Imaging gray scale correction method, device and equipment of X-ray equipment

Abstract

The embodiment of the invention provides an imaging gray scale correction method, device and equipment of X-ray equipment. The method comprises the following steps: acquiring an X-ray image shot by equipment to be corrected, and determining an actual gray value of the X-ray image; determining a gray scale difference value between the actual gray scale value and a target gray scale value; determining a shooting parameter adjustment amount corresponding to the gray difference value based on a relation curve model corresponding to the to-be-corrected equipment; wherein the relation curve model represents a mapping relation between a shooting parameter of the to-be-corrected device and an imaging gray level, and the shooting parameter adjustment amount comprises a tube voltage adjustment amount and / or a tube current adjustment amount; and adjusting the tube voltage and / or tube current of the to-be-corrected equipment according to the shooting parameter adjustment amount so as to correct the imaging gray level of the to-be-corrected equipment to the target gray level value. The method is used for achieving the effect of improving the correction precision of the imaging gray scale of the X-ray equipment.

Description

Technical Field

[0001] This application relates to the field of industrial inspection technology, and in particular to an imaging grayscale correction method, apparatus and equipment for X-ray equipment. Background Technology

[0002] In industrial inspection scenarios, X-ray equipment, during long-term continuous operation, is affected by factors such as cumulative usage time, fluctuations in ambient temperature and humidity, and unstable power grid voltage. This causes irreversible degradation in the performance of core components, directly leading to imaging grayscale drift. Even with constant imaging parameters such as tube voltage and current, the overall grayscale value of the image of the object under inspection acquired by the equipment will still exhibit a systematic, slow shift or irregular fluctuation. Subsequent core image processing algorithms, such as defect identification and dimensional measurement, largely rely on fixed grayscale thresholds for judgment. Continuous instability in grayscale values ​​can cause algorithms to misjudge qualified parts and miss minute defects, severely compromising the consistency and reliability of inspection results.

[0003] In existing technologies, the calibration of X-ray equipment imaging parameters mostly adopts a manual, periodic calibration method. Typically, engineers preset initial imaging parameters such as tube current and tube voltage based on past experience. After a certain period of equipment operation, if abnormal image grayscale is found, parameters such as tube voltage and tube current are manually adjusted to restore the imaging effect. However, this calibration method relies on the engineer's subjective experience, lacks quantitative judgment and adjustment basis, and the calibration accuracy varies greatly and has poor consistency. Summary of the Invention

[0004] The imaging grayscale correction method, apparatus, and device for X-ray equipment provided in this application are used to improve the accuracy of imaging grayscale correction for X-ray equipment.

[0005] In a first aspect, embodiments of this application provide an imaging grayscale correction method for an X-ray device, comprising:

[0006] Acquire the X-ray image captured by the device to be calibrated, and determine the actual grayscale value of the X-ray image;

[0007] Determine the grayscale difference between the actual grayscale value and the target grayscale value;

[0008] The adjustment amount of the shooting parameters corresponding to the gray difference is determined based on the relationship curve model corresponding to the device to be calibrated; wherein, the relationship curve model characterizes the mapping relationship between the shooting parameters of the device to be calibrated and the imaging gray level, and the shooting parameter adjustment amount includes tube voltage adjustment amount and / or tube current adjustment amount;

[0009] Adjust the tube voltage and / or tube current of the device to be calibrated according to the shooting parameter adjustment amount, so as to correct the imaging grayscale of the device to be calibrated to the target grayscale value.

[0010] In one possible implementation, determining the actual grayscale value of the X-ray image includes:

[0011] On the X-ray image, taking the center point of the X-ray image as a reference, multiple regions of interest are divided along the intersection of the two diagonals of the X-ray image; wherein, each region of interest has a weight coefficient, which is set based on the distance from the center point of the X-ray image;

[0012] For each region of interest, the average value of all pixels within that region of interest is obtained by averaging the values ​​of the region of interest.

[0013] Based on the weight coefficients of the region of interest, a weighted average is performed on the average gray value of the region of interest to obtain the actual gray value of the X-ray image.

[0014] In one possible implementation, determining the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model corresponding to the device to be corrected includes:

[0015] Based on the grayscale difference, the relationship curve model, and the actual shooting parameters when the X-ray image was captured by the device to be corrected, the adjustment amount of the shooting parameters is calculated; or...

[0016] Substituting the grayscale difference into the relationship curve model, the adjustment amount of the shooting parameters is obtained by solving.

