CN113628203B - Image quality detection method and detection system - Google Patents
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- CN113628203B CN113628203B CN202110967697.XA CN202110967697A CN113628203B CN 113628203 B CN113628203 B CN 113628203B CN 202110967697 A CN202110967697 A CN 202110967697A CN 113628203 B CN113628203 B CN 113628203B
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- 238000001514 detection method Methods 0.000 title claims abstract description 23
- 238000001228 spectrum Methods 0.000 claims abstract description 68
- 238000000034 method Methods 0.000 claims description 8
- 238000012790 confirmation Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 5
- 238000007781 pre-processing Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
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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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20132—Image cropping
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
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Abstract
According to the image quality detection method and system provided by the application, the image to be detected is cut into N non-overlapping images, the power spectrum estimated value and the average brightness value of each small image are calculated, the power spectrum estimated value and the average brightness value are compared with the preset threshold value to obtain overexposure and overdarkness areas, and whether the image to be detected has quality problems or not is determined according to the ratio of the number of the overexposure and overdarkness areas to the number of the total areas.
Description
Technical Field
The invention belongs to the technical field of image recognition, and particularly relates to an image quality detection method and an image detection system.
Background
With the continuous development of computer vision technology, the requirements of the related fields on image quality are also higher and higher. Such as the exposure of the relevant practitioner to a large amount of image data per day; for example, people need to judge the image quality before performing related operations such as face recognition, and the like, and the images meeting the standards are subjected to recognition operations and the like. The accuracy and the speed of the existing image quality detection are not enough.
Disclosure of Invention
In view of this, it is necessary to provide an image quality detection method with higher accuracy and higher speed for detecting image quality against the defects existing in the prior art.
In order to solve the problems, the invention adopts the following technical scheme:
the application provides an image quality detection method, which comprises the following steps:
cutting the image to be detected into N non-overlapping images;
calculating a power spectrum estimated value of each small image;
Calculating the average brightness value of each small image;
Comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposure area and an overexposure area;
and determining whether the image to be detected has quality problems or not according to the ratio of the number of the overexposure and the overdrising areas to the total number of the areas.
In some embodiments, the step of calculating the power spectrum estimation value of each small image specifically includes the following steps:
Performing Fourier transform on the small image to obtain a frequency spectrum;
Calculating a power spectrum according to the frequency spectrum;
centering the power spectrum;
the power spectrum after the centering treatment is unidimensionally obtained into a low-frequency region and a high-frequency region;
And respectively calculating the total value of the low frequency region and the total value of the high frequency region, and taking the ratio of the total value to the total value as a power spectrum estimated value.
In some embodiments, the step of calculating the average brightness value of each small image specifically includes the steps of:
And converting the small image into HSV, and extracting the average value of the V channels as the average brightness value of the image.
In some embodiments, in the step of comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposed area and an overexposed area, the method specifically includes:
The threshold values comprise a power spectrum overexposure threshold value, an average brightness overexposure threshold value and an average brightness overexposure threshold value:
Comparing the power spectrum estimated value with the average brightness value and a preset threshold value, and recognizing the area as an overexposure area when the power spectrum estimated value is smaller than the power spectrum overexposure threshold value and the average brightness value is larger than the average brightness overexposure threshold value;
And when the power spectrum estimated value is larger than the power spectrum over-dark threshold value and the average brightness value is smaller than the average brightness over-dark threshold value, the area is considered to be an over-dark area.
In some embodiments, in the step of determining whether the image to be detected has a quality problem by the ratio of the number of the overexposed areas and the overdosed areas to the total number of the areas, the method specifically includes:
And when the ratio of the number of the overexposure and the overdosed areas to the total area number is larger than the threshold value, the image to be detected has quality problems.
In addition, the application also provides an image quality detection system, which comprises:
The image clipping module is used for clipping the image to be detected into N non-overlapping images;
The first calculation module is used for calculating a power spectrum estimated value of each small image;
The second calculation module is used for calculating the average brightness value of each small image;
The comparison module is used for comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposure area and an overdark area;
and the confirmation module is used for determining whether the image to be detected has quality problems or not according to the ratio of the number of the overexposed areas to the number of the total areas.
