A method and system for photon number statistics of a solar-blind ultraviolet imager

Abstract

This application relates to the technical field of ultraviolet imaging, and in particular, to a method and system for photon number statistics of a solar-blind ultraviolet imager, which includes: acquiring an ultraviolet image and performing multi-scale decomposition to separate noise objects and photon objects; filtering and eliminating the noise objects in the ultraviolet image and retaining the photon objects; performing binarization processing on the filtered ultraviolet image to obtain a binary image; analyzing the connectivity relationship between each pixel point and adjacent pixel points in the binary image, and establishing a number of connected regions containing a number of foreground pixels based on the analysis result, where the connectivity relationship includes position connectivity and attribute connectivity; counting the number of foreground pixels in each connected region to calculate the total area of all connected regions, obtaining the pixel area of a single photon object calibrated in advance, and converting the equivalent photon number based on the pixel area and the total area. This application has the effect of reducing the underestimation of photon counting caused by photon overlap.

Description

Technical Field

[0001] The present application relates to the technical field of ultraviolet imaging, and in particular, to a method and system for photon number statistics of a solar-blind ultraviolet imager. Background Art

[0002] After high-voltage equipment in substations and transmission lines has been operating in the atmospheric environment for a long time, some structural defects will occur, or the insulation performance will decline due to the increase in surface contamination and the influence of environmental humidity, resulting in corona or partial discharge phenomena. Corona belongs to high-voltage pulse discharge, which can cause chemical reactions in the air, producing substances such as ozone and nitrogen oxides, leading to a decline in the insulation performance of electrical equipment and further causing equipment accidents. Therefore, it is crucial to detect the corona discharge level in a timely and accurate manner to ensure the safe operation of the power grid.

[0003] During the discharge process, electrons in the air continuously acquire and release energy, and when electrons release energy, ultraviolet rays will be released. Ultraviolet imaging technology utilizes this principle to receive the ultraviolet rays generated during the discharge of high-voltage electrical equipment, so as to determine the position and intensity of the corona, thereby providing a reliable basis for evaluating the operation status of the equipment.

[0004] The number of photons of the ultraviolet imager can indicate the discharge intensity of the corona, which plays an important role in evaluating the severity level of disasters at the discharge position. However, when the current ultraviolet imager performs photon number statistics, it directly counts the number of connected regions in the binary image, but ignores some key issues:

[0005] Photons have a superposition effect, that is, multiple electrons will be superimposed in the same connected region during high-density discharge. The traditional technical method will regard the photons superimposed in the same region as belonging to the same connected region, which will seriously underestimate the actual number of photons. At the same time, the connected regions in the existing technology all adopt a fixed area (such as directly deleting connected regions with a pixel number less than 5), which will mis-filter some weak photon signals. Summary of the Invention

[0006] In order to reduce the underestimation of photon counting caused by photon overlap, the present application provides a method and system for photon number statistics of a solar-blind ultraviolet imager.

[0007] In a first aspect, the present application provides a method for photon number statistics of a solar-blind ultraviolet imager, adopting the following technical solution:

[0008] A method for photon number statistics of a solar-blind ultraviolet imager includes the following steps:

[0009] Obtain an ultraviolet image and perform multi-scale decomposition to separate noise objects and photon objects;

[0010] Filter and eliminate the noise objects in the ultraviolet image and retain the photon objects;

[0011] Perform binarization on the filtered ultraviolet image to obtain a binary image;

[0012] Analyze the connectivity relationship between each pixel point and its adjacent pixel points in the binary image, and establish a number of connected regions containing a number of foreground pixels based on the analysis results. The connectivity relationship includes position connectivity and attribute connectivity;

[0013] Count the number of foreground pixels in each connected region to calculate the total area of all connected regions, obtain the pixel area of a single photon object calibrated in advance, and convert the equivalent number of photons based on the pixel area and the total area.

[0014] In some embodiments, obtaining an ultraviolet image and performing multi-scale decomposition to separate noise objects and photon objects includes the following steps:

[0015] Obtain a scale factor and a translation factor, and perform wavelet transform on the ultraviolet image in combination with the Haar wavelet function, where

[0016] Analyze the low-frequency and high-frequency features in the ultraviolet image by changing the scale factor to obtain a frequency distribution, and control the position of the Haar wavelet function on the time axis by changing the translation factor to obtain a position distribution;

[0017] Generate the high-frequency components of feature points based on the frequency distribution and the position distribution;

[0018] Define the feature points corresponding to the high-frequency components with concentrated position distribution and stable frequency distribution as the photon objects, and define the feature points corresponding to the high-frequency components with discrete position distribution and fluctuating frequency distribution as the noise objects.

[0019] In some embodiments, filtering and eliminating the noise objects and retaining the photon objects includes the following steps:

[0020] After performing the wavelet transform, obtain wavelet coefficients, extract high-frequency subband coefficients from the wavelet coefficients, calculate the median absolute deviation of each high-frequency subband coefficient and calculate the noise standard deviation in combination with a preset distribution constant;

[0021] Calculate the total number of pixels in the ultraviolet image, and calculate a dynamic threshold in combination with the noise standard deviation;

[0022] Compare each wavelet coefficient with the dynamic threshold, and filter all wavelet coefficients smaller than the dynamic threshold to complete the filtering of the ultraviolet image.

[0023] In some of these embodiments, after calculating the dynamic threshold, the following steps are further included:

[0024] Regionally segment the ultraviolet image based on the total number of pixels to obtain a number of sub-images;

[0025] Calculate the sub-wavelet coefficients corresponding to each of the sub-images, and statistically analyze the probability distribution of the sub-wavelet coefficients of each value;

[0026] Calculate the characteristic entropy corresponding to each of the sub-images based on the probability distribution, and calculate the average entropy corresponding to the number of sub-images;

[0027] Establish a linear mapping function based on the characteristic entropy and the average entropy to calculate a threshold adjustment coefficient, and optimize the dynamic threshold based on the threshold adjustment coefficient.

[0028] In some of these embodiments, performing binarization processing on the filtered ultraviolet image to obtain a binarized image includes the following steps:

[0029] Obtain the pixel intensity of each pixel point in the filtered ultraviolet image, and calculate the global mean value of the image as a reference value;

[0030] Assign a value of 1 to the pixel points whose pixel intensity is higher than the reference value to define them as foreground pixels, and assign a value of 0 to the pixel points whose pixel intensity is lower than the reference value to define them as background pixels;

[0031] Eliminate the background pixels through morphological closing operations of dilation first and then erosion to obtain the binarized image.

