CN109658372B - Image uniformity evaluation method and device - Google Patents

Image uniformity evaluation method and device Download PDF

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CN109658372B
CN109658372B CN201710933092.2A CN201710933092A CN109658372B CN 109658372 B CN109658372 B CN 109658372B CN 201710933092 A CN201710933092 A CN 201710933092A CN 109658372 B CN109658372 B CN 109658372B
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CN109658372A (en
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郭慧
姚毅
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Luster LightTech Co Ltd
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Abstract

The present application relates to the field of image detection technologies, and in particular, to an image uniformity assessment method and apparatus. The method comprises the following steps: obtaining an ensemble averaged pixel value I of an image0The imaging environment of each pixel point of the image is the same; dividing the image into rectangular areas with equal M multiplied by N areas, wherein M is the number of rectangular areas in each row in the image, and N is the number of rectangular areas in each column in the image; calculating the region average pixel value I of each rectangular regionijSum region pixel value mean square error SijWherein, i is 1, 2.. said., M; j 1, 2.... cndot.n; averaging the pixel values I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0Calculating a uniformity coefficient U of the image; and evaluating the uniformity of the image according to the uniformity coefficient U. The method fully considers the fluctuation condition of the pixel value of each area in the image, and is beneficial to improving the accuracy of image uniformity evaluation.

Description

Image uniformity evaluation method and device
Technical Field
The present application relates to the field of image detection technologies, and in particular, to an image uniformity assessment method and apparatus.
Background
Electronic image in the process of imaging, light emitted by an imaging object is presented on an imaging plane of a camera in the form of a two-dimensional image under the action of an optical element of the camera, and the imaging plane is generally provided with a large number of imaging units which can convert two-dimensional image signals into electric signals, and the electric signals are amplified and displayed to present an electronic image.
In general, when response values of each imaging unit on an imaging plane of a camera to a light signal are consistent, and when the camera shoots an object (such as pure white paper) with the same light signal, pixel values of each pixel point on a shot image are the same, and the image is an image with uniform pixel values, which also indicates that the camera has a better imaging effect. However, in practice, due to manufacturing processes and the like, there is inevitably a certain difference between the imaging units, which results in different response values of the imaging units to light signals, and non-uniformity of pixel values of images obtained by capturing objects having the same light signals. It is conceivable that an image captured by the camera may be distorted or the like. At this time, an image correction algorithm is generally employed to calibrate the optical signal response curve of the imaging unit so that it maintains the same response value for the same optical signal. The effectiveness of the correction algorithm can be reflected by the uniformity of the pixel values of the object images with the same optical signal, so that the response values of the imaging units to the optical signal are calibrated by the correction algorithm, and the influence of hardware facilities on the imaging quality is eliminated.
At present, when uniformity of pixel values of electronic images is evaluated, a mean square error between pixel values of all pixel points in the electronic images and an average pixel value of the whole images is usually used to measure fluctuation conditions of the whole pixel values of the electronic images. However, the breadth of a high-pixel image is too large, the difference of pixel values of different areas in the image is also large, and the uniformity of the image is difficult to accurately measure by adopting the mean square error of the whole image.
Disclosure of Invention
The application provides an image uniformity evaluation method and device, which are used for solving the problem that the image uniformity evaluation in the prior art is inaccurate.
In a first aspect of the present application, there is provided an image uniformity evaluation method, including:
obtaining an ensemble averaged pixel value I of an image0The imaging environment of each pixel point of the image is the same;
dividing the image into rectangular areas with equal M multiplied by N areas, wherein M is the number of rectangular areas in each row in the image, and N is the number of rectangular areas in each column in the image;
calculating the region average pixel value I of each rectangular regionijSum region pixel value mean square error SijWherein, i is 1, 2.. said., M; j 1, 2.... cndot.n;
averaging the pixel values I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0Calculating a uniformity coefficient U of the image;
and evaluating the uniformity of the image according to the uniformity coefficient U.