The technical scheme adopted by the application has the following effects:
According to the image quality detection method and system provided by the application, the image to be detected is cut into N non-overlapping images, the power spectrum estimated value and the average brightness value of each small image are calculated, the power spectrum estimated value and the average brightness value are compared with the preset threshold value to obtain overexposure and overdarkness areas, and whether the image to be detected has quality problems or not is determined according to the ratio of the number of the overexposure and overdarkness areas to the number of the total areas.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the embodiments of the present invention or the drawings used in the description of the prior art, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart illustrating steps of an image quality detection method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a step of calculating a power spectrum estimation value of each small image according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a low-frequency region and a high-frequency region obtained by unidimensionally processing a power spectrum according to the embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an image quality detection system according to an embodiment of the present application.
Fig. 5 is a schematic diagram of overexposure and overdrising detection in the image quality detection method according to embodiment 1 of the present application.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
In the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", "horizontal", "inner", "outer", etc., are based on the directions or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
Referring to fig. 1, a flowchart of steps of an image quality detection method provided by the present application includes the following steps:
Step S110: the image to be detected is cut into non-overlapping N images.
Step S120: a power spectrum estimate is calculated for each patch image.
Specifically, referring to fig. 2, in the step of calculating the power spectrum estimated value of each small image provided in the embodiment of the present application, the method specifically includes the following steps:
Step S121: and carrying out Fourier transform on the small-block image to obtain a frequency spectrum.
Specifically, the fourier transform formula is:
M, N represents the length and width of the image; f (x, y) is the pixel value of a specific position of the image; u, v are the frequency domain components corresponding to x and y.
Represented as real and imaginary parts.
Step S122: and calculating a power spectrum according to the frequency spectrum.
Specifically, the calculation formula of the power spectrum is:
P(u,v)＝|F(u,v)|2。
Step S123: and (5) centering the power spectrum.
Step S124: and the power spectrum after the centering treatment is unidimensionally obtained into a low-frequency region and a high-frequency region.
Referring to fig. 3, a schematic diagram of a low-frequency region and a high-frequency region obtained by unidimensionally processing a power spectrum according to the present embodiment is provided, wherein an X-axis represents frequency, a Y-axis represents energy, a is a high-frequency region, b is a low-frequency region, and c is a noise region.
Step S125: and respectively calculating the total value of the low frequency region and the total value of the high frequency region, and taking the ratio of the total value to the total value as a power spectrum estimated value.
The power spectrum estimation value for each small image can be completed by the steps S121 to S125 described above.
Step S130: an average luminance value of each small image is calculated.
Specifically, the small image is converted into HSV, and the average value of the V channel is extracted as the average brightness value of the image.
It will be appreciated that the tile image may also be converted into LAB or YUV space, and the average value of the L-channel or Y-channel extracted as the average luminance value of the image, respectively.
Step S140: and comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposure area and an overexposure area.
Specifically, in the step of comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposed and an excessively darkened region, the method specifically includes:
The threshold values comprise a power spectrum overexposure threshold value, an average brightness overexposure threshold value and an average brightness overexposure threshold value:
Comparing the power spectrum estimated value with the average brightness value and a preset threshold value, and recognizing the area as an overexposure area when the power spectrum estimated value is smaller than the power spectrum overexposure threshold value and the average brightness value is larger than the average brightness overexposure threshold value;
And when the power spectrum estimated value is larger than the power spectrum over-dark threshold value and the average brightness value is smaller than the average brightness over-dark threshold value, the area is considered to be an over-dark area.
Step S150: and determining whether the image to be detected has quality problems or not according to the ratio of the number of the overexposure and the overdrising areas to the total number of the areas.
Specifically, in the step of determining whether the image to be detected has quality problems by the overexposure and the ratio of the number of the excessively dark areas and the total number of the areas, the method specifically includes:
And when the ratio of the number of the overexposure and the overdosed areas to the total area number is larger than the threshold value, the image to be detected has quality problems.
The image quality detection method provided by the application can detect the quality problems of exposure, darkness and the like in the image, filter the problems, improve the overall quality of the collected image, and can be applied to preprocessing in certain recognition fields so as to improve the recognition precision.
Referring to fig. 4, the present application further provides an image quality detection system, which includes: the image clipping module 110 is configured to clip an image to be detected into N non-overlapping images; the first calculation module 120 is configured to calculate a power spectrum estimation value of each small image; the second calculating module 130 is configured to calculate an average brightness value of each small image; the comparison module 140 is configured to compare the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposed area and an excessively darkened area; the confirmation module 150 is configured to determine whether the image to be detected has a quality problem by a ratio of the number of the overexposed and the overdosed regions to the total number of the regions.
