CN111369552B - Infrared blind pixel detection method and device and computer readable storage medium - Google Patents
Infrared blind pixel detection method and device and computer readable storage medium Download PDFInfo
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Abstract
The application discloses an infrared blind pixel detection method, an infrared blind pixel detection device and a computer readable storage medium. The method comprises the steps of utilizing a pre-generated blind pixel table to detect infrared blind pixels in the production process of infrared imaging equipment; in the using process of the infrared imaging equipment, sliding window processing is carried out on the infrared image to be output according to a preset neighborhood value, and the maximum value and the minimum value of each window are obtained; if the infrared image to be output meets the preset judging condition, judging random blind pixels and flash blind pixels in the scene based on the relation between the variance of the infrared image to be output and a third preset threshold value. The blind pixel table is a fixed blind pixel which is obtained by combining model blind pixels and/or flash blind pixels and/or response blind pixels and is used for determining the calibration process; the preset determination condition is generated based on a relationship between the maximum value, the minimum value, and a fourth preset threshold value. On the basis of not increasing the process time, the method and the device can rapidly and accurately detect any type of blind pixels and reduce the occurrence of blind pixel misjudgment.
Description
Technical Field
The present disclosure relates to the field of infrared imaging device manufacturing technologies, and in particular, to an infrared blind pixel detection method, an infrared blind pixel detection device, and a computer readable storage medium.
Background
At present, a refrigeration infrared detector works in a refrigeration environment, and the detector often experiences temperature impact between high temperature and low temperature in each power-on and power-off process. Due to limitations of existing manufacturing processes and raw materials, infrared imaging often has dead pixels with a response rate less than 1/2 of the average response rate and overheated pixels with a noise voltage 2 times greater than the average noise voltage described in national army standard GB/T17444-2013. Wherein, the invalid pixels in the national army mark are also called blind pixels, and comprise two types of dead pixels and overheat pixels.
In practical application, all blind pixels cannot be judged by using general judgment conditions in national army standards, and blind pixel missing judgment can occur. The existence of the blind pixels severely restricts the infrared imaging effect, and has serious influence on the application and popularization of the infrared detector. In actual engineering, the blind pixels are represented as isolated or continuous bright spots and dark spots in the infrared image, part of the bright spots and the dark spots do not change along with the scene temperature, and only the blind pixels are represented as spots with larger difference between gray scale and surrounding neighborhood in space, so as to form fixed blind pixels; the partial blind pixels are flash pixels which are suddenly changed along with the change of time to form flash blind pixels; and part of the blind pixels are represented as bright spots or dark spots which randomly appear along with temperature or time change, so that random blind pixels are formed. The blind pixels are accurately detected by a certain method, and then the detected blind pixels are replaced by a proper blind pixel compensation algorithm, so that the quality of infrared imaging can be improved, and the method has important application value for application and popularization of the infrared detector.
The related art generally detects blind pixels by a black body scale-based detection method and a scene-based detection method. The blackbody calibration method is used for judging blind pixels according to indexes such as response rate and noise of pixels in national standards through uniform blackbody images obtained in the calibration process. The method is simple in principle and wide in application, but cannot process random blind pixels. Random blind pixels can be processed based on a scene detection method without depending on a blackbody reference source, but the defects of easy misjudgment, large operand and large influence by image non-uniformity are commonly existed.
In view of this, how to detect any blind pixel with high accuracy on the basis of not increasing the process time length, and reduce the occurrence of the blind pixel misjudgment phenomenon is a technical problem to be solved by the skilled person.
Disclosure of Invention
The application provides an infrared blind pixel detection method, an infrared blind pixel detection device and a computer readable storage medium, which can rapidly and accurately detect any type of blind pixels on the basis of not increasing the process time length and reduce the occurrence of blind pixel misjudgment.
In order to solve the technical problems, the embodiment of the invention provides the following technical scheme:
in one aspect, the embodiment of the invention provides an infrared blind pixel detection method, which includes:
In the production process of the infrared imaging equipment, infrared blind pixel detection is carried out by utilizing a pre-generated blind pixel table; the blind pixel table is a fixed blind pixel which is obtained by combining model blind pixels and/or flash blind pixels and/or response blind pixels and is used for determining the calibration process;
in the process of using the infrared imaging equipment by a user, carrying out sliding window processing on an infrared image to be output according to a preset neighborhood value to obtain a maximum value and a minimum value of each window;
if the infrared image to be output meets a preset judging condition, judging random flash blind pixels in a scene based on the relation between the variance of the infrared image to be output and a third preset threshold value;
the model blind pixels are judged according to the gain value range of the non-uniformity correction model, the flash blind pixels are judged by utilizing the relation between the time domain extremum absolute values of the high-temperature point image and the low-temperature point image after the non-uniformity correction and a first preset threshold value, and the response blind pixels are judged by adopting the relation between the response matrix of the high-temperature point and the low-temperature point after the non-uniformity correction and a second preset threshold value; the preset determination condition is determined based on a relationship among the maximum value, the minimum value and a fourth preset threshold value.
Optionally, the determining the model blind pixel according to the gain value range of the non-uniformity correction model includes:
a blackbody calibration method is adopted to collect a plurality of continuous high-temperature images and a plurality of continuous low-temperature images;
calculating an accumulated probability density distribution function by using gain values obtained by calculating the non-uniformity corrected image to obtain gain values with interval duty ratio of the gain value distribution within a preset value range;
and judging pixels with the current gain value not in the preset value range as the model pixels.