Optionally, averaging the pixel values I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0And calculating a uniformity coefficient U of the image, including:
averaging the pixel values I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-Sij；
Acquiring a maximum element max and a minimum element min in all the pixel value upper limits and the pixel value lower limits;
according to the uniformity coefficient formula
Figure BDA0001429216600000021
And calculating the uniformity coefficient U.
Optionally, averaging the pixel values I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-SijThe method comprises the following steps:
averaging pixel values I according to areaijSum region pixel value mean square error SijForming a region mean pixel value matrix I and a region mean pixel value variance S for the image, wherein,
Figure BDA0001429216600000022
and calculating a pixel value upper limit matrix C and a pixel value lower limit matrix D according to the average pixel value matrix I and the mean square error matrix S, wherein the pixel value upper limit matrix C is I + S, and the pixel value lower limit matrix D is I-S.
Optionally, the obtaining a maximum value max and a minimum value min of all the upper pixel value limits and the lower pixel value limits includes:
combining the pixel value upper limit matrix C and the pixel value lower limit matrix D into an integrated matrix E, wherein E is [ C, D ];
obtaining a maximum element max (E) in the matrix E, taking max (E) as the maximum element max, obtaining a minimum element min (E) in the matrix E, and taking min (E) as the minimum element min.
Optionally, averaging the pixel values I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-SijThe method also comprises the following steps:
judging the Iij+SijWhether the value of any one element is greater than 2bit-1, if so, with 2bit-1 replacing all of said greater than 2bit-1, where bit is the bit width of the image pixel value;
judging the Iij-SijIf any one element is less than 0, and if so, replacing all the elements less than 0 with 0.
In a second aspect of the embodiments of the present application, there is provided an image uniformity evaluating apparatus, including:
a first calculation unit for obtaining an ensemble average pixel value I of the image0The imaging environment of each pixel point of the image is the same;
the image dividing unit is used for dividing the image into M multiplied by N rectangular areas with equal area, wherein M is the number of rectangular areas in each row in the image, and N is the number of rectangular areas in each column in the image;
a second calculation unit for calculating the region average pixel value I of each rectangular regionijSum region pixel value mean square error SijWherein, i is 1, 2.. said., M; j 1, 2.... cndot.n;
a third calculation unit for averaging the pixel values I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0Calculating a uniformity coefficient U of the image;
and the uniformity evaluation unit is used for evaluating the uniformity of the image according to the uniformity coefficient U.
Optionally, the third computing unit is further configured to:
averaging the pixel values I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-Sij；
Acquiring a maximum element max and a minimum element min in all the pixel value upper limits and the pixel value lower limits;
according to the uniformity coefficient formula
Figure BDA0001429216600000023
And calculating the uniformity coefficient U.
Optionally, the third computing unit is further configured to:
averaging pixel values I according to areaijSum region pixel value mean square error SijForming a region mean pixel value matrix of the image I region mean pixel value variance S, wherein,
Figure BDA0001429216600000031
and calculating a pixel value upper limit matrix C and a pixel value lower limit matrix D according to the average pixel value matrix I and the mean square error matrix S, wherein the pixel value upper limit matrix C is I + S, and the pixel value lower limit matrix D is I-S.
Optionally, the third computing unit is further configured to:
combining the pixel value upper limit matrix C and the pixel value lower limit matrix D into an integrated matrix E, wherein E is [ C, D ];
obtaining a maximum element max (E) in the matrix E, taking max (E) as the maximum element max, obtaining a minimum element min (E) in the matrix E, and taking min (E) as the minimum element min.
Optionally, the third computing unit is further configured to:
judging the Iij+SijWhether the value of any one element is greater than 2bit-1, if so, thenBy 2bit-1 replacing said greater than 2bit-1, where bit is the bit width of the image pixel value;
judging the Iij-SijIf any of the elements is less than 0, and if so, replacing the element less than 0 with 0.