[0032] In some of these embodiments, analyzing the connectivity relationship between each pixel point and adjacent pixel points in the binarized image, and establishing a number of connected domains containing a number of foreground pixels based on the analysis results includes the following steps:

[0033] Traverse all the pixel points in the binarized image and mark the foreground pixels passed through to add labels;

[0034] When marking, based on the 8-neighborhood algorithm, determine whether there are foreground pixels that are adjacent in position and have labels around. If so, select the label with the smallest value that conforms to the connectivity relationship for marking. If not, recursively generate a new label based on the largest value among the existing labels in the current binarized image;

[0035] A number of foreground pixels with the same label together form a connected domain.

[0036] In some of these embodiments, the number of foreground pixels in each of the connected regions is counted to calculate the total area of all the connected regions, the pixel area of a single photon object pre-calibrated is obtained, and the equivalent number of photons is calculated based on the pixel area and the total area, including the following steps:

[0037] Define the pixel area of the single pre-calibrated photon object as the standard area;

[0038] Calculate the sub-area based on the number of foreground pixels included in each of the connected regions;

[0039] Obtain the maximum aspect ratio of each of the connected regions and generate a first coefficient;

[0040] Obtain the position distribution of the hole pixels in each of the connected regions to generate a second coefficient, where the hole pixels are characterized as foreground pixels that do not all have a connection relationship in the 8-neighborhood;

[0041] Obtain the ratio between the sub-area of each of the connected regions and the overall area of the ultraviolet image to generate a third coefficient;

[0042] Adjust the standard area based on the first coefficient, the second coefficient, and the third coefficient to obtain the final pixel area;

[0043] Divide each of the sub-areas by the pixel area to obtain the number of photons per unit, and add up a number of the numbers of photons per unit to calculate the equivalent number of photons.

[0044] In some of these embodiments, calculating the equivalent number of photons based on the pixel area and the total area further includes the following steps:

[0045] Label the connected regions whose sub-areas are smaller than the pixel area of the photon object as objects to be verified;

[0046] Calculate the minimum pixel distance between the objects to be verified and other connected regions;

[0047] When the minimum pixel distance is less than a preset value, set the number of photons per unit calculated for the object to be verified to 1;

[0048] When the minimum pixel distance is greater than the preset value, the object to be verified does not participate in the calculation of the number of photons per unit.

[0049] In some of these embodiments, it further includes the following steps:

[0050] Obtain the measurement distance of the ultraviolet image, and correct the equivalent number of photons based on an attenuation model. Specifically,

[0051] ,

[0052] Among them, is characterized as the corrected photon number, is characterized as the equivalent photon number, d is characterized as the measurement distance, and D is characterized as the constant parameter of the ultraviolet imager.

[0053] In a second aspect, the present application provides a solar-blind ultraviolet imager photon number statistics system, adopting the following technical solution:

[0054] A solar-blind ultraviolet imager photon number statistics system is used to implement the above method.

[0055] The technical solution provided by the embodiments of the present application has the following technical effects:

[0056] First, a three-level hybrid denoising architecture is adopted to separate noise and photon signals, and filtering and binarization processing are carried out. At the same time, the number of connected domains in the binarized image is counted. No provisions are made for the area of the connected domains. Instead, based on connected domains of different sizes, the overall area is determined, and the equivalent photon number is calculated by dividing the area by the pixel area of a calibrated single photon, ensuring that even if multiple photon signals overlap in the same area, their total contribution will be correctly counted, thus avoiding the problem of underestimating the photon number in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a schematic diagram of the steps of a solar-blind ultraviolet imager photon number statistics method provided in this embodiment. DETAILED DESCRIPTION

[0058] To more clearly understand the purpose, technical solution, and advantages of the present application, the present application will be described and illustrated below in conjunction with the drawings and embodiments. However, those of ordinary skill in the art should understand that the present application can be implemented without these details. In some cases, to avoid unnecessary descriptions from making various aspects of the present application obscure, well-known methods, processes, systems, components, and / or circuits that have been described at a higher level will not be elaborated in detail. For those of ordinary skill in the art, it is obvious that various changes can be made to the disclosed embodiments of the present application, and without departing from the principles and scope of the present application, the general principles defined in the present application can be applied to other embodiments and application scenarios. Therefore, the present application is not limited to the shown embodiments, but conforms to the broadest scope consistent with the scope claimed in the present application.

[0059] It should be noted here that the descriptions of these embodiments are used to help understand the present invention, but do not constitute a limitation to the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0060] In the description of the present application, the meaning of "a number of" is one or more, the meaning of "a plurality of" is more than two, and understandings such as "greater than", "less than", "exceeding", etc. do not include the present number, and understandings such as "above", "below", "within", etc. include the present number. If there is a description of "first", "second", etc., it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0061] In the description of the present application, the descriptions with reference to terms such as "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a combined manner.

[0062] As Figure 1 shown, the embodiments of the present application disclose a method for counting photon numbers of a solar-blind ultraviolet imager, including the following steps:

[0063] S100, acquire an ultraviolet image and perform multi-scale decomposition to separate noise objects and photon objects.

[0064] In the actual use of the ultraviolet imager, it will acquire one or more frames of ultraviolet images at the target position, and the normally acquired ultraviolet images contain photon pulses corresponding to partial discharges or corona, and also include other noises corresponding to the environment or other heat sources.

[0065] Then, in order to accurately retain photon objects when measuring the number of photons subsequently, it is necessary to first preprocess the ultraviolet image to separate noise objects and photon objects, facilitating subsequent processing steps such as filtering and binarization.

[0066] First of all, it is necessary to perform multi-scale decomposition on the ultraviolet image. The function of multi-scale decomposition is applicable to decomposing the information in the image into different frequency components. The high-frequency information obtained after multi-scale decomposition will contain the detailed information of the image. Then, according to the distribution and positional relationship of high-frequency and low-frequency in its frequency components, the noise objects and photon objects in the ultraviolet image can be effectively distinguished.

[0067] S200, filter and eliminate the noise objects in the ultraviolet image and retain the photon objects.

[0068] Secondly, filter the noise objects separated after multi-scale decomposition to effectively remove the noise in the ultraviolet image, while retaining and restoring the photon pulse characteristics.