Optionally, the determining that the flash blind pixel is based on the relation between the absolute value of the time domain extremum of the high temperature point image and the low temperature point image after the non-uniformity correction and the first preset threshold value includes:
calculating an average value matrix of corresponding pixel points of the multi-frame high-temperature point map after the non-uniformity correction and a time domain extremum absolute value matrix of the corresponding pixel points to obtain a high Wen Junzhi matrix and a high-temperature time domain extremum absolute value matrix;
traversing each element in the high-temperature time domain extremum absolute value matrix, and classifying a pixel point corresponding to a current element value into a first type time domain blind pixel set if the current element value in the high-temperature time domain extremum absolute value matrix is larger than a first preset threshold value or a multiple of any element value in the mean value matrix;
Calculating an average value matrix of corresponding pixel points of a plurality of frames of low Wen Diantu after the non-uniformity correction and a time domain extremum absolute value matrix of the corresponding pixel points to obtain a low-temperature average value matrix and a low Wen Shiyu extremum absolute value matrix;
traversing each element in the low Wen Shiyu extremum absolute value matrix, and classifying a pixel point corresponding to the current element value into a second type time domain blind pixel set if the current element value in the low Wen Shiyu extremum absolute value matrix is larger than a fifth preset threshold value or a multiple of any element value in the average value matrix;
and combining the first type time domain blind pixel set and the second type time domain blind pixel set to obtain the flash blind pixels used for determining the calibration process.
Optionally, the calculating the mean value matrix of the corresponding pixel points and the time domain extremum absolute value matrix of the corresponding pixel points of the multi-frame high-temperature point image after the non-uniformity correction to obtain the high Wen Junzhi matrix and the high-temperature time domain extremum absolute value matrix includes:
calculating an average value matrix of corresponding pixel points of the multi-frame high-temperature point images after the non-uniformity correction by using an average value matrix relation to obtain a high Wen Junzhi matrix; the mean matrix relation is:
calculating a time domain extremum absolute value matrix of a corresponding pixel point of the multi-frame high-temperature point image after the non-uniformity correction by using a time domain extremum calculation relational expression to obtain a high Wen Shiyu extremum absolute value matrix, wherein the time domain extremum calculation relational expression is as follows:
In the formula, meanFigT1 (i, j) is the high-temperature mean matrix, minuxfigt 1 (i, j) is the high-temperature time domain extremum absolute value matrix, region= {1,2,3, …, num }, num is the total frame number of the high-temperature point image, figT1 frame (i, j) is the value of the ith row and jth column of the high temperature point image corrected by the frame when the temperature is T1.
Optionally, the determining that the response blind pixel is the relation between the response matrix of the high temperature point and the low temperature point after the non-uniformity correction and the second preset threshold value includes:
calculating gray average values of a plurality of frames of high-temperature point images and low-temperature point images after the non-uniformity correction to obtain a high-temperature average value and a low-temperature average value;
subtracting the high-temperature average value from the low-temperature average value to obtain the response matrix;
calculating the average value of all pixels of the whole area array by the response matrix to obtain an area array average value;
and if the difference value between the current element value in the response matrix and the area array mean value is larger than a preset sixth preset threshold value, judging the pixel corresponding to the current element value as the response blind pixel.
Optionally, the calculating the average value of the response matrix for all pixels of the whole area array to obtain an area array average value is as follows:
calculating the area array mean value by using an area array mean value calculation relational expression, wherein the area array mean value calculation relational expression is as follows:
response(i,j)＝meanFigT1(i,j)-meanFigT2(i,j)；
Wherein response (i, j) is the response matrix, meansresponse is the area array mean value, m is the area array length and width, meansFigT 1 (i, j) is the mean matrix of the pixels corresponding to the non-uniformity corrected high temperature point image at the temperature T1, and meansFigT 2 (i, j) is the mean matrix of the pixels corresponding to the non-uniformity corrected high temperature point image at the temperature T2.
Optionally, the preset neighborhood value is m×n, and if the infrared image to be output meets a preset determination condition, determining the random flash blind pixel in the scene based on the relationship between the variance of the infrared image to be output and a third preset threshold value includes:
if the maximum value and the minimum value of m×n of the infrared image outFig to be output with (i, j) as the center are tempMax and the minimum value tempMin respectively, the preset determination condition is:
calculating variance (i, j) of the infrared image to be output after the center point is removed, and judging the random flash blind pixels according to a judgment relation, wherein the judgment relation is as follows:
wherein threshold3 is the third preset threshold, and threshold4 is the fourth preset threshold; if map4 (i, j) is 1, the pixel point corresponding to the abscissa of the infrared image to be output is (i, j) is the random flash blind pixel, and if map4 (i, j) is 0, the pixel point corresponding to (i, j) is not the random flash blind pixel.
Another aspect of the embodiment of the present invention provides an infrared blind pixel detection device, including:
the calibration blind pixel detection module is used for carrying out infrared blind pixel detection by utilizing a pre-generated blind pixel table in the production process of the infrared imaging equipment; the blind pixel table is a fixed blind pixel which is obtained by combining model blind pixels and/or flash blind pixels and/or response blind pixels and is used for determining the calibration process; the model blind pixels are judged according to the gain value range of the non-uniformity correction model, the flash blind pixels are judged by utilizing the relation between the time domain extremum absolute values of the high-temperature point image and the low-temperature point image after the non-uniformity correction and a first preset threshold value, and the response blind pixels are judged by adopting the relation between the response matrix of the high-temperature point and the low-temperature point after the non-uniformity correction and a second preset threshold value;
the sliding window processing module is used for carrying out sliding window processing on the infrared image to be output according to a preset neighborhood value in the process of using the infrared imaging equipment by a user so as to obtain the maximum value and the minimum value of each window;
the random flash blind pixel detection module is used for judging random flash blind pixels in a scene based on the relation between the variance of the infrared image to be output and a third preset threshold value if the infrared image to be output meets a preset judgment condition; the preset determination condition is determined based on a relationship among the maximum value, the minimum value and a fourth preset threshold value.
The embodiment of the invention also provides an infrared blind pixel detection device, which comprises a processor, wherein the processor is used for realizing the steps of the infrared blind pixel detection method according to any one of the previous steps when executing the computer program stored in the memory.
The embodiment of the invention finally provides a computer readable storage medium, wherein the computer readable storage medium stores an infrared blind pixel detection program, and the infrared blind pixel detection program realizes the steps of the infrared blind pixel detection method according to any one of the previous steps when being executed by a processor.