[0017] In one possible implementation, the method further includes:

[0018] Obtain the allowable operating extreme values ​​of the tube voltage and tube current of the device to be calibrated;

[0019] Based on the allowable operating extreme values ​​of the tube voltage and the allowable operating extreme values ​​of the tube current, multiple sets of experimental parameter pairs are obtained; wherein each set of experimental parameter pairs includes experimental tube voltage value and experimental tube current value;

[0020] For each pair of experimental parameters, the experimental image captured by the device to be calibrated according to the pair of experimental parameters is obtained, and the experimental grayscale value of the experimental image is determined to obtain the experimental grayscale value corresponding to the pair of experimental parameters.

[0021] Based on each set of experimental parameter pairs and the corresponding experimental grayscale values, the relationship curve model corresponding to the device to be calibrated is determined.

[0022] In one possible implementation, determining the relationship curve model corresponding to the device to be calibrated based on each set of experimental parameter pairs and the corresponding experimental grayscale values ​​of each set of experimental parameter pairs includes:

[0023] For any two pairs of experimental parameters, multiple sets of experimental differences are determined based on the two pairs of experimental parameters and the corresponding experimental grayscale values. Each set of experimental differences includes the experimental voltage difference, the experimental current difference, and the experimental grayscale difference.

[0024] According to the preset nonlinear function, the difference between each group of experiments is fitted to obtain the relationship curve model corresponding to the device to be calibrated.

[0025] In one possible implementation, the method further includes:

[0026] Determine the target grayscale value of the device to be calibrated; wherein the target grayscale value is the imaging grayscale value of the calibrated device or the preset imaging grayscale value of the detection task to be performed.

[0027] Secondly, embodiments of this application provide an imaging grayscale correction device for an X-ray equipment, comprising:

[0028] The acquisition module is used to acquire the X-ray image captured by the device to be calibrated and determine the actual grayscale value of the X-ray image;

[0029] The first determining module is used to determine the grayscale difference between the actual grayscale value and the target grayscale value;

[0030] The second determining module is used to determine the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model corresponding to the device to be calibrated; wherein, the relationship curve model characterizes the mapping relationship between the shooting parameters of the device to be calibrated and the imaging grayscale, and the shooting parameter adjustment amount includes tube voltage adjustment amount and / or tube current adjustment amount;

[0031] The correction module is used to adjust the tube voltage and / or tube current of the device to be corrected according to the shooting parameter adjustment amount, so as to correct the imaging grayscale of the device to be corrected to the target grayscale value.

[0032] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0033] The memory stores computer-executed instructions;

[0034] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0036] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0037] The imaging grayscale correction method, apparatus, and device for X-ray equipment provided in this application involve an electronic device acquiring an X-ray image captured by the device to be corrected and determining the actual grayscale value. The device calculates the grayscale difference between the actual and target grayscale values, and then calculates the corresponding adjustment amount of the imaging parameters based on a relationship curve model. The device's tube voltage and / or tube current are adjusted according to this adjustment amount. This method improves the accuracy and efficiency of X-ray equipment imaging grayscale correction by utilizing quantified grayscale deviation analysis and reliable relationship curve model calculations. It achieves automated grayscale correction of X-ray equipment imaging, reduces reliance on manual experience, and ensures the consistency and accuracy of correction precision. Simultaneously, it can respond in real-time to grayscale drift caused by equipment performance degradation or environmental changes, accurately maintaining the stability of the imaging grayscale. This reduces misjudgments and missed detections that may occur with subsequent detection algorithms that rely on fixed grayscale thresholds, effectively improving the long-term reliability and detection accuracy of the X-ray detection system. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] Figure 1 A flowchart illustrating the imaging grayscale correction method for X-ray equipment provided in this application. Figure 1 ;

[0040] Figure 2 A schematic diagram illustrating the division of the region of interest provided in this application;

[0041] Figure 3 A flowchart illustrating the imaging grayscale correction method for X-ray equipment provided in this application. Figure 2 ;

[0042] Figure 4 A flowchart illustrating the imaging grayscale correction method for X-ray equipment provided in this application. Figure 3 ;

[0043] Figure 5 A schematic diagram of the imaging grayscale correction device for the X-ray equipment provided in this application;

[0044] Figure 6 A schematic diagram of the structure of the electronic device provided in this application.