[0069] By filtering and eliminating the noise, the intensity of the photon signal in the image can be effectively increased, making the subsequent photon statistics more accurate and reducing the possibility of misjudgment and missed judgment.

[0070] S300, perform binarization on the filtered ultraviolet image to obtain a binary image.

[0071] After filtering, perform binarization on the current ultraviolet image. Binarization is to distinguish the photon pixels and noise pixels in the ultraviolet image based on a required threshold, and represent the photon pixels and noise pixels with different binary values and further eliminate the discrete noise points.

[0072] At the same time, the binary image obtained after binarization is also convenient for subsequent selection of connected regions to count the number of photons.

[0073] S400, analyze the connectivity relationship between each pixel point and its adjacent pixel points in the binary image, and establish several connected regions containing several foreground pixels based on the analysis results. The connectivity relationship includes position connectivity and attribute connectivity.

[0074] Perform a comprehensive scan and analysis on the binary image. The role of the analysis is to judge whether each pixel point in the image conforms to the same connectivity relationship with its adjacent pixel points based on the neighborhood algorithm. If it conforms, it means that the pixel point and its adjacent pixel points belong to the same connected region.

[0075] Among them, the connectivity relationship includes position connectivity and attribute connectivity. Position connectivity is characterized by the adjacency between a pixel point and another pixel point, and the adjacency relationship varies according to different neighborhood algorithms. In the 4-neighborhood algorithm, position connectivity is characterized by the presence of pixel points adjacent to the left, right, top, and bottom of a pixel point. In the 8-neighborhood algorithm, the position connectivity relationship is characterized by the presence of pixel points adjacent to the left, right, top, bottom, upper left, lower left, upper right, and lower right of a pixel point. Attribute connectivity means that the pixel points that meet the position connectivity are all characterized as photon pixels. Among them, photon pixels are defined as foreground pixels, while irrelevant noise pixels are characterized as background pixels.

[0076] After traversing all the pixel points, several different connected regions can be obtained. Each connected region is composed of photon pixels characterized as photon objects, and each connected region contains at least one photon pixel.

[0077] S500, count the number of foreground pixels in each connected component to calculate the total area of all connected components, obtain the pixel area of a single photon object pre-calibrated, and convert the equivalent number of photons based on the pixel area and the total area.

[0078] In the existing technical solutions, it is default that each connected component corresponds to a number of photons. However, in the actual environment, due to reasons such as different discharge faults, different acquisition positions, and different acquisition angles, there will be a problem of multiple photons overlapping. Then, in this case, a connected component often contains multiple overlapping photons. Therefore, the existing technical method will seriously underestimate the number of photons when photons overlap, resulting in multiple overlapping pixels being misrecognized as a single photon signal, making the final detection error relatively large.

[0079] In this application, to solve this problem, first, the regional area corresponding to each connected component is calculated according to the number of foreground pixels, and at the same time, the pixel area of a calibrated and dynamic single photon is obtained.

[0080] Dividing the regional area of each connected component by the pixel area of a single photon can calculate the theoretical number of photons in each connected component. This is because when multiple photons overlap, the photon energy increases, which will cause the pixel area corresponding to the photon signal in the image to increase. When multiple photons overlap, some of the energy between the overlapping photons may be superimposed in the binary image. However, compared with the traditional method, this method can effectively make the contribution of overlapping photons be statistically correct relatively, improving the accuracy of statistics.

[0081] Finally, adding up the number of photons calculated for each connected component can obtain the total equivalent number of photons in the ultraviolet image.

[0082] It should be noted that in the equivalent conversion of the above area, when the result has a decimal, the rounding method is used to obtain an integer equivalent number of photons.

[0083] Through the above method, first, a three-level hybrid denoising architecture is adopted to separate noise and photon signals, and filtering and binarization processing are carried out. At the same time, the number of connected components in the binary image is counted without setting a defense for the area of the connected components. Instead, based on connected components of different sizes, the overall area is determined, and the method of dividing by the pixel area of a calibrated single photon is used to convert the equivalent number of photons, ensuring that even if multiple photon signals overlap in the same area, their total contribution will be correctly counted, thus avoiding the problem of underestimating the number of photons in the traditional method.

[0084] Obtain an ultraviolet image and perform multi-scale decomposition to separate noise objects and photon objects, including the following steps:

[0085] S110, obtain the scale factor and translation factor, and perform wavelet transform on the ultraviolet image in combination with the Haar wavelet function.

[0086] First, the formula for multi-scale decomposition in the embodiments of the present application is as follows:

[0087] ,

[0088] where a represents the scale factor, b represents the translation factor, represents the mother wavelet function. In the embodiments of the present application, the Haar wavelet function is adopted, t represents the current time, represents the wavelet coefficient.

[0089] S120. Analyze the low-frequency features and high-frequency features in the ultraviolet image by changing the scale factor to obtain the frequency distribution, and control the position of the Haar wavelet function on the time axis by changing the translation factor to obtain the position distribution.

[0090] The scale factor is used to stretch or scale the wavelet function at multiple scales. When the scale factor increases, the wavelet function is stretched, and it will analyze the features of larger scales (low frequencies) in the signal; when the scale factor decreases, the wavelet function is compressed, and it can focus on the features of smaller sizes (high frequencies) in the signal. Therefore, the low-frequency features and high-frequency features of each signal in the ultraviolet image can be analyzed through the scale factor to analyze the frequency distribution.

[0091] The translation factor can control the position of the wavelet function on the time axis, and analyze the signal by continuously changing the size of the translation factor to adjust different positions, so as to analyze the position distribution of the signal.

[0092] The Haar wavelet has a rectangular waveform. In signal analysis, it can effectively capture the mutation points and local features of the signal. Regarding the ultraviolet image as a signal f(t) and performing discrete wavelet transform on it, the image can be decomposed into components of different frequencies. Among them, the high-frequency components contain the detailed information of the image, such as the edges, textures and other features of the objects in the image.

[0093] S130. Generate the high-frequency components of the feature points based on the frequency distribution and the position distribution.

[0094] The high-frequency components are characterized as the partial frequency components in the image that reflect the details and high-speed change features after wavelet transform, and they reflect the fine structure and change situation of the signal in the local area.

[0095] S140. Define the feature points corresponding to the high-frequency components with concentrated position distribution and stable frequency distribution as photon objects, and define the feature points corresponding to the high-frequency components with discrete position distribution and fluctuating frequency distribution as noise objects.