The technical scheme provided by the application has the advantages that the blind pixel table is calibrated in the production process, a computer program relied on in the implementation process is not written into the machine core, the system power consumption is not increased, additional acquisition data is not needed, the calculation speed is high, and the production flow process is not increased. The blind pixels can be accurately positioned and fixed by using the blind pixel table in the production process of the equipment, and the corrected high-temperature target and the corrected low-temperature target are adopted for judgment, so that the influence of non-uniformity correction on the high-temperature target and the low-temperature target is considered, the false judgment of the blind pixels can be effectively avoided, and the blind pixel detection precision is improved; in the actual use process, the dense flash points on the uniform surface can be detected by setting the threshold value of the blind pixel detection based on the scene, and other detail information of the scene is not influenced. The combination of the scene-based blind pixel detection and the calibration-based blind pixel detection enables the output infrared image to be cleaner, and improves the quality of the infrared image.
In addition, the embodiment of the invention also provides a corresponding implementation device and a computer readable storage medium for the infrared blind pixel detection method, so that the method has more practicability, and the device and the computer readable storage medium have corresponding advantages.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the related art, the drawings that are required to be used in the embodiments or the description of the related art will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.
Fig. 1 is a schematic flow chart of an infrared blind pixel detection method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of K cumulative probability density distribution according to an embodiment of the present invention;
FIG. 3 is an enlarged partial schematic view of FIG. 2 provided in an embodiment of the present invention;
FIG. 4 shows signal values of a 200 th row pixel of an exemplary image at 14℃and 4℃respectively at the same ambient temperature and target temperature according to an embodiment of the present invention;
FIG. 5 is a graph showing the difference between signal values of the 200 th row pixel of an exemplary image at 14℃and 4℃at the same ambient temperature and target temperature, respectively, according to an embodiment of the present invention;
FIG. 6 shows signal values of a 200 th row of pixels of an exemplary image after non-uniformity correction at 14℃and 4℃respectively at the same ambient temperature and target temperature in accordance with an embodiment of the present invention;
FIG. 7 is a graph showing the difference between signal values of a 200 th row pixel of an exemplary image after non-uniformity correction at the same ambient temperature and a target temperature of 14 ℃ and 4 ℃ according to an embodiment of the present invention;
fig. 8 is a block diagram of a specific implementation of an infrared blind pixel detection device according to an embodiment of the present invention;
fig. 9 is a block diagram of another embodiment of an infrared blind pixel detection device according to an embodiment of the present invention.
Detailed Description
In order to better understand the aspects of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of this application and in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may include other steps or elements not expressly listed.
Having described the technical solutions of embodiments of the present invention, various non-limiting implementations of the present application are described in detail below.
Referring first to fig. 1, fig. 1 is a schematic flow chart of an infrared blind pixel detection method provided by an embodiment of the present invention, where the embodiment of the present invention may include the following matters:
s101: in the production process of the infrared imaging equipment, the infrared blind pixel detection is carried out by utilizing a pre-generated blind pixel table.
In the embodiment of the invention, the production process of the infrared imaging equipment is the calibration process before the infrared imaging equipment leaves the factory. The blind pixel table is obtained by calculation through an upper computer program before the equipment leaves the factory, and can be a fixed blind pixel used for determining the calibration process by combining model blind pixels and/or flash blind pixels and/or response blind pixels. The blind pixels of the model, the flash blind pixels and the response blind pixels can be combined by the person skilled in the art according to the actual application scene and the special properties of the infrared detector, and the application is not limited in any way.
It will be appreciated that pixel flicker is often caused by unstable detector response rates, and that the same pixel has a large difference in gray scale in successive multi-frame images when facing a uniform surface at a certain temperature. Instability in the response rate of detector pixels can be represented by a time domain variance or a time domain extremum of corresponding pixels of successive multi-frame images at a certain temperature. The time domain variance can detect the flash blind pixels with more intense time variation, the time domain extremum can detect the flash blind pixels with a certain periodic mutation and also can detect the flash blind pixels with more intense time variation. The flash blind pixels at each ring temperature and each target temperature can be obtained through the comparison calculation of the time domain variance or the time domain extremum of the pixels corresponding to the continuous multi-frame images at different ring temperatures and different target temperatures and the set threshold. And if the signal values of adjacent pixel points have larger difference under the same ring temperature and the same target temperature, forming a fixed blind pixel. Fixed blind pixels are often caused by the large difference in the response rates of the detector units. The non-uniformity corrected gain and bias may alleviate the difference in response rate between detector units to some extent. The direct judgment of the difference of the original image or the original image at different target temperatures is easy to cause the misjudgment of blind pixels, and the number of the blind pixels is too large. An excessive number of blind pixels directly leads to degradation of the blind pixel replacement effect. The number of fixed blind pixel judgment can be reduced by judging the fixed blind pixels of the images after two-point correction, multi-point correction or segmentation correction, and the blind pixel misjudgment is reduced to a certain extent. Based on the above, the model blind pixel can be judged according to the gain value range of the non-uniformity correction model, the flash blind pixel can be judged by utilizing the relation between the time domain extremum absolute value of the high-temperature point image and the low-temperature point image after the non-uniformity correction and the first preset threshold value, and the response blind pixel can be judged by adopting the relation between the response matrix of the high-temperature point and the low-temperature point after the non-uniformity correction and the second preset threshold value.
It should be noted that, because the calibration is performed in the production process, the computer program corresponding to the part is not written into the machine core, and the system power consumption is not increased. Because the process is carried out in the conventional calibration process, the calculation is carried out according to the high-temperature picture and the low-temperature picture in the calibration process, the additional acquisition of data is not needed, the calculation speed is high, and the production flow process is not increased.
S102: and in the process of using the infrared imaging equipment by a user, carrying out sliding window processing on the infrared image to be output according to a preset neighborhood value to obtain the maximum value and the minimum value of each window.
It can be understood that the infrared imaging device can ensure the quality of output infrared imaging after infrared blind pixel detection and replacement in the calibration process. However, in the process of using the infrared imaging device by a user, that is, in the actual application process after the infrared imaging device leaves the factory, the infrared detector generates random blind pixels along with time or temperature change, so that in order to cope with the random blind pixels generated along with time or temperature change, the high quality of an output image is improved, the blind pixels based on scenes are necessary to detect, the part of blind pixels change along with environmental change, the flash blind pixels in relatively uniform scenes are detected in real time, and the relatively uniform scenes are cleaner through a blind pixel replacement mode.