[0045] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0046] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0047] In related technologies, the reliance on manual periodic calibration is limited by the subjective experience of engineers and lacks quantitative judgment and adjustment standards, making it difficult to guarantee calibration accuracy and consistency. Therefore, the inventors of this application have conceived of establishing a precise mathematical model of the tube voltage, tube current and imaging grayscale of X-ray equipment, combining real-time image acquisition and calculation of actual grayscale values, comparing the difference with the reference grayscale value, and then using the model to deduce the required parameter changes. Ultimately, this achieves automatic calibration of the imaging grayscale of X-ray equipment by automatically adjusting the shooting parameters, thereby eliminating the reliance on manual experience, fundamentally solving the problem of unstable imaging quality caused by equipment performance drift, and improving calibration accuracy and consistency.

[0048] The executing entity in this application embodiment can be an electronic device with processing capabilities, such as a computer or server, and this application embodiment does not limit this. The electronic device can be communicatively connected to the device to be calibrated, and this application embodiment does not limit the method of communication connection, such as wired or wireless connection.

[0049] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0050] First, we will explain how to establish a curve model relating shooting parameters to image grayscale. Figure 1 A flowchart illustrating the imaging grayscale correction method for X-ray equipment provided in this application. Figure 1 ,like Figure 1As shown, the method includes:

[0051] S101. Obtain the allowable operating extreme values ​​of the tube voltage and tube current of the device to be calibrated.

[0052] For example, the device to be calibrated refers to an X-ray imaging device that requires grayscale correction. The permissible operating limits of the tube voltage refer to the maximum and minimum values ​​that the tube voltage can output when the device to be calibrated is operating safely and stably, or the permissible operating range of the tube voltage as stated in the device's instruction manual. The permissible operating limits of the tube current refer to the maximum and minimum values ​​that the tube current can output when the device to be calibrated is operating safely and stably, or the permissible operating range of the current as stated in the device's instruction manual.

[0053] In one example, after establishing a communication connection with the device to be calibrated, the electronic device can retrieve the pre-stored allowable operating limits for the transistor voltage and current from the device's built-in specification parameter database. Alternatively, the electronic device can receive the allowable operating limits for the transistor voltage and current of the device to be calibrated from user input.

[0054] S102. Based on the allowable operating extreme values ​​of tube voltage and tube current, multiple sets of experimental parameter pairs are obtained.

[0055] For example, each experimental parameter pair includes an experimental tube voltage value and an experimental tube current value. That is, an experimental parameter pair refers to a parameter combination consisting of an experimental tube voltage value and an experimental tube current value, used to control the device to be calibrated to perform an imaging experiment. Among them, the experimental tube voltage value is a discrete voltage value selected within the allowable operating extreme range of the tube voltage; the experimental tube current value is a discrete current value selected within the allowable operating extreme range of the tube current.

[0056] In one example, the electronic device can uniformly select several discrete voltage values ​​within the range of the allowable operating extreme values ​​of the tube voltage according to a preset sampling step size, and at the same time uniformly select several discrete current values ​​within the range of the allowable operating extreme values ​​of the tube current. The experimental tube voltage value and the experimental tube current value are fully combined to generate multiple sets of one-to-one corresponding experimental parameter pairs.

[0057] For example, the allowable operating extreme values ​​of the tube voltage of the device to be calibrated are determined to be 40kV to 120kV and the allowable operating extreme values ​​of the tube current are determined to be 0.5mA to 5.0mA. Then, the sampling step size is defined according to the determined allowable operating extreme values ​​and data volume requirements. The extreme value difference of the tube voltage is 80kV and the extreme value difference of the tube current is 4.5mA. Sampling is performed by dividing the current and voltage extreme values ​​by 100 respectively, resulting in 100 sampling points for each. This leads to 100×100=10000 sets of voltage and current parameter combinations, i.e., 10000 sets of experimental parameter pairs.

[0058] S103. For each pair of experimental parameters, acquire the experimental image captured by the device to be calibrated according to the pair of experimental parameters, determine the experimental grayscale value of the experimental image, and obtain the experimental grayscale value corresponding to the pair of experimental parameters.

[0059] For example, an experimental image refers to a digital imaging result obtained by capturing a preset empty detection disk after the device to be calibrated has loaded a certain set of experimental parameters. The empty detection disk refers to a standard imaging carrier with no object to be inspected on its surface and uniform material, used to eliminate interference from the shape of the object to be inspected on the image grayscale value. The experimental grayscale value refers to a quantitative value that can characterize the brightness of the experimental image.

[0060] In one example, the electronic device sends a parameter loading command to the device to be calibrated, controlling it to load each set of experimental parameter pairs in sequence and triggering a shooting command. After the device to be calibrated completes the shooting, it transmits the corresponding experimental image to the electronic device. The electronic device receives the image and classifies and stores it according to the number of the experimental parameter pair.

