CN111192253A - Definition checking method and system based on contrast sensitivity and contrast - Google Patents
Definition checking method and system based on contrast sensitivity and contrast Download PDFInfo
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- CN111192253A CN111192253A CN201911402628.3A CN201911402628A CN111192253A CN 111192253 A CN111192253 A CN 111192253A CN 201911402628 A CN201911402628 A CN 201911402628A CN 111192253 A CN111192253 A CN 111192253A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
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Abstract
The invention relates to the technical field of image processing, in particular to a definition checking method and a system based on contrast sensitivity and contrast, which comprises the following steps: step S1, shooting an object to be inspected to obtain an inspection image, and extracting an interested area of the inspection image to obtain an interested area grid image; step S2, extracting pixels from each row of the raster image, and calculating a contrast and a contrast sensitivity of each row of the raster image according to the pixels; step S3, performing coordinated mathematical modeling on the contrast and contrast sensitivity of each row of raster image to obtain the data coordinates of each row of raster image pixels; step S4, calculating a sharpness level of the raster image according to the datamation coordinates of each row of the raster image pixels. The invention can avoid human subjective error when judging the image definition, and can better judge the image definition.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a definition checking method and system based on contrast sensitivity and contrast.
Background
The image quality is an important index for judging the performance of the image acquisition equipment and whether the working state is normal or not, and is also used for comparing the performance of an image processing algorithm and optimizing system parameters. Therefore, it is of great significance to establish an effective image quality evaluation mechanism in the fields of image acquisition, coding compression, network transmission and the like, and the definition is one of indexes for measuring the image quality.
The known resolution sharpness detection is performed by manual visual inspection, and the resolution is judged to be the best visible value by manual visual inspection. This has higher requirements for manual work itself, for example, the manual vision will directly have great influence on the examination result, and it is easy to cause wrong judgment.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a method and a system for sharpness inspection based on contrast sensitivity and contrast.
The technical problem solved by the invention can be realized by adopting the following technical scheme:
a sharpness inspection method based on contrast sensitivity and contrast, comprising:
step S1, shooting an object to be inspected to obtain an inspection image, and extracting an interested area of the inspection image to obtain an interested area grid image;
step S2, extracting pixels from each row of the raster image, and calculating a contrast and a contrast sensitivity of each row of the raster image according to the pixels;
step S3, performing coordinated mathematical modeling on the contrast and contrast sensitivity of each row of raster image to obtain the data coordinates of each row of raster image pixels;
step S4, calculating a sharpness level of the raster image according to the datamation coordinates of each row of the raster image pixels.
Preferably, the contrast ratio is calculated using the following formula:
wherein x is the raster image contrast per line, ImaxFor each row of the raster image corresponding maximum gray value, IminThe minimum gray value corresponding to each row of the raster image.
Preferably, the contrast sensitivity is a number of periods of alternating light and dark in each line of the raster image.
Preferably, the sharpness level is calculated using the formula:
wherein (X, Y) are definition horizontal plane coordinate values, Σ Linex is a sum of contrast ratios of the raster images of each line, Σ Liney is a sum of periods of light-dark alternation of the raster images of each line, and LineCount is a total number of lines of the raster images.
A contrast sensitivity and contrast based sharpness inspection system, comprising:
an image acquisition module for acquiring the inspection image;
the image extraction module is connected with the image acquisition module and is used for extracting the region of interest in the inspection image;
and the data processing module is connected with the image extraction module and used for carrying out data modeling and data processing on the region of interest to obtain definition level data of the region of interest.
Preferably, the image acquisition module is a camera.
Preferably, the image extraction module comprises the steps of extracting the region of interest and extracting the pixel value, the gray value and the alternating light and shade period number of the region of interest.
Preferably, the data modeling includes performing mathematical modeling on the contrast and contrast sensitivity of the region of interest to obtain the datamation coordinates.