In the embodiment of the present invention, the preset neighborhood value may be determined according to an actual scene, for example, 7*7 or 5*5, and a window size calculation method in the related sliding window processing technology may be used to calculate each window value of the present application, and a maximum value and a minimum value are selected from these values.
S103: and if the infrared image to be output meets the preset judging condition, judging random flash blind pixels in the scene based on the relation between the variance of the infrared image to be output and a third preset threshold value.
The preset determination condition may be determined based on a relationship among the maximum value, the minimum value and a fourth preset threshold, and the fourth preset threshold and the third preset threshold may be determined according to actual requirements, which is not limited in any way in the application. The third preset threshold is a threshold for scene-based blind pixel detection set based on the intensity of homogeneous surface flash points in the scene. In the embodiment of the invention, any image variance calculation mode can be adopted to calculate the variance value of the infrared image to be output, and the application is not limited in any way. After detecting the random flash blind pixels in real time in S103, any blind pixel replacement method, for example, a neighborhood mean value replacement method, may be used to replace the random flash blind pixels, so that the denser flash points on the uniform surface in the scene may be compensated, and no influence is caused on other detailed information of the scene.
Finally, it should be further noted that, the computer programs relied on in the implementation process of S102 and S103 of the present application need to be written into the core of the infrared imaging device, and these two steps can be implemented by embedded software.
In the technical scheme provided by the embodiment of the invention, the blind pixel table is calibrated in the production process, so that a computer program relied on in the implementation process is not written into the machine core, the system power consumption is not increased, additional data acquisition is not needed, the calculation speed is high, and the production flow process is not increased. The blind pixels can be accurately positioned and fixed by using the blind pixel table in the production process of the equipment, and the corrected high-temperature target and the corrected low-temperature target are adopted for judgment, so that the influence of non-uniformity correction on the high-temperature target and the low-temperature target is considered, the false judgment of the blind pixels can be effectively avoided, and the blind pixel detection precision is improved; in the actual use process, the dense flash points on the uniform surface can be detected by setting the threshold value of the blind pixel detection based on the scene, and other detail information of the scene is not influenced. The combination of the scene-based blind pixel detection and the calibration-based blind pixel detection can enable the output infrared image to be cleaner, and improve the image quality.
In the above embodiment, how to execute S103 is not limited, and in this embodiment, a method for detecting a random flash blind pixel is provided, and if the preset neighborhood value of S102 is m×n, S103 may include the following steps:
if the maximum value and the minimum value of m×n of the infrared image to be output with (i, j) as the center are tempMax and the minimum value tempMin, respectively, when the maximum value and the minimum value are not equal and the absolute value of the difference between the maximum value and the minimum value is smaller than the fourth meeting and the center point is the maximum value or the minimum value, the subsequent variance calculation step may be performed, that is, the preset determination condition of the present application may be expressed as:
when the above condition is satisfied, in order to improve the blind pixel detection accuracy, before calculating the variance, the center point may be removed first, that is, the variance (i, j) of the infrared image to be output after removing the center point is calculated, and the random flash blind pixel is determined according to a determination relational expression, where the determination relational expression is:
wherein threshold3 is a third preset threshold and threshold4 is a fourth preset threshold; if map4 (i, j) is 1, the pixel point corresponding to the abscissa of the infrared image to be output is the random flash blind pixel, and if map4 (i, j) is 0, the pixel point corresponding to (i, j) is not the random flash blind pixel, namely the texture is rich.
As an optional implementation manner, the application further provides a method for determining model blind pixels, which may include the following contents:
a blackbody calibration method is adopted to collect a plurality of continuous high-temperature images and a plurality of continuous low-temperature images; the number of consecutive high-temperature images and consecutive low-temperature images may be the same or different, and the present application is not limited in any way. Processing the image by using a non-uniformity correction method, such as a two-point correction method or a multi-point correction method, then calculating a gain value obtained after correction, namely a K value, and calculating an accumulated probability density distribution function or probability density function by using the K value, wherein a K accumulated probability density distribution diagram can be shown as a diagram in fig. 2 so as to obtain a gain value with the interval ratio of the gain value distribution in a preset value range; the preset value range can be, for example, 1% to 99.9% of the ratio, and the value can be carried out according to actual requirements, which does not affect the implementation of the application. Because the infrared imaging device is developed based on an FPGA (Field Programmable Gate Array ) platform, integer operation is performed on the FPGA, and in order to improve the K value accuracy, the preset value range can be amplified by adjusting the device on the basis of the preset value range, so as to be used as a final preset value range, for example, the endpoint of the range is multiplied by the same coefficient value. And judging the pixels with the current gain value not in the preset value range as model pixels.
Specifically, the K value calculated after the non-uniformity correction is converted into a 1-dimensional array. Since the distribution function of K is a continuous random variable and K.epsilon. (-. Infinity, ++ infinity), the probability density function of K may be F (K), and the cumulative probability density distribution function may be F (K). When F (K) < F1 or F (K) > F2, the corresponding K value is replaced with the nearest threshold K1 or K2, as shown in the following relation:
thus, a model blind pixel is generated when the gain of the pel meets the following condition, where i, j are the corresponding abscissas of the pel in the image, and the condition can be expressed as:
if f1=0.0021, f2= 0.9991, and k1= 0.8931 and k2=1.22, respectively, as shown in fig. 3, the left and right translation of K1 and K2 can be performed according to the actual situation, and similar effects can be obtained.
In addition, the model blind pixels can also adopt probability density functions or histograms and cumulative distribution histograms to obtain gain values with interval duty ratio of gain value distribution within a preset value range, and threshold judgment is carried out according to K distribution.
In the embodiment of the invention, the two-point calibration model, the multi-point calibration model and the model blind pixels introduced by the limited coefficients of the sectional calibration model based on the black body are fully considered. If the model blind pixels are not considered, the gain calculated by the existing method is directly used, and the range of the gain value is expanded. The accuracy of each gain calculated for the bit width limitation of data storage in the FPGA is greatly compromised, even resulting in non-uniformity correction failure. The model blind pixels are adopted, the distribution range of the gain is determined according to the distribution and the duty ratio of the gain, the gain can be limited in a smaller value range, and the accuracy of obtaining the gain can be ensured to the greatest extent through displacement.