[0096] In solar-blind ultraviolet imaging, photon signals and random noise present different features in the high-frequency components.

[0097] First, for photon signals, they exhibit strong spatial concentration and relatively stable intensity. Therefore, in the image, the high-frequency components corresponding to photon signals will gather together in spatial positions, and the high-frequency distribution part is relatively stable. For example, when an electrical device discharges to generate photons, the signals formed by these photons on the image are concentrated in a certain local area, and their intensity changes little in a short period of time.

[0098] Secondly, for the noise signals corresponding to random noise, they exhibit uniform distribution, and the intensity changes of the high-frequency component part fluctuate greatly. Random noise may appear at various positions in the image, without an obvious concentrated area, and its intensity may change greatly at any time, being large or small. For example, the noise generated by electromagnetic interference during the imaging process will be evenly distributed throughout the image.

[0099] Therefore, by analyzing the position distribution and frequency distribution in the high-frequency components, the corresponding photon objects and noise objects can be separated.

[0100] In some other embodiments, filtering and eliminating the noise objects and retaining the photon objects includes the following steps:

[0101] S210, after performing wavelet transform to obtain wavelet coefficients, extract high-frequency subband coefficients from the wavelet coefficients, calculate the median absolute deviation of each high-frequency subband coefficient, and calculate the noise standard deviation in combination with a preset distribution constant.

[0102] After performing wavelet transform, wavelet coefficients are calculated. The wavelet coefficients are a coefficient matrix, which contains different frequency bands, including LL (low frequency - low frequency), LH (low frequency - high frequency), HL (high frequency - low frequency), HH (high frequency - high frequency). Among them, the high-frequency subband coefficients include LH, HL, and HH. The high-frequency subband coefficients contain the detailed information of the image and can be used to identify the edges and features of photon information.

[0103] When filtering the noise objects, the soft threshold filtering method needs to be used. Then, first, a threshold for distinguishing noise objects and photon objects needs to be set. At the same time, because different image precisions, different image sizes, etc. all affect the precision of the filtering process, the threshold needs to be further dynamically processed so that it can be applicable to more scenarios.

[0104] Specifically, first extract all high-frequency subband coefficients from the wavelet coefficients, and calculate the median absolute deviation of all coefficients. The specific formula is:

[0105]

[0106] Among them, Characterized as high-frequency subband coefficients.

[0107] After calculating the median absolute deviation, the noise standard deviation is calculated through the following formula.

[0108] ,

[0109] where, Characterized as a constant derived based on the Gaussian noise distribution.

[0110] By calculating the noise standard deviation through the above method, the noise standard deviation is a key indicator of the noise intensity in the horizontal image.

[0111] S220, calculate the total number of pixels in the ultraviolet image, and calculate the dynamic threshold in combination with the noise standard deviation.

[0112] Secondly, obtain the total number of pixels N in the ultraviolet image, and calculate the dynamic threshold based on the following formula:

[0113] ,

[0114] In the related art, generally, after calculating the noise standard deviation, noise filtering is directly performed based on this value, which seriously depends on linear filters such as the median value. Such methods will cause signal distortion in a complex electromagnetic interference environment. At the same time, the fixed median filtering threshold is difficult to adapt to dynamic noise (daylight fluctuations), and will blur the photon pulse transformation.

[0115] Therefore, in the embodiments of the present application, while calculating the noise standard deviation, the total number of pixels in the image is collected. Generally speaking, there is no direct numerical relationship between the noise standard deviation and the total number of pixels. However, in actual applications, the total number of pixels will affect the accuracy of the noise standard deviation. When the total number of pixels is large, the number of samples used to calculate the median absolute deviation is large, and the estimated value of the noise standard deviation obtained will be more accurate and stable. On the contrary, when the total number of pixels is small, the error of the estimated value may be large.

[0116] Therefore, by collecting the total number of pixels, the dynamic weight of the noise standard deviation can be further dynamically verified. When dealing with different images and noise situations, the filtering threshold calculated by the noise standard deviation can be dynamically optimized based on the weight corresponding to the total number of pixels, and the dynamic change relationship of the threshold under different relationships between the noise quantity and the pixel quantity can be further accurately determined.

[0117] At the same time, through the dynamically adjusted threshold, it is also possible to avoid the loss of some weak photon signals due to over-segmentation in some cases where the imaging effect of the image is poor.

[0118] S230, compare each wavelet coefficient with the dynamic threshold, and filter all wavelet coefficients less than the dynamic threshold to complete the filtering of the ultraviolet image.

[0119] By setting a dynamic threshold to filter the threshold, for each wavelet coefficient, if its value is greater than the dynamic threshold, it is considered that this coefficient is very likely to represent a photon signal and is retained. Conversely, if it is less than the dynamic threshold, it is considered that it may represent a noise signal and is discarded to complete the filtering.

[0120] The basis for filtering is based on: Since the intensity of photon signals is relatively stable and concentrated, the high-frequency coefficients corresponding to most photon signals will be greater than the threshold, while the intensity of random noise fluctuates greatly, and the high-frequency coefficients corresponding to many noises will be less than the threshold, so that an effective separation of photon signals and noises can be achieved.

[0121] Since the intensity of photon signals is relatively stable and concentrated, the high-frequency coefficients corresponding to most photon signals will be greater than the threshold, while the intensity of random noise fluctuates greatly, and the high-frequency coefficients corresponding to many noises will be less than the threshold, so that an effective separation of photon signals and noises can be achieved.

[0122] In some other embodiments, after calculating the dynamic threshold, in order to further optimize the size of the dynamic threshold, the following steps are further included:

[0123] S240, perform regional segmentation on the ultraviolet image based on the total number of pixels to obtain a number of sub-images.

[0124] The local feature entropy of an image can reflect the complexity and information richness of the region. In an image, the region where photon signals are concentrated usually has a higher feature entropy, while the feature entropy of the noise region is relatively low. By calculating the feature entropy of the local region of the image, the threshold coefficient can be dynamically adjusted according to the complexity of different regions, so as to more accurately separate photon signals and noises.

[0125] Therefore, first segment the ultraviolet image with a small window to obtain a number of sub-images, and the segmentation size can be adjusted according to the specific size, resolution, detection environment, etc. of the image.

