2019-04-05 | 浙江大华技术股份有限公司 | A kind of image capture device selection method, device, electronic equipment and storage medium |
| CN110049319A (en) * | 2019-05-09 | 2019-07-23 | 王博文 | A kind of camera clarity detection method and clarity detect graph card |
| CN110519588A (en) * | 2019-09-05 | 2019-11-29 | 普联技术有限公司 | For the Approach for detecting image sharpness of focusing, device and photographic device |
| CN110807745A (en) * | 2019-10-25 | 2020-02-18 | 北京小米智能科技有限公司 | Image processing method and device and electronic equipment |
| CN111122126A (en) * | 2019-12-31 | 2020-05-08 | 北京灵犀微光科技有限公司 | Optical system definition testing method and device |
| CN111770278A (en) * | 2020-07-31 | 2020-10-13 | 重庆盛泰光电有限公司 | Camera module automatic focusing system based on turntable |
| CN112284274A (en) * | 2020-10-22 | 2021-01-29 | 西北工业大学 | A method and system for detecting the aperture and socket diameter of mechanical connection holes |
| CN112508887A (en) * | 2020-11-26 | 2021-03-16 | 西安电子科技大学 | Image definition evaluation method, system, storage medium, equipment and application |
| CN112561890A (en) * | 2020-12-18 | 2021-03-26 | 深圳赛安特技术服务有限公司 | Image definition calculation method and device and computer equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1390335A (en) * | 1999-06-04 | 2003-01-08 | 卢克戴纳米克斯公司 | Method and apparatus for searching for and comparing imayes |
| CN1688163A (en) * | 2004-12-24 | 2005-10-26 | 武汉精伦电子股份有限公司 | Image processing method and apparatus based on sensing resolution |
| CN102156129A (en) * | 2009-12-02 | 2011-08-17 | 南京农业大学 | Beef quality intelligent grading system and method based on machine vision |
-
2011
- 2011-09-23 CN CN2011102850806A patent/CN102404602A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1390335A (en) * | 1999-06-04 | 2003-01-08 | 卢克戴纳米克斯公司 | Method and apparatus for searching for and comparing imayes |
| CN1688163A (en) * | 2004-12-24 | 2005-10-26 | 武汉精伦电子股份有限公司 | Image processing method and apparatus based on sensing resolution |
| CN102156129A (en) * | 2009-12-02 | 2011-08-17 | 南京农业大学 | Beef quality intelligent grading system and method based on machine vision |
Non-Patent Citations (2)
| Title |
|---|
| 孙越: "一种改进的图像清晰度评价函数", 《应用科技》 * |
| 陈晓娟: "实时视频图像的清晰度检测算法研究", 《微型机与应用》 * |
Cited By (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102780847A (en) * | 2012-08-14 | 2012-11-14 | 北京汉邦高科数字技术股份有限公司 | Camera automatic focusing control method focused on moving target |
| CN103596001A (en) * | 2013-11-27 | 2014-02-19 | 天津大学 | Method for objectively evaluating quality of parallel macrophotography of stereo camera |
| CN103686148A (en) * | 2013-12-05 | 2014-03-26 | 北京华戎京盾科技有限公司 | Automatic video image resolution detecting method based on image processing |
| CN103686148B (en) * | 2013-12-05 | 2015-09-09 | 北京华戎京盾科技有限公司 | A kind of method of the automatic detection video image clarity based on Digital Image Processing |
| CN103927749A (en) * | 2014-04-14 | 2014-07-16 | 深圳市华星光电技术有限公司 | Image processing method and device and automatic optical detector |
| CN105763871A (en) * | 2014-12-18 | 2016-07-13 | 深圳市同为数码科技股份有限公司 | Real time detection system and detection method for camera definition |
| CN104637064B (en) * | 2015-02-28 | 2017-09-12 | 中国科学院光电技术研究所 | Defocus blurred image definition detection method based on edge intensity weight |
| CN104637064A (en) * | 2015-02-28 | 2015-05-20 | 中国科学院光电技术研究所 | Defocus blurred image definition detection method based on edge intensity weight |
| CN105809686B (en) * | 2016-03-08 | 2019-02-19 | 上海敏达网络科技有限公司 | The method of image definition detection is realized in computer system |
| CN105809686A (en) * | 2016-03-08 | 2016-07-27 | 上海敏达网络科技有限公司 | Method for realizing image definition detection in computer system |
| CN105915896A (en) * | 2016-05-20 | 2016-08-31 | 信利光电股份有限公司 | Wide angle camera module definition testing system and testing method |
| CN106067020A (en) * | 2016-06-02 | 2016-11-02 | 广东工业大学 | The system and method for quick obtaining effective image under real-time scene |
| CN108227232A (en) * | 2016-12-14 | 2018-06-29 | 浙江舜宇智能光学技术有限公司 | The diverging light formula speckle projector and its focus adjustment method and three-dimensional reconstruction system |
| CN108206944A (en) * | 2016-12-20 | 2018-06-26 | 浙江舜宇智能光学技术有限公司 | Evaluate the method and system of the projection clarity of the diverging light formula speckle projector |