Optionally, the present application further provides a calculation process under an implementation manner of the flash blind pixel, which may include the following contents:
calculating an average value matrix of corresponding pixel points of the multi-frame high-temperature point map after the non-uniformity correction and a time domain extremum absolute value matrix of the corresponding pixel points to obtain a high Wen Junzhi matrix and a high-temperature time domain extremum absolute value matrix. In this step, the mean matrix and the time domain extremum absolute value matrix of each of the corrected high temperature point image and the low temperature point image may be calculated. The total number of the high-temperature point images and the low-temperature point images can be the same or different, and the realization of the application is not affected. The calibration form of N segments K can be adopted, or the calibration form of one segment K can be adopted, and n=1. For a plurality of groups of K, only the blind pixel tables obtained by the images of the target temperature points adopted in the calibration of different groups of K are combined. The average matrix of the pixel points corresponding to the multi-frame high-temperature point images after the non-uniformity correction can be calculated by utilizing an average matrix relation to obtain a high Wen Junzhi matrix; the time domain extremum absolute value matrix of the corresponding pixel points of the multi-frame high-temperature point image after the non-uniformity correction can be calculated by using the time domain extremum calculation relation to obtain a high Wen Shiyu extremum absolute value matrix. The mean matrix relationship can be expressed as:
The time domain extremum calculating relation can be expressed as:
in the formula, meanFigT1 (i, j) is a high Wen Junzhi matrix, minmaxFigT1 (i, j) is a high Wen Shiyu extremum absolute value matrix, region= {1,2,3, …, num }, num is the total frame number of the high temperature point image, figT1 frame (i, j) is the value of the ith row and jth column of the high temperature point image corrected by the frame when the temperature is T1.
Traversing each element in the high-temperature time domain extremum absolute value matrix, and classifying the pixel point corresponding to the current element value into a first type time domain blind pixel set if the current element value in the high-temperature time domain extremum absolute value matrix is larger than a first preset threshold value or a multiple of any element value in the mean value matrix. When the value of a certain point in the time domain extremum absolute value matrix minmaxFigT1 is larger than the first preset threshold A1 or the multiple of any element value in the mean value matrix, the point is a blind pixel, the whole time domain extremum absolute value matrix minmaxFigT1 is traversed to obtain all blind pixels as time domain blind pixels 1, the blind pixels can be marked as map21, and map21 can be expressed as:
And calculating an average value matrix of corresponding pixel points of the multi-frame low Wen Diantu after the non-uniformity correction and a time domain extremum absolute value matrix of the corresponding pixel points to obtain a low-temperature average value matrix and a low Wen Shiyu extremum absolute value matrix. The method comprises the steps that a low-temperature average matrix relational expression can be utilized to calculate an average matrix of corresponding pixel points of a plurality of frames of high-temperature point images after non-uniformity correction, so that a low-temperature average matrix is obtained; and calculating a time domain extremum absolute value matrix of the corresponding pixel points of the multi-frame high-temperature point image after the non-uniformity correction by using a low Wen Shiyu extremum calculation relation to obtain a low Wen Shiyu extremum absolute value matrix. The low temperature mean matrix relationship can be expressed as:
The low Wen Shiyu extremum absolute value matrix can be expressed as:
in figT2 frame (i, j) represents the value of the ith row and jth column of the picture after frame correction at temperature T2, region= {1,2,3, …, num }.
And traversing each element in the low Wen Shiyu extremum absolute value matrix, and classifying the pixel point corresponding to the current element value into a second type time domain blind pixel set if the current element value in the low Wen Shiyu extremum absolute value matrix is larger than a fifth preset threshold value or a multiple of any element value in the average value matrix. When the value of a certain point in the time domain extremum absolute value matrix minmaxFigT2 is larger than the fifth preset threshold A2 or the multiple of any element value in the mean value matrix, the point is a blind pixel, the whole time domain extremum absolute value matrix is traversed to obtain all blind pixels which are time domain blind pixels 2, the blind pixels can be marked as map22, and the map22 can be expressed as:
Combining the first type time domain blind pixel set and the second type time domain blind pixel set to obtain the flash blind pixel used in the determination calibration process.
In addition, the embodiment of the invention can also adopt a time domain to calculate the average value, then obtain similar effects by calculating the maximum value of the absolute value after subtracting the average value from each number, and also can detect the periodic abrupt-change flash blind pixels in the calibration process.
As can be seen from the above, the embodiment of the present invention performs calculation and comparison according to the variance of each point in the time domain for the judgment of the flash blind pixels in the related art, so that the calculated amount is large, and the variance of the blind pixel point with periodic mutation is averaged by a plurality of points to have a smaller numerical value, so that the blind pixels are often omitted. The method for solving the absolute value of the maximum and minimum difference in the time domain is adopted, so that the calculated amount can be reduced, and the periodic abrupt change blind pixels can be effectively detected.
As another alternative embodiment, the present application further provides a calculation method for responding to blind pixels, which may include the following:
the embodiment of the invention can calculate the gray average value of the multi-frame high-temperature point image and the multi-frame low-temperature point image after the non-uniformity correction, thereby obtaining the high-temperature average value and the low-temperature average value. Then, subtracting the high-temperature average value from the low-temperature average value to obtain a response matrix, wherein the response matrix can be obtained by calculating the following relation response (i, j) =meanfigt 1 (i, j) -meanFigT2 (i, j), and calculating the average value of all pixels of the whole area array by using the response matrix to obtain an area array average value; for example, the area array mean may be calculated using an area array mean calculation relationship, which may be expressed as:
In the formula, response (i, j) is a response matrix, meansresponse is an area array mean value, m is the length and width of the area array, meansFigT 1 (i, j) is a mean matrix of pixels corresponding to the non-uniformity corrected high-temperature point image at a temperature of T1, and meansFigT 2 (i, j) is a mean matrix of pixels corresponding to the non-uniformity corrected high-temperature point image at a temperature of T2.