Preferably, the data processing includes calculation processing of contrast and contrast sensitivity of the region of interest and calculation processing of the modeled datamation coordinates, so as to obtain definition level data of the region of interest.
The beneficial effects are that:
the invention can avoid human subjective error when judging the image definition, and can better judge the image definition.
Drawings
FIG. 1 is a diagram illustrating a method for examining sharpness based on contrast sensitivity and contrast according to the present invention;
FIG. 2 is a block diagram of a sharpness inspection system based on contrast sensitivity and contrast according to the present invention;
FIG. 3 is a raster image of a region of interest in accordance with an embodiment of the present invention;
fig. 4 is a pixel reference center of a raster image of a region of interest in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
Referring to fig. 1, a step diagram of a sharpness inspection method based on contrast sensitivity and contrast provided by the present invention includes:
step S1, shooting an object to be inspected to obtain an inspection image, and extracting an interested area of the inspection image to obtain an interested area grid image;
step S2, extracting pixels from each row of the raster image, and calculating a contrast and a contrast sensitivity of each row of the raster image according to the pixels;
step S3, performing coordinated mathematical modeling on the contrast and contrast sensitivity of each row of raster image to obtain the data coordinates of each row of raster image pixels;
step S4, calculating a sharpness level of the raster image according to the datamation coordinates of each row of the raster image pixels.
Specifically, an inspection image is acquired by shooting, an area of interest of the acquired inspection image is extracted, rasterization processing is performed, contrast and contrast sensitivity of the obtained rasterized image are calculated, the contrast and contrast sensitivity of each line of the rasterized image are obtained by calculation, mathematical modeling is performed on the contrast and contrast sensitivity of each line of the rasterized image to obtain a datamation coordinate of each line of the rasterized image pixels, and finally an average datamation coordinate of the rasterized image pixels is obtained by calculating the datamation coordinate of each line of the rasterized image pixels, wherein the average datamation coordinate is the definition level of the rasterized image.
Further, the contrast is calculated using the following formula:
where x is the raster image contrast per line, ImaxFor the maximum gray value, I, corresponding to each row of raster imageminThe corresponding minimum gray value for each row of the raster image.
Further, the contrast sensitivity is the number of periods of alternating light and dark of the raster image per line.
Further, the sharpness level is calculated using the following formula:
wherein (X, Y) are definition horizontal plane coordinate values, Σ Linex is the sum of the contrast of the raster image of each line, Σ Liney is the sum of the periods of the raster image of each line alternating in light and dark, Linecount is the total number of lines of the raster image.
Referring to fig. 2, a block diagram of a sharpness inspection system based on contrast sensitivity and contrast provided in the present invention includes:
the image acquisition module is used for acquiring an inspection image;
the image extraction module is connected with the image acquisition module and is used for extracting the region of interest in the inspection image;
and the data processing module is connected with the image extraction module and is used for carrying out data modeling and data processing on the region of interest to obtain definition level data of the region of interest.
Furthermore, the image acquisition module is a camera.
Further, the image extraction module comprises extraction of a region of interest and extraction of a pixel value, a gray value and a period number of alternating bright and dark of the region of interest.
Further, the data modeling comprises the step of carrying out mathematical modeling on the contrast and the contrast sensitivity of the region of interest to obtain the data coordinates.
Further, the data processing comprises calculation processing of the contrast and contrast sensitivity of the region of interest and calculation processing of the datamation coordinates obtained through modeling, and definition level data of the region of interest are obtained.
Specifically, when the image definition is judged, human subjective errors can be avoided, and the image definition can be judged better.