| CN109587477B (en) * | 2018-08-09 | 2020-04-03 | 浙江大华技术股份有限公司 | Image acquisition equipment selection method and device, electronic equipment and storage medium |
| US11195263B2 (en) | 2018-08-09 | 2021-12-07 | Zhejiang Dahua Technology Co., Ltd. | Method and system for selecting an image acquisition device |
| CN109587477A (en) * | 2018-08-09 | 2019-04-05 | 浙江大华技术股份有限公司 | A kind of image capture device selection method, device, electronic equipment and storage medium |
| CN109005368A (en) * | 2018-10-15 | 2018-12-14 | Oppo广东移动通信有限公司 | High dynamic range image generation method, mobile terminal and storage medium |
| CN110049319A (en) * | 2019-05-09 | 2019-07-23 | 王博文 | A kind of camera clarity detection method and clarity detect graph card |
| CN110519588A (en) * | 2019-09-05 | 2019-11-29 | 普联技术有限公司 | For the Approach for detecting image sharpness of focusing, device and photographic device |
| CN110807745A (en) * | 2019-10-25 | 2020-02-18 | 北京小米智能科技有限公司 | Image processing method and device and electronic equipment |
| CN111122126A (en) * | 2019-12-31 | 2020-05-08 | 北京灵犀微光科技有限公司 | Optical system definition testing method and device |
| CN111122126B (en) * | 2019-12-31 | 2022-03-22 | 北京灵犀微光科技有限公司 | Optical system definition testing method and device |
| CN111770278A (en) * | 2020-07-31 | 2020-10-13 | 重庆盛泰光电有限公司 | Camera module automatic focusing system based on turntable |
| CN111770278B (en) * | 2020-07-31 | 2022-05-20 | 盛泰光电科技股份有限公司 | Camera module automatic focusing system based on turntable |
| CN112284274A (en) * | 2020-10-22 | 2021-01-29 | 西北工业大学 | A method and system for detecting the aperture and socket diameter of mechanical connection holes |
| CN112508887A (en) * | 2020-11-26 | 2021-03-16 | 西安电子科技大学 | Image definition evaluation method, system, storage medium, equipment and application |
| CN112508887B (en) * | 2020-11-26 | 2024-02-02 | 西安电子科技大学 | Image definition evaluation method, system, storage medium, device and application |
| CN112561890A (en) * | 2020-12-18 | 2021-03-26 | 深圳赛安特技术服务有限公司 | Image definition calculation method and device and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102404602A (en) | Camera definition detection method based on definition test card | |
| CN108759973B (en) | Water level measuring method | |
| Kumar et al. | Sharpness estimation for document and scene images | |
| CN109460753B (en) | Method for detecting floating object on water | |
| US8396269B2 (en) | Image quality assessment including comparison of overlapped margins | |
| CN113850749B (en) | Method for training defect detector | |
| CN104363815B (en) | Image processing apparatus and image processing method | |
| CN103164692B (en) | A kind of special vehicle instrument automatic identification system based on computer vision and method | |
| CN110210448B (en) | Intelligent face skin aging degree identification and evaluation method | |
| CN103413288A (en) | LCD general defect detecting method | |
| CN108629775A (en) | A kind of hot high-speed rod surface image processing method | |
| CN109948684A (en) | Quality detecting method, device and its relevant device of point cloud data mark quality | |
| CN103793918A (en) | Image definition detecting method and device | |
| CN109632808A (en) | Seamed edge defect inspection method, device, electronic equipment and storage medium | |
| CN106204524A (en) | A kind of method and device of evaluation image quality | |
| CN104102899B (en) | Retinal vessel recognition methods and device | |
| CN107578021A (en) | Pedestrian detection method, apparatus and system based on deep learning network | |
| CN115862259B (en) | Fire alarm early warning system based on temperature monitoring | |
| CN102819850A (en) | Method for detecting edge of color image on basis of local self-adaption color difference threshold | |
| Chen et al. | Evaluating fabric pilling with light-projected image analysis | |
| CN103632369A (en) | Method for universally detecting quality of non-reference underwater images on basis of combination of block average definition | |
| CN107529963A (en) | Image processing apparatus, image processing method and image processing program | |
| CN116721391A (en) | Method for detecting separation effect of raw oil based on computer vision | |
| CN110766683A (en) | Pearl finish grade detection method and system | |
| CN111445435B (en) | Multi-block wavelet transform-based reference-free image quality evaluation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20120404 |