If the difference between the current element value and the area array mean value in the response matrix is greater than a preset sixth preset threshold value threshold6, the pixel corresponding to the current element value is determined to be a response blind pixel, and the determination process of the response blind pixel map3 (i, j) can be expressed as follows:
in addition, the embodiment of the invention can also adopt the method that the blind pixel is judged to be responded when the signal value of a certain pixel of the response matrix is greater than the sixth threshold value.
As can be seen from the above, the embodiment of the present invention determines the gray level difference between the original uncorrected high-temperature target and the low-temperature target according to the determination based on the response blind pixel in the related art, and does not consider the influence of correction on the high-temperature target and the low-temperature target, so that the threshold is set improperly, and the blind pixel erroneous determination is easily caused.
It should be noted that, in the present application, the steps may be executed simultaneously or in a certain preset order as long as the steps conform to the logic order, and fig. 1 is only a schematic manner and does not represent only such an execution order.
The embodiment of the invention also provides a corresponding device for the infrared blind pixel detection method, so that the method has more practicability. Wherein the device may be described separately from the functional module and the hardware. The following describes an infrared blind pixel detection device provided by the embodiment of the present invention, and the infrared blind pixel detection device described below and the infrared blind pixel detection method described above can be referred to correspondingly.
Based on the angle of the functional module, referring to fig. 8, fig. 8 is a structural diagram of an infrared blind pixel detection device provided by an embodiment of the present invention under a specific implementation manner, where the device may include:
the calibration blind pixel detection module 801 is configured to perform infrared blind pixel detection by using a pre-generated blind pixel table in a production process of the infrared imaging device; the blind pixel table is a fixed blind pixel which is obtained by combining model blind pixels and/or flash blind pixels and/or response blind pixels and is used for determining the calibration process; the model blind pixels are judged according to the gain value range of the non-uniformity correction model, the flash blind pixels are judged by utilizing the relation between the time domain extremum absolute values of the high-temperature point image and the low-temperature point image after the non-uniformity correction and the first preset threshold value, and the response blind pixels are judged by adopting the relation between the response matrix of the high-temperature point and the low-temperature point after the non-uniformity correction and the second preset threshold value.
And the sliding window processing module 802 is configured to perform sliding window processing on the infrared image to be output according to a preset neighborhood value during the process of using the infrared imaging device by a user, so as to obtain a maximum value and a minimum value of each window.
The random flash blind pixel detection module 803 is configured to determine a random flash blind pixel in a scene based on a relationship between a variance of the infrared image to be output and a third preset threshold value if the infrared image to be output meets a preset determination condition; the preset determination condition is determined based on a relationship between the maximum value, the minimum value, and a fourth preset threshold value.
Optionally, in some implementations of the present embodiment, the calibration blind pixel detection module 801 includes a blind pixel table building sub-module, where the blind pixel table building sub-module may include a model blind pixel determining unit, where the model blind pixel determining unit is configured to collect multiple frames of continuous high-temperature images and multiple frames of continuous low-temperature images by using a blackbody calibration method; calculating an accumulated probability density distribution function by using the gain value obtained by calculating the non-uniformity corrected image to obtain a gain value with the interval ratio of gain value distribution within a preset value range; and judging the pixels with the current gain value not in the preset value range as model pixels.
In other implementations of this embodiment, the blind pixel table construction submodule may include a flash blind pixel determining unit, where the flash blind pixel determining unit may be configured to calculate a mean matrix of corresponding pixels and a time domain extremum absolute value matrix of corresponding pixels of the multi-frame high-temperature point image after the non-uniformity correction to obtain a high Wen Junzhi matrix and a high-temperature time domain extremum absolute value matrix; traversing each element in the high-temperature time domain extremum absolute value matrix, and classifying the pixel point corresponding to the current element value into a first type time domain blind pixel set if the current element value in the high-temperature time domain extremum absolute value matrix is larger than a first preset threshold value or a multiple of any element value in the average value matrix; calculating an average value matrix of corresponding pixel points of a plurality of frames of low Wen Diantu after the non-uniformity correction and a time domain extremum absolute value matrix of the corresponding pixel points to obtain a low-temperature average value matrix and a low Wen Shiyu extremum absolute value matrix; traversing each element in the low Wen Shiyu extremum absolute value matrix, and classifying the pixel point corresponding to the current element value into a second type time domain blind pixel set if the current element value in the low Wen Shiyu extremum absolute value matrix is larger than a fifth preset threshold value or a multiple of any element value in the average value matrix; combining the first type time domain blind pixel set and the second type time domain blind pixel set to obtain the flash blind pixel used in the determination calibration process.
Optionally, in still other implementations of the embodiments of the present invention, the blind pixel table building sub-module may include a response blind pixel determining unit, where the response blind pixel determining unit may be configured to calculate a gray average value of a plurality of frames of high-temperature point images and low-temperature point images after the non-uniformity correction, to obtain a high-temperature average value and a low-temperature average value; subtracting the high-temperature average value from the low-temperature average value to obtain a response matrix; calculating the average value of all pixels of the whole area array by using the response matrix to obtain an area array average value; if the difference value between the current element value in the response matrix and the area array mean value is larger than a preset sixth preset threshold value, the pixel corresponding to the current element value is judged to be the response blind pixel.
As another alternative implementation manner, the random flash blind pixel detection module 803 may be further configured to, if the maximum value and the minimum value of m×n centered on (i, j) of the infrared image to be output are respectively tempMax and minimum value tempMin, preset determination conditions are:
calculating variance (i, j) of the infrared image to be output after the central point is removed, and judging the random flash blind pixels according to a judging relational expression, wherein the judging relational expression is as follows:
wherein threshold3 is a third preset threshold and threshold4 is a fourth preset threshold; if map4 (i, j) is 1, the pixel point corresponding to the abscissa of the infrared image to be output is the random flash blind pixel, and if map4 (i, j) is 0, the pixel point corresponding to (i, j) is not the random flash blind pixel.
The functions of each functional module of the infrared blind pixel detection device according to the embodiment of the present invention may be specifically implemented according to the method in the embodiment of the method, and the specific implementation process may refer to the related description of the embodiment of the method, which is not repeated herein.
Therefore, the embodiment of the invention can rapidly and accurately detect any type of blind pixels on the basis of not increasing the process time, and reduces the occurrence of blind pixel misjudgment.