The technical scheme provided by the application comprises the following beneficial technical effects:
the method calculates the integral average pixel value I of the image0Dividing the image into rectangular regions with equal area of M × N, and calculating the average pixel value I of each rectangular regionijSum region pixel value mean square error Sij(ii) a By ensemble averaging pixel values I0Area average pixel value IijSum region pixel value mean square error SijAnd calculating a uniformity coefficient U, and evaluating the uniformity coefficient of the image through the uniformity coefficient U. The method fully considers the fluctuation condition of the pixel value of each area in the image, and is beneficial to improving the accuracy of image uniformity evaluation.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a flowchart of an image uniformity evaluation method according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of an image area division structure according to the present application.
Fig. 3 is a flowchart of another image uniformity evaluation method according to an embodiment of the present disclosure.
Fig. 4 is a flowchart of another image uniformity evaluation method according to an embodiment of the present application.
Fig. 5 is a connection block diagram of an image uniformity evaluation apparatus according to an embodiment of the present application.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Example 1
Referring to fig. 1, an embodiment of the present application provides a flowchart of an image uniformity evaluation method, which is implemented by the following steps 101 to 105.
Step S101, obtaining the integral average pixel value I of the image0And the imaging environment of each pixel point of the image is the same.
The global average pixel value I of the image0And the average value of the pixel values of all the pixel points in the image is obtained. Calculating an ensemble averaged pixel value I of the image according to equation (1)0：
Figure BDA0001429216600000041
Wherein A is the number of pixel points in each row of the image, and B is the number of pixel points in each column of the image; p is the abscissa of each pixel point in the image, and p is 1, 2. q is a vertical coordinate q of each pixel point in the image, which is 1, 2. And I (p, q) is the pixel value of each pixel point of the image.
It should be noted that, in the present application, the imaging environment of each pixel point is the same, which means that each pixel point of the image is formed by collecting the optical signal of the same uniform object (such as pure white paper) by using the imaging element of the same model.
In this embodiment, the pixel value of each dot may be RGB (red green blue), and the RGB color pattern is a color standard in the industry, and various colors are obtained by changing three color channels of red, green and blue and superimposing them on each other. Of course, the pixel value may be other forms of numerical values representing the image pixel, and the application is not limited thereto.
Step S102, dividing the image into rectangular areas with equal M multiplied by N areas, wherein M is the number of the rectangular areas in each row of the image, and N is the number of the rectangular areas in each column of the image.
Referring to fig. 2, an image area dividing structure according to an embodiment of the present disclosure is shown. In the present application, M and N are non-negative integers, and specific numerical values of M and N can be determined according to specific situations. For example, when the number of pixels in the image to be evaluated is large, the values of M and N may be increased appropriately. When the number of the pixel points in the image to be evaluated is less, the values of M and N can be properly reduced. In this embodiment, M is 4 and N is 3.
Step S103, calculating the area average pixel value I of each rectangular areaijSum region pixel value mean square error SijWherein, i is 1, 2.. said., M; j 1, 2.
Area average pixel value I of the imageijAnd averaging the pixel values of all the pixels in each rectangular region. Calculating the region average pixel value I according to formula (2)ij。
Figure BDA0001429216600000042
Wherein a is the number of pixel points in each line of each rectangular region; b is the number of each row of pixel points in each rectangular area; (p, q) is the coordinate value of each pixel point in the image; and I (p, q) is the pixel value of each pixel point in the image.
In this embodiment, for a rectangular area located in the ith row and the jth column, an abscissa p of a pixel point (p, q) in the rectangular area is a (i-1) +1, a (i-1) +2, · as; the ordinate q of the pixel point in the region is b (j-1) +1, b (j-1) + 2.