[0126] S250, calculate the sub-wavelet coefficients corresponding to each sub-image, and statistically analyze the probability distribution of the sub-wavelet coefficients with each value.

[0127] After segmentation, it is necessary to calculate the sub-wavelet coefficients corresponding to each sub-image respectively, and calculate the feature entropy of each local image based on the analysis of the sub-wavelet coefficients.

[0128] Specifically, statistically analyze the distribution of wavelet coefficients in each sub-image, calculate the probability of the coefficient value of each wavelet coefficient appearing, and thus obtain its probability distribution.

[0129] Such as coefficient The number of occurrences is count( )), then its probability is .

[0130] Among them, m and n respectively represent the horizontal distance and the vertical distance of the sub-image.

[0131] S260. Calculate the characteristic entropy corresponding to each sub-image based on the probability distribution, and calculate the average entropy corresponding to several sub-images.

[0132] After calculating the probability distribution content of each wavelet coefficient, calculate the characteristic entropy based on the definition formula of the characteristic entropy in combination with the probability distribution. Specifically:

[0133] ,

[0134] The summation here is performed on all different wavelet coefficients within each sub-image.

[0135] After calculating the characteristic entropy corresponding to each sub-image, it is also necessary to calculate the average entropy corresponding to all sub-images. The calculation process of the average entropy is to accumulate the characteristic entropies of all sub-images and divide by the number of sub-images to calculate.

[0136] The characteristic entropy represents the richness of photon signals in each independent local region, and the average entropy also represents the average richness of photon signals in the entire image.

[0137] S270. Establish a linear mapping function based on the characteristic entropy and the average entropy to calculate the threshold adjustment coefficient, and optimize the dynamic threshold based on the threshold adjustment coefficient.

[0138] Finally, it is necessary to determine the corresponding coefficient for adjusting the final dynamic threshold through the characteristic entropy and the average entropy.

[0139] First, retrieve a mapping function to establish a linear mapping relationship, and further map the characteristic entropy to a threshold adjustment factor.

[0140] The linear mapping function in this application is:

[0141] ,

[0142] Among them, represents the threshold adjustment coefficient, H represents the characteristic entropy of the current sub-image, represents the average entropy, and k is an adjustable constant parameter, which is determined through experiments and generally takes -0.1.

[0143] When the feature entropy is greater than the average entropy, it indicates that the photon signal features in this sub-image are more complex compared to the overall average, and it may contain more detailed photon information. At this time, the threshold adjustment coefficient is less than 1, which means that the dynamic threshold needs to be dynamically reduced to retain more photon signals. When the feature entropy is less than the average value, it indicates that the photon signal features in this sub-image are relatively simple compared to the overall average and may contain more random noise. At this time, the threshold adjustment coefficient will be greater than 1, which means that the dynamic threshold needs to be dynamically increased to filter out more noise signals to enhance the suppression of noise.

[0144] Then, after finally optimizing the dynamic threshold, the new dynamic threshold is:

[0145] ,

[0146] Through the above dynamic threshold optimization scheme based on feature entropy, it is possible to adaptively adjust the dynamic threshold according to the complexity of the features in the local area of the image, more accurately separate photon signals and noise in different regions, and improve the denoising effect and the accuracy of photon number statistics.

[0147] In some other embodiments, the filtered ultraviolet image is binarized to obtain a binary image, including the following steps:

[0148] S310, obtain the pixel intensity of each pixel point in the filtered ultraviolet image, and calculate the global mean value of the image as a reference value.

[0149] After filtering the ultraviolet image, it is necessary to provide an image basis for subsequent steps such as connected component search and marking, and it is also necessary to binarize the ultraviolet image.

[0150] During binarization, first obtain the pixel intensity of each pixel point in the ultraviolet image, that is, the gray value size of each pixel point on this image, recorded as I(x, y).

[0151] Calculate the global mean value of the ultraviolet image. The global mean value is characterized as the result obtained by dividing the sum of the pixel intensities of all pixel points in the image by the total number of pixel points, recorded as . The global mean value serves as an adaptive threshold, that is, a reference value for determining whether a pixel point is 0 or 1.

[0152] S320, assign a value of 1 to the pixel points with pixel intensity higher than the reference value to define them as foreground pixels, and assign a value of 1 to the pixel points with pixel intensity lower than the reference value to define them as background pixels.

[0153] Establish a binarization process system. Specifically:

[0154] ,

[0155] Characterized as a binarization result, when the pixel intensity is higher than the reference value, the corresponding pixel is assigned a value of 1, and at the same time it is defined as a foreground pixel (photon). When the pixel intensity is lower than the preset value, the corresponding pixel is assigned a value of 0, and at the same time it is defined as a background pixel (background noise).

[0156] S330, eliminate background pixels through morphological closing operation of dilation first and then erosion to obtain a binarized image.

[0157] After binarization processing, it is also necessary to eliminate discrete noise through morphological closing operation. Among them, the dilation operation is characterized by first expanding the foreground area in the image to fill the holes in the foreground area; the erosion operation is to shrink the foreground area to remove noise and small discrete points, while retaining the morphology and boundary features of important photon signal pulses.

[0158] Through binarization conversion and morphological closing operation, the clarity and distinguishability of the signal are enhanced (the binarized image only contains 0 and 1), while discrete noise is removed and the integrity of the photon signal is retained, thus providing a more accurate basis for subsequent connected component analysis and photon number statistics.

[0159] In some other embodiments, analyze the connectivity relationship between each pixel point and adjacent pixel points in the binarized image, and establish several connected components containing several foreground pixels based on the analysis results, including the following steps:

[0160] S410, traverse all pixel points in the binarized image and mark the foreground pixels passed through to add labels.

[0161] S420, when marking, based on the 8-neighborhood algorithm, judge whether there are foreground pixels that are adjacent in position and have labels around. If so, select the label with the smallest value that conforms to the connectivity relationship for marking. If not, generate a new label recursively based on the largest value among the existing labels in the current binarized image.

[0162] S430, jointly form a connected component with several foreground pixels with the same label.

[0163] First, perform the search for connected components:

[0164] Use DFS to search for connected components in the binarized image. During the search process, start from each unmarked pixel, check its adjacent pixels through the 8-neighborhood, and a group of pixels that are adjacent to the current pixel and have the same attribute will be regarded as the same connected component. It should be noted that for the search for connected components, the search objects are all foreground pixels.