[0061] Furthermore, the electronic device can average the pixel values ​​of the experimental image to obtain an average pixel value, and use this average pixel value as the experimental grayscale value of the experimental image, that is, the experimental grayscale value corresponding to the set of experimental parameters. Alternatively, the electronic device can determine the experimental grayscale value of the experimental image based on a preset region of interest, and then obtain the experimental grayscale value corresponding to the set of experimental parameters.

[0062] The preset Region of Interest (ROI) refers to a specific region on an image pre-defined for calculating grayscale values, which can eliminate the influence of interference regions such as edge noise and invalid background on the accuracy of grayscale calculation. For example, an electronic device can call a preset ROI partitioning algorithm to divide multiple ROIs on the experimental image. This preset ROI partitioning algorithm can, for example, divide multiple square ROIs along the intersection of two diagonals with the image center point as the reference, or it can be a circular ROI, a regular grid-like ROI, or other structured ROIs that meet the grayscale statistical requirements and are partitioned with the image center area as the core. The embodiments of this application do not limit the partitioning method, shape, and number of ROIs.

[0063] For example, for each experimental image, multiple square regions of interest are defined along the intersection of two diagonals with the center point of the image as the reference. Different weight coefficients are assigned to each region according to the distance between the region and the center point. The average gray value of all pixels in each region of interest is calculated. Then, the average gray value of each region is weighted and averaged to obtain the experimental gray value corresponding to the experimental image.

[0064] The intersection path of the two diagonals refers to the area division path that extends along the diagonal of the image with the center point as the intersection point. Figure 2 A schematic diagram illustrating the division of the region of interest provided in this application, such as... Figure 2 As shown, nine regions of interest were divided along the intersection of the two diagonals of the experimental image. It should be noted that this embodiment does not limit the number of regions of interest.

[0065] Each region of interest (ROI) has a weighting coefficient, which is set based on its distance from the center point of the experimental image. In other words, the weighting coefficient is a quantization coefficient assigned based on the distance between each ROI and the center point. The closer the distance, the higher the weight, which is used to compensate for the "halo effect" in X-ray imaging.

[0066] refer to Figure 2 As shown, the electronic device invokes a preset ROI partitioning algorithm to first locate the coordinates of the center point of the experimental image, and then delineates nine square regions of interest (ROIs) along the intersection of the two diagonals of the image. The center ROI is designated as ROI9, the middle ring as ROIs (ROIs 5-8), and the edge ring as ROIs (ROIs 1-4). Weight coefficients are assigned to the ROIs in different rings based on their distance from the center point of the experimental image; for example, the center ROI has a weight of 1.0, the middle ring ROIs have a weight of 0.8, and the edge ring ROIs have a weight of 0.5. It should be noted that this embodiment does not limit the specific values ​​of the weight coefficients; they can be set according to actual needs.

[0067] Furthermore, the electronic device can assign different weight coefficients based on the distance between each region and the center point, calculate the average gray value of all pixels within each region of interest, and then perform a weighted average of the average gray values ​​of each region to obtain the experimental gray value corresponding to the experimental image. Simultaneously, this experimental gray value is associated with and stored in relation to the corresponding experimental parameter pair. For example, Table 1 provides an example of the experimental parameter pair and corresponding experimental gray value provided in the embodiments of this application.

[0068] Table 1. Experimental parameters and corresponding experimental gray values

[0069]

[0070] S104. Based on each set of experimental parameters and the corresponding experimental grayscale values, determine the relationship curve model corresponding to the device to be calibrated.

[0071] For example, the relationship curve model is used to characterize the mapping relationship between the imaging parameters of the device to be calibrated and the imaging grayscale, wherein the imaging parameters include tube current and / or tube voltage.

[0072] It is understandable that a relationship curve model corresponding to each type of device to be calibrated can be established based on the aforementioned steps. In other words, devices to be calibrated with the same manufacturer, model, and equipment parameters can share the same relationship curve model.

[0073] In some possible implementations, the electronic device can directly perform fitting processing according to a preset nonlinear function based on multiple sets of experimental parameter pairs and their corresponding experimental grayscale values, as shown in Table 1, to obtain the relationship curve model corresponding to the device to be calibrated. In this case, the relationship curve model can characterize the mapping relationship between the shooting parameters (tube voltage and / or tube current) and absolute quantities such as grayscale values. It should be noted that the form of the nonlinear function is not limited in this application embodiment; for example, it can be a bivariate polynomial, an exponential function, or other nonlinear functions suitable for fitting shooting parameters and grayscale values.