Illustratively, in the present embodiment, the average pixel value for a rectangular area of i-1 (the first row in the image) and j-2 (the second column in the image)
Figure BDA0001429216600000043
Mean square error S of area pixel value of each rectangular areaijCalculated by equation (3):
Figure BDA0001429216600000044
wherein a is the number of pixel points in each line of each rectangular region; b is the number of each row of pixel points in each rectangular area; (p, q) is the coordinate value of each pixel point in the image; i (p, q) is the pixel value of each pixel point in the image, IijThe pixel value is averaged for the region of the ith row and jth column rectangular region.
For example, the mean square error of the area pixel values for rectangular areas of i-1 (first row in the image) and j-2 (second column in the image)
Figure BDA0001429216600000051
Step S104, averaging the pixel value I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0And calculating the uniformity coefficient U of the image.
In the present embodiment, referring to fig. 3, step S104 is implemented by steps S301 to S303.
Step S301, averaging the pixel value I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-Sij。
Optionally, in this embodiment, step S301 is implemented as follows.
Averaging pixel values I according to areaijSum region pixel value mean square error SijForming the said patternA region average pixel value matrix I and a region mean square error pixel value matrix S of the image.
The average pixel value matrix
Figure BDA0001429216600000052
Wherein, Iij(I1, 2.... M; j 1, 2.... N) the position in the average pixel value matrix I and IijThe positions of the corresponding pixel points in the image are the same.
The mean square error matrix
Figure BDA0001429216600000053
Wherein S isij( i 1, 2..... M; j 1, 2.... N) is the position of the mean square error matrix S and SijThe positions of the corresponding pixel points in the image are the same.
Step S302, obtaining a maximum element max and a minimum element min in all the pixel value upper limits and the pixel value lower limits.
In the present embodiment, referring to fig. 4, step S302 is implemented by steps S401 to S403 as follows.
Step S401, calculating a pixel value upper limit matrix C and a pixel value lower limit matrix D according to the average pixel value matrix I and the mean square error matrix S, where the pixel value upper limit matrix C is I + S and the pixel value lower limit matrix D is I-S.
Figure BDA0001429216600000054
Figure BDA0001429216600000055
In the formula (4), Iij+SijRepresenting the upper limit of pixel values within each rectangular area. In the formula (4), Iij-SijRepresenting the lower limit of pixel values within each rectangular region. That is, the pixel value upper limit matrix C is constituted by the pixel value upper limit of each region. The pixel value lower limit matrix D is composed of the pixel value lower limits of each region.
Optionally, step S401 further includes the following constraint conditions: judging whether any element in the matrix C is larger than 2bit-1, if so, with 2bit-1 replaces all larger than 2 in matrix Cbit-1, wherein bit is the bit width of the image pixel value. For example, when the bit width bit of the pixel value is 8, 2bit-1＝28-1 ═ 255. And judging whether any element in the matrix D is less than 0, and if so, replacing all elements less than 0 in the matrix D with 0. By the above-described constraint conditions, it is possible to prevent the elements in the matrix C and the matrix D from exceeding the pixel value range [0,2 ] of the imagebit-1]And abnormal data is avoided.
In step S402, the pixel value upper limit matrix C and the pixel value lower limit matrix D are combined into an integrated matrix E, where E is [ C, D ].
Figure BDA0001429216600000061
Step S403, obtain the maximum element max (E) in the matrix E, use max (E) as the maximum element max, obtain the minimum element min (E) in the matrix E, and use min (E) as the minimum element min.
Step S303, according to the uniformity coefficient formula
Figure BDA0001429216600000062
And calculating the uniformity coefficient U.
In the present embodiment, it is preferred that,
Figure BDA0001429216600000063
where max (E) is the largest element in matrix E, min (E) is the smallest element in matrix E, I0Is the overall average pixel value of the image. max (e) -min (e) represents the maximum average fluctuation value of pixel values between the respective rectangular regions in the image.
Figure BDA0001429216600000064
Representing a maximum average fluctuation ratio of pixel values between rectangular regions in the imageLike the proportion of the overall average pixel value.