The above-mentioned infrared blind pixel detection device is described from the angle of the functional module, and further, the application also provides an infrared blind pixel detection device, which is described from the angle of hardware. Fig. 9 is a block diagram of another infrared blind pixel detection device according to an embodiment of the present application. As shown in fig. 9, the apparatus includes a memory 90 for storing a computer program;
a processor 91 for implementing the steps of the infrared blind pixel detection method as mentioned in the above embodiments when executing a computer program.
Processor 91 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 91 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 91 may also include a main processor, which is a processor for processing data in an awake state, also called CPU (Central Processing Unit ); a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 91 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 91 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
Memory 90 may include one or more computer-readable storage media, which may be non-transitory. Memory 90 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 90 is at least used for storing a computer program 901, where the computer program can implement the relevant steps of the infrared blind pixel detection method disclosed in any of the foregoing embodiments after being loaded and executed by the processor 91. In addition, the resources stored in the memory 90 may further include an operating system 902, data 903, and the like, where the storage mode may be transient storage or permanent storage. The operating system 902 may include Windows, unix, linux, among others. The data 903 may include, but is not limited to, data corresponding to the infrared blind pixel detection result, and the like.
In some embodiments, the infrared blind pixel detection device may further include a display 92, an input/output interface 93, a communication interface 94, a power supply 95, and a communication bus 96.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is not limiting of an infrared blind pixel detection device and may include more or fewer components than illustrated, such as sensor 97.
The functions of each functional module of the infrared blind pixel detection device according to the embodiment of the present invention may be specifically implemented according to the method in the embodiment of the method, and the specific implementation process may refer to the related description of the embodiment of the method, which is not repeated herein.
Therefore, the embodiment of the invention can rapidly and accurately detect any type of blind pixels on the basis of not increasing the process time, and reduces the occurrence of blind pixel misjudgment.
It will be appreciated that if the infrared blind pixel detection method in the above embodiment is implemented in the form of a software functional unit and sold or used as a separate product, it may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution contributing to the prior art, or in a software product stored in a storage medium, performing all or part of the steps of the methods of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random-access Memory (Random Access Memory, RAM), an electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, a magnetic disk, or an optical disk, etc. various media capable of storing program codes.
Based on this, the embodiment of the invention also provides a computer readable storage medium storing an infrared blind pixel detection program, where the infrared blind pixel detection program is executed by a processor, and the steps of the infrared blind pixel detection method according to any one of the embodiments are described above.
The functions of each functional module of the computer readable storage medium according to the embodiments of the present invention may be specifically implemented according to the method in the embodiments of the method, and the specific implementation process may refer to the relevant description of the embodiments of the method, which is not repeated herein.
Therefore, the embodiment of the invention can rapidly and accurately detect any type of blind pixels on the basis of not increasing the process time, and reduces the occurrence of blind pixel misjudgment.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above describes in detail an infrared blind pixel detection method, an infrared blind pixel detection device and a computer readable storage medium. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present invention and its core ideas. It should be noted that it would be obvious to those skilled in the art that various improvements and modifications can be made to the present application without departing from the principles of the present invention, and such improvements and modifications fall within the scope of the claims of the present application.
Claims (9)
1. The infrared blind pixel detection method is characterized by comprising the following steps of:
in the production process of the infrared imaging equipment, infrared blind pixel detection is carried out by utilizing a pre-generated blind pixel table; the blind pixel table is a fixed blind pixel which is obtained by combining model blind pixels and/or flash blind pixels and/or response blind pixels and is used for determining the calibration process;
in the process of using the infrared imaging equipment by a user, carrying out sliding window processing on an infrared image to be output according to a preset neighborhood value to obtain a maximum value and a minimum value of each window;
if the infrared image to be output meets a preset judging condition, judging random flash blind pixels in a scene based on the relation between the variance of the infrared image to be output and a third preset threshold value;
The model blind pixels are judged according to the gain value range of the non-uniformity correction model, the flash blind pixels are judged by utilizing the relation between the time domain extremum absolute values of the high-temperature point image and the low-temperature point image after the non-uniformity correction and the first preset threshold value, and the response blind pixels are judged by adopting the relation between the response matrix of the high-temperature point and the low-temperature point after the non-uniformity correction and the second preset threshold value; the preset judging condition is determined based on the relation among the maximum value, the minimum value and a fourth preset threshold value;
the determining of the response blind pixel for the relation between the response matrix of the high temperature point and the low temperature point after the non-uniformity correction and the second preset threshold value comprises the following steps:
calculating gray average values of a plurality of frames of high-temperature point images and low-temperature point images after the non-uniformity correction to obtain a high-temperature average value and a low-temperature average value;
subtracting the high-temperature average value from the low-temperature average value to obtain the response matrix;
calculating the average value of all pixels of the whole area array by the response matrix to obtain an area array average value;
and if the difference value between the current element value in the response matrix and the area array mean value is larger than a preset sixth preset threshold value, judging the pixel corresponding to the current element value as the response blind pixel.
2. The method for detecting infrared blind pixels according to claim 1, wherein the determining of the model blind pixels for the gain range according to the non-uniformity correction model includes:
a blackbody calibration method is adopted to collect a plurality of continuous high-temperature images and a plurality of continuous low-temperature images;
calculating an accumulated probability density distribution function by using gain values obtained by calculating the non-uniformity corrected image to obtain gain values with interval duty ratio of the gain value distribution within a preset value range;
and judging pixels with the current gain value not in the preset value range as the model blind pixels.