[0165] Secondly, perform the marking of connected components:

[0166] For each detected connected component, all pixels within that component are marked with a unique label. By using a recursive method, the same label is assigned to all pixels belonging to the same connected component, and the marking information of each pixel is stored in a two-dimensional array.

[0167] During the marking process, first, in the first scan, starting from the upper left corner of the image, each pixel is scanned row by row and column by column. When a foreground pixel is encountered and it is not marked, a new connected component is started and a label is provided, and all adjacent foreground pixels will also be marked as belonging to the same connected component and provided with the same label.

[0168] When a foreground pixel is scanned and it is found that there is more than one label, that is, there is a conflict between the labels, the label with the smallest value within the 8-neighborhood is selected.

[0169] After that, a second scan is performed to check all unmarked foreground pixels and assign them the corresponding connected component labels.

[0170] By the above method, all connected components in the binary image are effectively identified and marked, providing basic data for subsequent area calculation and photon number statistics.

[0171] In some other embodiments, to calculate the total area of all connected components by counting the number of foreground pixels in each connected component, obtain the pixel area of a single pre-calibrated photon object, and convert the equivalent photon number based on the pixel area and the total area, the following steps are included:

[0172] S510, Define the pixel area of a single pre-calibrated photon object as the standard area.

[0173] After statistically marking all connected components in the image, first obtain the pixel area of a single pre-calibrated photon object, which is obtained after the experiment and represents the pixel area that an ideal photon object occupies in the image, and is mainly determined by the resolution of the detector.

[0174] S520, Calculate the sub-area based on the number of foreground pixels included in each connected component.

[0175] Judge the number of foreground pixels included in each connected component, and combine the resolution of the image to obtain the size of each pixel point, and thus calculate the sub-area of the entire connected component.

[0176] Normally, only by calculating the ratio of the area of each connected component to the area of a single photon can the number of photons N included in all connected components be calculated, as shown in the following formula:

[0177] ,

[0178] Among them, M represents the total number of connected regions, represents the sub - area corresponding to the i - th connected region, represents the pixel area corresponding to a single photon pixel.

[0179] However, in fact, since the binary image only reflects the difference between foreground pixels and background pixels and cannot intuitively reflect the pixel intensity of photon pixels in each pixel point, when there is overlap between multiple photons, part of the overlapping photon information will be fused in one pixel grid after binarization, which makes the binary image actually slightly ignore the area of some superimposed photons.

[0180] Therefore, in order to optimize the ignored photon area and improve the accuracy of the final photon count, in this application, it is also necessary to dynamically adjust the fixed standard area corresponding to a unit photon pixel to a dynamic pixel area that changes based on the environment and scenario.

[0181] Specifically,

[0182] S530, obtain the maximum - to - minimum aspect ratios of each connected region and generate a first coefficient.

[0183] First, determine the maximum and minimum aspect ratios of each connected region. Since normal photon signals without overlap will appear approximately circular or rectangular in actual imaging and have relatively clear boundaries, when multiple photon signals overlap, if they overlap horizontally, the horizontal pixel distance of the connected region corresponding to the overlapping photons will be greater than its vertical pixel distance; if they overlap vertically, the vertical pixel distance of the connected region corresponding to the overlapping photons will be greater than its horizontal pixel distance; and when multiple photons overlap randomly, the shape of the corresponding connected region will be extremely irregular, and the ratio differences between the longest width and the shortest length, and between the longest length and the shortest width will be relatively large.

[0184] Therefore, by calculating the maximum - to - minimum aspect ratios such as maximum length - maximum width, minimum length - minimum width, maximum length - minimum width, and minimum length - maximum width in each connected region, the shape distribution of each connected region can be analyzed.

[0185] Ideally, when there are no overlapping photons, the maximum and minimum values of each aspect ratio should be close to 1. When any aspect ratio is abnormal, the larger the abnormal value, the more likely the corresponding number of overlaps is, and at this time, more overlapping photon information may be ignored by the binary image. Therefore, in order to reduce the measurement error, the first coefficient can be appropriately reduced so that the finally calculated number of photons increases to make up for the error of the ignored overlapping photons.

[0186] S540, Obtain the position distribution of the hole pixels in each connected component to generate a second coefficient. The hole pixels are characterized as foreground pixels that do not all have a connected relationship in the 8-neighborhood.

[0187] The presentation form of the photon signal is generally high and dense in the center and low and dispersed around. Then, when there is an overlap between photons, due to the position difference between two or more overlapping photons, there will be certain holes in the binary image.

[0188] Then it is necessary to scan and screen the hole pixels in the adjacent connected components. The hole pixels are characterized as not all adjacent to other foreground pixels in the 8-neighborhood directions of a foreground pixel, but there are some adjacent background pixels. In this case, it is recognized as a hole pixel.

[0189] Generally speaking, the pixels on the boundary of the photon signal are all hole pixels. Since the pixels at the center position of the photon have higher energy, there are generally no hole pixels at the center position. When multiple photons overlap, if the edge parts of two or more photons overlap, a large number of hole pixels will appear in the central part of a connected component in a region (because the energy of the edge part of the photon is lower, which is characterized as sparser foreground pixels at the edge of the photon in the binary image). When the central parts of two or more photons overlap, there will be no hole pixels in the central part of the connected component (because the overlapping part is at the center positions of several photons, and the energy at the photon center is higher, which is characterized as denser foreground images at the photon center in the binary image).

[0190] Therefore, the overlapping situation between multiple photons can be intuitively analyzed through the position distribution relationship of the hole pixels in a connected component. If there are a large number of hole pixels in the central part of the connected component, it may be that the edge parts of multiple photons overlap, and the number of binary pixels lost due to the overlap is relatively small. Then the second coefficient can be relatively high to maintain the pixel area of the standard photon object. If there are not a large number of hole pixels in the central part of the connected component, it may be that the central parts of multiple photons overlap, and the number of binary pixels lost due to the overlap is relatively large. Then, in order to avoid excessive loss of pixels that cannot be reflected by binary at the overlapping position, the second coefficient can be appropriately reduced to reduce the pixel area of the photon object so that the finally calculated number of photons increases dynamically.

[0191] S550, Obtain the ratio between the sub-area of each connected component and the overall area of the ultraviolet image to generate a third coefficient.