For example, in an ideal state, the pixel values of each pixel of the image are identical, and at this time, the pixel values of the regions of the image do not fluctuate, SijAt this time, max (e) is equal to Iij＝I0，min(E)＝Iij＝I0Then coefficient of uniformity
Figure BDA0001429216600000065
Indicating that the image is completely uniform.
And step S105, evaluating the uniformity of the image according to the uniformity coefficient U.
In the present application, the uniformity coefficient U is in the range of [0,1], and if the uniformity coefficient U approaches a value of 1, it is considered that the uniformity of the image is better. The uniformity of the image is considered to be worse if the uniformity coefficient U approaches a value of 0.
Example 2:
an embodiment of the present application provides an image uniformity evaluation apparatus, please refer to fig. 5, the apparatus includes:
a first calculation unit 1 for obtaining an ensemble average pixel value I of an image0The imaging environment of each pixel point of the image is the same;
the image dividing unit 2 is configured to divide the image into M × N rectangular regions with equal areas, where M is the number of rectangular regions in each row in the image, and N is the number of rectangular regions in each column in the image;
a second calculation unit 3 for calculating an area average pixel value I for each rectangular areaijSum region pixel value mean square error SijWherein, i is 1, 2.. said., M; j 1, 2.... cndot.n;
a third calculation unit 4 for averaging the pixel values I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0Calculating a uniformity coefficient U of the image;
a uniformity evaluation unit 5 for evaluating the uniformity of the image according to the uniformity coefficient U.
Optionally, the third calculating unit 4 is further configured to:
averaging the pixel values I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-Sij；
Acquiring a maximum element max and a minimum element min in all the pixel value upper limits and the pixel value lower limits;
according to the uniformity coefficient formula
Figure BDA0001429216600000072
And calculating the uniformity coefficient U.
Optionally, the third calculating unit 4 is further configured to:
averaging pixel values I according to areaijSum region pixel value mean square error SijForming a region mean pixel value matrix of the image I region mean pixel value variance matrix S, wherein,
Figure BDA0001429216600000071
and calculating a pixel value upper limit matrix C and a pixel value lower limit matrix D according to the average pixel value matrix I and the mean square error matrix S, wherein the pixel value upper limit matrix C is I + S, and the pixel value lower limit matrix D is I-S.
Optionally, the third calculating unit 4 is further configured to:
combining the pixel value upper limit matrix C and the pixel value lower limit matrix D into an integrated matrix E, wherein E is [ C, D ];
obtaining a maximum element max (E) in the matrix E, taking max (E) as the maximum element max, obtaining a minimum element min (E) in the matrix E, and taking min (E) as the minimum element min.
Optionally, the third calculating unit 4 is further configured to:
judging the Iij+SijWhether the value of any one element is greater than 2bit-1, if so, thenBy 2bit-1 replacing said greater than 2bit-1, where bit is the bit width of the image pixel value;
judging the Iij-SijIf any of the elements is less than 0, and if so, replacing the element less than 0 with 0.
In summary, the method and the device provided by the application fully consider the fluctuation situation of the pixel value of each region in the image, and are beneficial to improving the accuracy of image uniformity evaluation.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be understood that the present application is not limited to what has been described above and shown in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (6)

1. An image uniformity evaluation method, characterized in that the method comprises:
obtaining an ensemble averaged pixel value I of an image0The imaging environment of each pixel point of the image is the same;
dividing the image into rectangular areas with equal M multiplied by N areas, wherein M is the number of rectangular areas in each row in the image, and N is the number of rectangular areas in each column in the image;
calculating the region average pixel value I of each rectangular regionijSum region pixel value mean square error SijWherein, i is 1, 2.. said., M; j 1, 2.... cndot.n;
averaging the pixel values I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0Calculating a uniformity coefficient U of the image; averaging the pixel values I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-Sij(ii) a Acquiring a maximum element max and a minimum element min in all the pixel value upper limits and the pixel value lower limits; according to the uniformity coefficient formula
Figure FDA0002703388620000011
Calculating the uniformity coefficient U; wherein the obtaining of the maximum value max and the minimum value min of all the upper pixel value limits and the lower pixel value limits includes: combining the pixel value upper limit matrix C and the pixel value lower limit matrix D into an integrated matrix E, E ═ C, D](ii) a Obtaining a maximum element max (E) in a matrix E, taking max (E) as the maximum element max, obtaining a minimum element min (E) in the matrix E, and taking min (E) as the minimum element min;
and evaluating the uniformity of the image according to the uniformity coefficient U.