3. The method according to claim 1, wherein the determining of the flash blind pixel from the relation between the absolute value of the temporal extremum of the high temperature point image and the low temperature point image corrected by using the non-uniformity and the first preset threshold value comprises:
calculating an average value matrix of corresponding pixel points of the multi-frame high-temperature point map after the non-uniformity correction and a time domain extremum absolute value matrix of the corresponding pixel points to obtain a high Wen Junzhi matrix and a high-temperature time domain extremum absolute value matrix;
traversing each element in the high-temperature time domain extremum absolute value matrix, and classifying a pixel point corresponding to a current element value into a first type time domain blind pixel set if the current element value in the high-temperature time domain extremum absolute value matrix is larger than a first preset threshold value or a multiple of any element value in the mean value matrix;
Calculating an average value matrix of corresponding pixel points of a plurality of frames of low Wen Diantu after the non-uniformity correction and a time domain extremum absolute value matrix of the corresponding pixel points to obtain a low-temperature average value matrix and a low Wen Shiyu extremum absolute value matrix;
traversing each element in the low Wen Shiyu extremum absolute value matrix, and classifying a pixel point corresponding to a current element value into a second type time domain blind pixel set if the current element value in the low Wen Shiyu extremum absolute value matrix is larger than a fifth preset threshold value or a multiple of any element value in the mean value matrix;
and combining the first type time domain blind pixel set and the second type time domain blind pixel set to obtain the flash blind pixels used for determining the calibration process.
4. The method for detecting infrared blind pixels according to claim 3, wherein the calculating the mean value matrix of the corresponding pixel points and the time domain extremum absolute value matrix of the corresponding pixel points of the non-uniformity corrected multi-frame high-temperature point image to obtain the high Wen Junzhi matrix and the high time domain extremum absolute value matrix comprises:
calculating an average value matrix of corresponding pixel points of the multi-frame high-temperature point images after the non-uniformity correction by using an average value matrix relation to obtain a high Wen Junzhi matrix; the mean matrix relation is:
Calculating a time domain extremum absolute value matrix of a corresponding pixel point of the multi-frame high-temperature point image after the non-uniformity correction by using a time domain extremum calculation relational expression to obtain a high Wen Shiyu extremum absolute value matrix, wherein the time domain extremum calculation relational expression is as follows:
in the formula, meanFigT1 (i, j) is the high-temperature mean matrix, minuxfigt 1 (i, j) is the high-temperature time domain extremum absolute value matrix, region= {1,2,3, …, num }, num is the total frame number of the high-temperature point image, figT1 frame (i, j) is the value of the ith row and jth column of the high temperature point image corrected by the frame when the temperature is T1.
5. The method for detecting infrared blind pixels according to claim 1, wherein the calculating the average value of the response matrix for all pixels of the entire area array to obtain an area array average value is:
calculating the area array mean value by using an area array mean value calculation relational expression, wherein the area array mean value calculation relational expression is as follows:
response(i,j)＝meanFigT1(i,j)-meanFigT2(i,j)；
wherein response (i, j) is the response matrix, meansresponse is the area array mean value, m is the area array length and width, meansFigT 1 (i, j) is the mean matrix of the pixels corresponding to the non-uniformity corrected high temperature point image at the temperature T1, and meansFigT 2 (i, j) is the mean matrix of the pixels corresponding to the non-uniformity corrected high temperature point image at the temperature T2.
6. The method for detecting infrared blind pixels according to any one of claims 1 to 5, wherein the preset neighborhood value is m×n, and the determining random flash blind pixels in a scene based on a relationship between the variance of the infrared image to be output and a third preset threshold value if the infrared image to be output meets a preset determination condition comprises:
if the maximum value and the minimum value of m×n of the infrared image outFig to be output with (i, j) as the center are tempMax and the minimum value tempMin respectively, the preset determination condition is:
calculating variance (i, j) of the infrared image to be output after the center point is removed, and judging the random flash blind pixels according to a judgment relation, wherein the judgment relation is as follows:
wherein threshold3 is the third preset threshold, and threshold4 is the fourth preset threshold; if map4 (i, j) is 1, the pixel point corresponding to the abscissa of the infrared image to be output is (i, j) is the random flash blind pixel, and if map4 (i, j) is 0, the pixel point corresponding to (i, j) is not the random flash blind pixel.
7. An infrared blind pixel detection device, comprising:
the calibration blind pixel detection module is used for carrying out infrared blind pixel detection by utilizing a pre-generated blind pixel table in the production process of the infrared imaging equipment; the blind pixel table is a fixed blind pixel which is obtained by combining model blind pixels and/or flash blind pixels and/or response blind pixels and is used for determining the calibration process; the model blind pixels are judged according to the gain value range of the non-uniformity correction model, the flash blind pixels are judged by utilizing the relation between the time domain extremum absolute value of the high-temperature point image and the low-temperature point image after the non-uniformity correction and the first preset threshold value, and the response blind pixels are judged by adopting the relation between the response matrix of the high-temperature point and the low-temperature point after the non-uniformity correction and the second preset threshold value;
The sliding window processing module is used for carrying out sliding window processing on the infrared image to be output according to a preset neighborhood value in the process of using the infrared imaging equipment by a user so as to obtain the maximum value and the minimum value of each window;
the random flash blind pixel detection module is used for judging random flash blind pixels in a scene based on the relation between the variance of the infrared image to be output and a third preset threshold value if the infrared image to be output meets a preset judgment condition; the preset judging condition is determined based on the relation among the maximum value, the minimum value and a fourth preset threshold value;
the calibration blind pixel detection module is further used for:
calculating gray average values of a plurality of frames of high-temperature point images and low-temperature point images after the non-uniformity correction to obtain a high-temperature average value and a low-temperature average value;
subtracting the high-temperature average value from the low-temperature average value to obtain the response matrix;
calculating the average value of all pixels of the whole area array by the response matrix to obtain an area array average value;
and if the difference value between the current element value in the response matrix and the area array mean value is larger than a preset sixth preset threshold value, judging the pixel corresponding to the current element value as the response blind pixel.
8. An infrared blind pixel detection device comprising a processor for implementing the steps of the infrared blind pixel detection method according to any one of claims 1 to 6 when executing a computer program stored in a memory.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon an infrared blind pixel detection program, which when executed by a processor, implements the steps of the infrared blind pixel detection method according to any one of claims 1 to 6.
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Application publication date: 20200703 Assignee: INFIRAY TECHNOLOGIES CO.,LTD. Assignor: Yantai Airui Photo-Electric Technology Co.,Ltd. Contract record no.: X2024980006380 Denomination of invention: Infrared blind element detection method, device, and computer-readable storage medium Granted publication date: 20230714 License type: Common License Record date: 20240530 |