[0192] Finally, it is also necessary to determine the ratio of the sub - area of each connected component to the overall area of the ultraviolet image. If the proportion of the sub - area in the overall area is relatively large, it indicates that the proportion of photons in this ultraviolet image is relatively large. On the one hand, this may be determined by the size of the shooting frame, or it may be determined by a relatively high level of situations such as discharge and corona.

[0193] Then, theoretically, the larger the occupied size of the photon object in the image, the greater the number of photons and the greater the possibility of photon overlap. Therefore, in order to make up for the influence of pixel loss caused by overlap, the size of the third coefficient can be appropriately reduced to dynamically increase the finally calculated number of photons.

[0194] When the proportion of the sub - area in the overall area is relatively small, it indicates that the proportion of photons in this ultraviolet image is relatively small. Then, the number of photons and the possibility of photon overlap are both relatively small. At this time, the size of the third coefficient can be increased.

[0195] S560, adjust the standard area based on the first coefficient, the second coefficient, and the third coefficient to obtain the final pixel area.

[0196] Generate several dynamic coefficients through the above - mentioned analysis indicators of image information, and dynamically adjust the standard area of a single photon pixel based on different scenarios and different images to optimize the content loss caused by photon overlap. The finally calculated photon pixel area can be closer to the actual needs based on the actual scenario.

[0197] S570, divide each sub - area by the pixel area to obtain the number of photons per unit, and add up several numbers of photons per unit to calculate the equivalent number of photons.

[0198] Divide each sub - area by the pixel area to obtain the number of photons per unit calculated for each connected component, and add up the photons corresponding to several connected components to obtain the equivalent number of photons contained in the entire ultraviolet image finally.

[0199] In some other embodiments, when calculating the equivalent number of photons based on the pixel area and the total area, it further includes the following steps:

[0200] S571, label the connected components with a sub - area smaller than the pixel area of the photon object as objects to be verified.

[0201] In some cases, it may occur that the area of some connected components is smaller than the pixel area of a single photon. There are two possibilities for this kind of situation. One is that during the processes of filtering, binarization, and morphological closing operation, the weak signals at the boundary of the photon object are erroneously eliminated, resulting in the binarized connected component area of a single photon signal being smaller than the normal pixel area of a single photon. The other situation is that some background noises are not correctly processed and are wrongly converted into photon objects.

[0202] If these small connected domains are not specially processed, there will be certain errors in the final calculated number of photons, which may be too large or too small.

[0203] Therefore, these small connected domains are first selected and marked as objects to be verified for further analysis and processing.

[0204] S572, calculating the minimum pixel distance between the object to be verified and other connected domains.

[0205] S573, when the minimum pixel distance is less than a preset value, the unit photon number calculated for the object to be verified is set to 1.

[0206] S574, when the minimum pixel distance is greater than a preset value, the object to be verified is not involved in the calculation of the unit photon number.

[0207] The minimum pixel distance between the edge of the object to be verified and other connected domains is analyzed, and the distance is characterized as the minimum distance between the object to be verified and other connected domains.

[0208] When the minimum pixel distance is greater than the preset value, it is considered that the object to be verified is far away from other large areas with high probability of being photon objects. In the actual application scenario of ultraviolet imagers, there will not be a situation where a certain fault causes only one photon signal and the large photon area corresponding to the actual fault is extremely far away, which will lead to a low correlation between the small area and other large areas. Therefore, it is considered that the object to be verified is likely to be background noise that is mistakenly identified as a photon object. Then, when calculating the unit photon number, the connected domain sub-area corresponding to the object to be verified is not calculated.

[0209] When the minimum pixel distance is less than the preset value, it is very likely that the photon pixel is actually in the same photon object as the shortest adjacent connected domain, but some photon pixels are mistakenly eliminated during signal processing and cannot be connected with other areas. Therefore, the unit photon number less than 1 calculated for the object to be verified can be directly changed to 1, which is assumed to correspond to one photon number.

[0210] Through the above optimization, the area of the photon object that is mistakenly eliminated or the area of the noise object that is mistakenly identified is effectively analyzed and processed, and the numerical accuracy of the equivalent photon number finally calculated is optimized.

[0211] In some other embodiments, the following steps are also included:

[0212] S600, obtaining the measured distance of the ultraviolet image, and correcting the equivalent photon quantity based on the attenuation model. Specifically,

[0213] ,

[0214] Among them, is characterized as the corrected photon number, is characterized as the equivalent photon number, d is characterized as the measured distance, and D is characterized as the constant parameter of the ultraviolet imager.

[0215] Further correction is carried out according to the attenuation characteristics of the photon number with distance. When ultraviolet photons propagate in the atmosphere, as the distance increases, the influence of scattering and absorption will also increase accordingly, and the calculated photon number will show a certain attenuation law.

[0216] Traditional photon attenuation models usually use a simple inverse-square attenuation law. However, in practical applications, due to the influence of factors such as the atmospheric environment, equipment characteristics, and measurement distance, the attenuation of photons is not just a simple inverse-square relationship. Therefore, a more refined correction method is needed.

[0217] Therefore, considering the distance attenuation characteristics, the measured photon number is corrected to improve the accuracy of long-distance measurement.

[0218] Among them, D can be obtained through experimental calibration and is characterized as the distance attenuation parameter. By measuring the photon number at different known distances and using these experimental data for curve fitting, the correction factor D for attenuation can be obtained, so that the correction model can adapt to different environments and equipment characteristics.

[0219] This model can effectively compensate for the photon number attenuation error caused by distance. Especially in long-distance measurements (such as more than 50 meters), the photon number may be significantly underestimated due to attenuation. After correction using a quadratic function, the measured photon number can be closer to the actual photon number.

[0220] Furthermore, when ultraviolet photons propagate in the atmosphere, high humidity and high haze conditions will also exacerbate photon attenuation. Then, according to the relationship between humidity, haze concentration, etc. and photon attenuation, a corresponding humidity correction factor k1 and haze attenuation factor k2 can be established, and the correction formula can be optimized to obtain:

[0221] ,

[0222] This application also discloses a photon number statistical system for a solar-blind ultraviolet imager to implement the above method.

[0223] Through the above steps, a three - level hybrid denoising architecture is first adopted to separate the noise and photon signals, and filtering and binarization processing are carried out. At the same time, the number of connected components in the binarized image is counted, and no specific defense is set for the area of the connected components. Instead, based on connected components of different sizes, the overall area is determined, and the equivalent number of photons is calculated by dividing by the pixel area of a calibrated single photon. This ensures that even if multiple photon signals overlap in the same area, their total contribution will be correctly counted, thus avoiding the problem of underestimating the number of photons in traditional methods.