2. The image of claim 1 eachMethod for uniformity evaluation, characterized in that said averaging pixel values I according to said regionsijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-SijThe method comprises the following steps:
averaging pixel values I according to areaijSum region pixel value mean square error SijForming a region mean pixel value matrix of the image I region mean pixel value variance S, wherein,
Figure FDA0002703388620000012
and calculating a pixel value upper limit matrix C and a pixel value lower limit matrix D according to the average pixel value matrix I and the mean square error matrix S, wherein the pixel value upper limit matrix C is I + S, and the pixel value lower limit matrix D is I-S.
3. The image uniformity evaluation method according to any one of claims 1-2, wherein the average pixel value I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-SijThe method also comprises the following steps:
judging the Iij+SijWhether the value of any one element is greater than 2bit-1, if so, with 2bit-1 replacing all of said greater than 2bit-1, where bit is the bit width of the image pixel value;
judging the Iij-SijIf any one element is less than 0, and if so, replacing all the elements less than 0 with 0.
4. An image uniformity evaluation apparatus, comprising:
a first calculation unit for obtaining an ensemble average pixel value I of the image0The imaging environment of each pixel point of the image is the same;
the image dividing unit is used for dividing the image into M multiplied by N rectangular areas with equal area, wherein M is the number of rectangular areas in each row in the image, and N is the number of rectangular areas in each column in the image;
a second calculation unit for calculating the region average pixel value I of each rectangular regionijSum region pixel value mean square error SijWherein, i is 1, 2.. said., M; j 1, 2.... cndot.n;
a third calculation unit for averaging the pixel values I according to the regionijMean square error of pixel value S in regionijAnd the ensemble averaged pixel value I0Calculating a uniformity coefficient U of the image; averaging the pixel values I according to the regionijMean square error of pixel value S in regionijCalculating an upper limit I of pixel values for each regionij+SijAnd a lower limit of pixel value I for each regionij-Sij(ii) a Combining the pixel value upper limit matrix C and the pixel value lower limit matrix D into an integrated matrix E, E ═ C, D](ii) a Obtaining a maximum element max (E) in a matrix E, taking max (E) as the maximum element max, obtaining a minimum element min (E) in the matrix E, taking min (E) as the minimum element min, and obtaining the maximum element max and the minimum element min in all the pixel value upper limits and the pixel value lower limits; according to the uniformity coefficient formula
Figure FDA0002703388620000021
Calculating the uniformity coefficient U;
and the uniformity evaluation unit is used for evaluating the uniformity of the image according to the uniformity coefficient U.
5. The image uniformity evaluation device according to claim 4, wherein the third calculation unit is further configured to:
averaging pixel values I according to areaijSum region pixel value mean square error SijForming a region mean pixel value matrix I and a region mean pixel value variance S for the image, wherein,
Figure FDA0002703388620000022
and calculating a pixel value upper limit matrix C and a pixel value lower limit matrix D according to the average pixel value matrix I and the mean square error matrix S, wherein the pixel value upper limit matrix C is I + S, and the pixel value lower limit matrix D is I-S.
6. The image uniformity evaluation device according to any one of claims 4-5, wherein the third calculation unit is further configured to:
judging the Iij+SijWhether the value of any one element is greater than 2bit-1, if so, with 2bit-1 replacing said greater than 2bit-1, where bit is the bit width of the image pixel value;
judging the Iij-SijIf any of the elements is less than 0, and if so, replacing the element less than 0 with 0.
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