[0074] For example, this relationship curve model can be represented as ,in, Represents grayscale value, Indicates the tube voltage value. The value represents the tube current, and a, b, c, d, e, and f are coefficients. It can be understood that the values ​​of a, b, c, d, e, and f can be obtained through data fitting, and then the relationship curve model can be obtained.

[0075] It is understandable that since the components such as resistors and capacitors of the device to be calibrated are fixed, there is a corresponding relationship between voltage and current. This relationship curve model can be further transformed into a mapping relationship between a single shooting parameter and grayscale value based on the correspondence between voltage and current, which is convenient for subsequent reverse solving.

[0076] In some possible implementations, the electronic device can determine multiple sets of experimental differences for any two sets of experimental parameter pairs based on the two sets of experimental parameter pairs and the experimental gray values ​​corresponding to the two sets of experimental parameter pairs; and perform fitting processing on each set of experimental differences according to a preset nonlinear function to obtain the relationship curve model corresponding to the device to be calibrated.

[0077] Each set of experimental differences includes experimental voltage difference, experimental current difference, and experimental grayscale difference. Experimental voltage difference = one set of experimental tube voltage values ​​minus the other set; experimental current difference = one set of experimental tube current values ​​minus the other set; experimental grayscale difference = the difference between the corresponding two sets of experimental grayscale values. The sign of the experimental grayscale difference indicates the direction of change: a positive value indicates an increase in grayscale, and a negative value indicates a decrease. In this case, the relationship curve model can characterize the mapping relationship between the adjustment amount of the shooting parameters (tube voltage adjustment amount and / or tube current adjustment amount) and the changes in grayscale difference, etc.

[0078] For example, the electronic device first retrieves all experimental parameter pairs and their corresponding experimental grayscale values, and presets a difference threshold, such as a voltage difference greater than 0.2kV and a current difference greater than 0.03mA. It retains experimental parameter pairs that meet the threshold to ensure the samples have statistical significance. From the selected experimental parameter pairs, any two groups are chosen, and the differences between the three parameters are calculated to obtain one set of experimental differences, and this process is repeated until multiple sets of experimental differences are obtained. Then, the experimental voltage difference, experimental current difference, and experimental grayscale difference in all experimental differences are normalized to eliminate dimensional differences. The processed experimental voltage difference and experimental current difference are then used as independent variables, and the corresponding experimental grayscale difference is used as the dependent variable. A multivariate nonlinear regression algorithm is used to fit the data, generating a bivariate polynomial function that describes the correspondence between the three. This function is the specific relationship curve model corresponding to the device to be calibrated. It should be noted that the form of the nonlinear function in this embodiment is not limited; for example, it can be a bivariate polynomial, an exponential function, or other nonlinear functions suitable for fitting shooting parameters and grayscale values.

[0079] For example, this relationship curve model can be represented as ,in, Indicates the difference in grayscale values. Indicates the amount of adjustment of the tube voltage. The value represents the tube current adjustment, and a, b, c, d, e, and f are coefficients. It can be understood that the values ​​of a, b, c, d, e, and f can be obtained through data fitting, thus yielding the relationship curve model. Similar to the aforementioned principle, this relationship curve model can be further transformed into a mapping relationship between the adjustment of a single shooting parameter and the grayscale difference, facilitating subsequent inverse calculations.

[0080] In some possible implementations, for any two pairs of experimental parameters, multiple sets of experimental differences are determined based on the two pairs of experimental parameters and the corresponding experimental gray values. The experimental voltage difference and experimental current difference in each set of experimental differences are used as labels for the initial model, and the experimental gray value difference is used as the input to the initial model. The initial model is then trained to obtain the computational model.

[0081] Each set of experimental differences includes experimental voltage difference, experimental current difference, and experimental grayscale difference. The initial model refers to a basic machine learning model that has not been trained with data, such as a neural network model or a gradient boosting tree model. This application embodiment does not limit the type of the initial model. The computational model is used to characterize the mapping relationship between the grayscale difference of the device to be corrected and the adjustment amount of the shooting parameters.