The detailed operation manner of each module of the image quality detection system provided in this embodiment is described in detail in embodiment 1, and will not be described here again.
The image quality detection system provided by the application can detect the quality problems of exposure, darkness and the like in the image, filter the problems, improve the overall quality of the collected image, and can be applied to preprocessing in certain recognition fields so as to improve the recognition precision.
Example 1
First, we cut the image to be detected into 15 x 15 non-overlapping patches.
Then we calculate the power spectrum estimate and the average luminance value of the patch image, respectively.
Next, we compare the power spectrum estimate and the average luminance value to a preset threshold (including a power spectrum overexposure threshold, an average luminance overexposure threshold, and an average luminance overexposure threshold). Meanwhile, the power spectrum overexposure threshold (smaller than) and the average brightness overexposure threshold (larger than) are met, and the area is considered to be an overexposure area; while satisfying the power spectrum over-darkness threshold (greater than) and the average brightness over-darkness threshold (less than) to identify the region as an over-darkness region.
Referring to fig. 5, an overexposure and darkness diagram is provided in embodiment 1 of the present application, wherein a frame a represents overexposure and a frame B represents darkness.
And finally, determining whether the image to be detected has quality problems or not according to the ratio of the number of the overexposed dark areas to the total number of the areas.
The image quality detection method and the system can detect the quality problems of exposure, darkness and the like in the image, filter the problems, improve the overall quality of the collected image, and can be applied to preprocessing in certain recognition fields so as to improve the recognition precision.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (4)
1. An image quality detection method, characterized by comprising the steps of:
cutting the image to be detected into N non-overlapping images;
calculating a power spectrum estimated value of each small image;
Calculating the average brightness value of each small image;
Comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposure area and an overexposure area;
determining whether the image to be detected has quality problems or not according to the overexposure and the ratio of the number of the overexposed areas to the total number of the areas;
The step of calculating the power spectrum estimated value of each small image specifically comprises the following steps:
Performing Fourier transform on the small image to obtain a frequency spectrum;
Calculating a power spectrum according to the frequency spectrum;
centering the power spectrum;
the power spectrum after the centering treatment is unidimensionally obtained into a low-frequency region and a high-frequency region;
Respectively calculating the total value of the low frequency region and the total value of the high frequency region, and taking the ratio of the total value to the total value as a power spectrum estimated value;
the step of calculating the average brightness value of each small image specifically comprises the following steps:
And converting the small image into HSV, and extracting the average value of the V channels as the average brightness value of the image.
2. The method according to claim 1, wherein the step of comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain the overexposed and the excessively darkened region specifically comprises:
The threshold values comprise a power spectrum overexposure threshold value, an average brightness overexposure threshold value and an average brightness overexposure threshold value:
Comparing the power spectrum estimated value with the average brightness value and a preset threshold value, and recognizing the area as an overexposure area when the power spectrum estimated value is smaller than the power spectrum overexposure threshold value and the average brightness value is larger than the average brightness overexposure threshold value;
And when the power spectrum estimated value is larger than the power spectrum over-dark threshold value and the average brightness value is smaller than the average brightness over-dark threshold value, the area is considered to be an over-dark area.
3. The image quality detecting method according to claim 1, wherein in the step of determining whether or not there is a quality problem in the image to be detected by the ratio of the number of the overexposed and the excessively darkened areas and the total area number, specifically comprising:
And when the ratio of the number of the overexposure and the overdosed areas to the total area number is larger than the threshold value, the image to be detected has quality problems.
4. An image quality detecting system, characterized in that the image quality detecting method according to any one of claims 1 to 3 is employed; the system comprises:
The image clipping module is used for clipping the image to be detected into N non-overlapping images;
The first calculation module is used for calculating a power spectrum estimated value of each small image;
The second calculation module is used for calculating the average brightness value of each small image;
The comparison module is used for comparing the power spectrum estimated value with the average brightness value and a preset threshold value to obtain an overexposure area and an overdark area;
and the confirmation module is used for determining whether the image to be detected has quality problems or not according to the ratio of the number of the overexposed areas to the number of the total areas.
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