In a preferred embodiment of the present invention, an inspection image is acquired, and then the region-of- interest raster images 01, 02, 03, 04 are acquired in the inspection image as shown in FIG. 3. As shown in FIG. 4, 20 rows of pixel values are taken for the raster image, respectively, on a 500 centered basis, and the contrast is taken for the first row
Comparing the sensitivity y to obtain coordinate data (x, y), wherein y is the number of light and shade alternating periods of the first row of pixel pictures; and then carrying out data coordinate on lines 2 to 40 line by line to obtain coordinate data, and finally carrying out average value calculation on the obtained 40 line coordinate data to obtain the definition level (X, Y) of the region of interest.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (9)
1. A sharpness inspection method based on contrast sensitivity and contrast, comprising:
step S1, shooting an object to be inspected to obtain an inspection image, and extracting an interested area of the inspection image to obtain an interested area grid image;
step S2, extracting pixels from each row of the raster image, and calculating a contrast and a contrast sensitivity of each row of the raster image according to the pixels;
step S3, performing coordinated mathematical modeling on the contrast and contrast sensitivity of each row of raster image to obtain the data coordinates of each row of raster image pixels;
step S4, calculating a sharpness level of the raster image according to the datamation coordinates of each row of the raster image pixels.
2. A method for sharpness inspection based on contrast sensitivity and contrast according to claim 1, wherein the contrast is calculated by the following formula:
wherein x is the raster image contrast per line, ImaxFor each row of the raster image corresponding maximum gray value, IminThe minimum gray value corresponding to each row of the raster image.
3. A method for contrast-based sharpness inspection according to claim 1, wherein the contrast sensitivity is a number of cycles of light and dark alternation of the raster image per line.
4. A method for contrast sensitivity and contrast based sharpness inspection according to claim 1, wherein the sharpness level is calculated using the following formula:
wherein (X, Y) are definition horizontal plane coordinate values, Σ Linex is a sum of contrast ratios of the raster images of each line, Σ Liney is a sum of periods of light-dark alternation of the raster images of each line, and LineCount is a total number of lines of the raster images.
5. A sharpness inspection system based on contrast sensitivity and contrast, which is used in a sharpness inspection method based on contrast sensitivity and contrast according to any one of claims 1 to 4, and which comprises:
an image acquisition module for acquiring the inspection image;
the image extraction module is connected with the image acquisition module and is used for extracting the region of interest in the inspection image;
and the data processing module is connected with the image extraction module and used for carrying out data modeling and data processing on the region of interest to obtain definition level data of the region of interest.
6. The system of claim 5, wherein the image capture module is a camera.
7. The system of claim 5, wherein the image extraction module comprises extracting the region of interest and extracting the number of cycles of pixel values, gray values and light and shade alternation of the region of interest.
8. A contrast sensitivity and contrast based sharpness inspection system according to claim 5, wherein the data modeling includes mathematically modeling the contrast and contrast sensitivity of the region of interest to obtain the datamation coordinates.
9. The system according to claim 5, wherein the data processing comprises calculating contrast and sensitivity of the region of interest and calculating the modeled digitized coordinates to obtain the sharpness level data of the region of interest.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114257695A (en) * | 2021-12-14 | 2022-03-29 | 成都信和创业科技有限责任公司 | Universal image projection equipment imaging definition detection method |
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| US20150294452A1 (en) * | 2014-04-14 | 2015-10-15 | Shenzhen China Star Optoellectronics Technology Co. Lrd | Image processing method, image processing device and automated optical inspection machine |
| CN110324596A (en) * | 2019-07-30 | 2019-10-11 | 歌尔股份有限公司 | Clarity detection method and detection device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20150294452A1 (en) * | 2014-04-14 | 2015-10-15 | Shenzhen China Star Optoellectronics Technology Co. Lrd | Image processing method, image processing device and automated optical inspection machine |
| CN110324596A (en) * | 2019-07-30 | 2019-10-11 | 歌尔股份有限公司 | Clarity detection method and detection device |
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| 朱祖祥, 浙江教育出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114257695A (en) * | 2021-12-14 | 2022-03-29 | 成都信和创业科技有限责任公司 | Universal image projection equipment imaging definition detection method |
| CN114257695B (en) * | 2021-12-14 | 2023-11-07 | 成都信和创业科技有限责任公司 | Universal imaging definition detection method for image projection equipment |
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