[0224] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and they can be executed in other orders.

[0225] The above are all preferred embodiments of this application, and the protection scope of this application is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for photon number statistics of a solar-blind ultraviolet imager, characterized in that, It includes the following steps: Obtain an ultraviolet image and perform multi-scale decomposition to separate noise objects and photon objects; Filter and eliminate the noise objects in the ultraviolet image and retain the photon objects; Perform binarization processing on the filtered ultraviolet image to obtain a binary image; Analyze the connectivity relationship between each pixel point and its adjacent pixel points in the binary image, and establish several connected regions containing several foreground pixels based on the analysis results. The connectivity relationship includes position connectivity and attribute connectivity; Count the number of foreground pixels in each connected region to calculate the total area of all the connected regions, obtain the pixel area of a single pre-calibrated photon object, and calculate the equivalent photon number based on the pixel area and the total area. Specifically, Define the pixel area of a single pre-calibrated photon object as the standard area; Calculate the sub-area based on the number of foreground pixels contained in each connected region; Obtain the maximum aspect ratio of each connected region and generate a first coefficient; Obtain the position distribution of the hole pixels in each connected region to generate a second coefficient. The hole pixels are characterized as foreground pixels that do not all have a connectivity relationship in the 8-neighborhood; Obtain the ratio between the sub-area of each connected region and the overall area of the ultraviolet image to generate a third coefficient; Adjust the standard area based on the first coefficient, the second coefficient, and the third coefficient to obtain the final pixel area; Divide each sub-area by the pixel area to obtain the number of photons per unit, and sum up several numbers of photons per unit to calculate the equivalent photon number.

2. The method for counting the number of photons of a solar-blind ultraviolet imager according to claim 1, wherein, Obtain an ultraviolet image and perform multi-scale decomposition to separate noise objects and photon objects, including the following steps: Obtain a scale factor and a translation factor, and perform wavelet transform on the ultraviolet image in combination with the Haar wavelet function. Among them, Analyze the low-frequency features and high-frequency features in the ultraviolet image by changing the scale factor to obtain a frequency distribution, and control the position of the Haar wavelet function on the time axis by changing the translation factor to obtain a position distribution; Generate the high-frequency components of the feature points based on the frequency distribution and the position distribution; Define the feature points corresponding to the high-frequency components with concentrated position distribution and stable frequency distribution as the photon objects, and define the feature points corresponding to the high-frequency components with discrete position distribution and fluctuating frequency distribution as the noise objects.

3. The method for photon number statistics of a solar-blind ultraviolet imager according to claim 2, characterized in that, Filter and eliminate the noise objects and retain the photon objects, including the following steps: After performing the wavelet transform, obtain wavelet coefficients, extract high-frequency sub-band coefficients from the wavelet coefficients, calculate the median absolute deviation of each high-frequency sub-band coefficient, and calculate the noise standard deviation in combination with a preset distribution constant; Calculate the total number of pixels in the ultraviolet image, and calculate the dynamic threshold in combination with the noise standard deviation; Compare each wavelet coefficient with the dynamic threshold, and filter all wavelet coefficients smaller than the dynamic threshold to complete the filtering of the ultraviolet image.

4. The method for photon number statistics of a solar-blind ultraviolet imager according to claim 3, characterized in that, After calculating the dynamic threshold, it further includes the following steps: Perform regional segmentation on the ultraviolet image based on the total number of pixels to obtain a number of sub-images; Calculate the sub-wavelet coefficients corresponding to each of the sub-images, and statistically analyze the probability distribution of the sub-wavelet coefficients with each value; Calculate the characteristic entropy corresponding to each of the sub-images based on the probability distribution, and calculate the average entropy corresponding to the number of sub-images; Establish a linear mapping function based on the characteristic entropy and the average entropy to calculate a threshold adjustment coefficient, and optimize the dynamic threshold based on the threshold adjustment coefficient.

5. The method for photon number statistics of a solar-blind ultraviolet imager according to claim 1, characterized in that, Perform binarization processing on the filtered ultraviolet image to obtain a binarized image, including the following steps: Obtain the pixel intensity of each pixel point in the filtered ultraviolet image, and calculate the global mean value of the image as a reference value; Assign the pixel points with pixel intensity higher than the reference value to 1 to define them as foreground pixels, and assign the pixel points with pixel intensity lower than the reference value to 0 to define them as background pixels; Eliminate the background pixels through morphological closing operation of first dilation and then erosion to obtain the binarized image.

6. The method for counting the number of photons of a solar-blind ultraviolet imager according to claim 5, characterized in that, Analyze the connectivity relationship between each pixel point and adjacent pixel points in the binarized image, and establish a number of connected regions containing a number of foreground pixels based on the analysis results, including the following steps: Traverse all pixel points in the binarized image and mark the foreground pixels passed through to add labels; When marking, based on the 8-neighborhood algorithm, determine whether there are foreground pixels with adjacent positions and existing labels around. If so, select the label with the smallest value that conforms to the connectivity relationship for marking. If not, generate a new label recursively based on the largest value among the existing labels in the current binarized image; Form a connected region by combining a number of foreground pixels with the same label.

7. The method for counting the number of photons of a solar-blind ultraviolet imager according to claim 1, characterized in that Convert the equivalent photon number based on the pixel area and the total area, and further include the following steps: Label the connected regions with sub-areas smaller than the pixel area of the photon object as objects to be verified; Calculate the minimum pixel distance between the object to be verified and other connected regions; When the minimum pixel distance is less than a preset value, set the calculated unit photon number of the object to be verified to 1; When the minimum pixel distance is greater than the preset value, the object to be verified does not participate in the calculation of the unit photon number.

8. The method for photon number statistics of a solar-blind ultraviolet imager according to claim 1, characterized in that, Further include the following steps: Obtain the measurement distance of the ultraviolet image, and correct the equivalent photon number based on the attenuation model. Specifically, ， wherein, is characterized as the corrected photon number, is characterized as the equivalent photon number, d is characterized as the measurement distance, and D is characterized as a constant parameter of the ultraviolet imager.

9. A solar-blind ultraviolet imager photon number statistics system, characterized in that, For implementing the method described in any one of claims 1-8.

Images