[0082] For example, the electronic device first normalizes the experimental voltage difference, experimental current difference, and experimental grayscale difference in all groups of experimental differences to eliminate the influence of dimensional differences on model training. At the same time, it divides all experimental grayscale differences and their corresponding experimental voltage difference and experimental current difference labels into training set and validation set. The training set data is input into the initial model, and training hyperparameters such as the number of iterations and learning rate are set. The backpropagation algorithm is used to continuously optimize the model parameters. During the training process, the electronic device calls the validation set data in real time to evaluate the model's fitting accuracy. When the error between the model's predicted tube voltage adjustment and tube current adjustment and the actual experimental voltage difference and experimental current difference is lower than the preset threshold, the training stops. The model obtained at this time is a computational model that can characterize the mapping relationship between the grayscale difference of the device to be corrected and the adjustment of the shooting parameters.

[0083] The imaging grayscale correction method for X-ray equipment provided in this application involves an electronic device acquiring the allowable operating extreme values ​​of voltage and current of the device to be corrected, generating multiple sets of experimental parameter pairs, and acquiring the corresponding experimental images captured by the device to be corrected according to the experimental parameters, as well as the experimental grayscale values ​​of the experimental images. Finally, a relationship curve model is obtained that can characterize the mapping relationship between the grayscale value of the device to be corrected and the imaging parameters such as tube voltage and / or tube current. This method ensures the accuracy and uniqueness of the relationship curve model through standardized parameter sampling, image acquisition, and grayscale calculation processes, reduces subjective errors caused by human intervention, provides accurate and reliable mathematical support for the subsequent real-time imaging grayscale correction of X-ray equipment, and effectively ensures the stability of the equipment's imaging grayscale.

[0084] Figure 3 A flowchart illustrating the imaging grayscale correction method for X-ray equipment provided in this application. Figure 2 ,like Figure 3 As shown, the method includes:

[0085] S201. Acquire the X-ray image captured by the device to be calibrated, and determine the actual grayscale value of the X-ray image.

[0086] For example, the device to be calibrated refers to an X-ray device that requires calibration of its imaging parameters to maintain stable image grayscale. An X-ray image is a digital grayscale image generated by the device to be calibrated after it emits X-rays driven by tube voltage and tube current, capturing images of the object under test or a standard imaging carrier. The actual grayscale value is a quantitative value reflecting the overall brightness and darkness of the X-ray image, used to measure the current imaging state of the device to be calibrated.

[0087] In one example, after establishing communication with the device to be calibrated, the electronic device receives the captured X-ray image uploaded by the device to be calibrated and obtains the actual grayscale value corresponding to the X-ray image according to a preset image grayscale calculation rule. The image grayscale calculation rule could be, for example, averaging the pixel values ​​of the image to obtain the average pixel value as the actual grayscale value; or it could be a weighted average based on the average grayscale value of a preset region of interest and the weight coefficient of each region of interest to obtain the actual grayscale value.

[0088] It should be noted that the embodiments of this application do not limit the method of determining the actual gray value, as long as the method of calculating the actual gray value is consistent with the method of calculating the experimental gray value when establishing the relationship curve model through experiments.

[0089] S202. Determine the grayscale difference between the actual grayscale value and the target grayscale value.

[0090] For example, the target gray value refers to a pre-set reference gray value that conforms to the imaging quality standard, and is the target value to which the imaging gray value of the device to be calibrated needs to be corrected. The gray value difference refers to the numerical difference between the actual gray value and the target gray value, and is used to quantify the magnitude and direction of the deviation between the current imaging gray value of the device to be calibrated and the standard gray value.

[0091] In one example, the electronic device retrieves the preset stored target grayscale value, and performs a difference calculation between the determined actual grayscale value and the target grayscale value to obtain the grayscale difference value.

[0092] S203. Determine the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model corresponding to the device to be calibrated.

[0093] The relationship curve model represents the mapping relationship between the shooting parameters of the device to be corrected and the image grayscale. For example, it can be the relationship curve model obtained by fitting experimental data as mentioned above.

[0094] The shooting parameter adjustment amount refers to the amount of change in tube voltage and / or tube current required to eliminate grayscale difference and make the image grayscale reach the target grayscale value. In other words, the shooting parameter adjustment amount includes the tube voltage adjustment amount and / or tube current adjustment amount.

[0095] In one example, the electronic device first retrieves the relationship curve model corresponding to the device to be calibrated, substitutes the grayscale difference into the relationship curve model, and obtains the shooting parameter adjustment amount by reverse solving.

[0096] For example, in the relation curve model represented as described above It can be understood that there is a corresponding relationship between the tube voltage adjustment and the tube current adjustment, and thus the formula can be further transformed into a grayscale difference value. With tube voltage adjustment The correspondence between them, or the grayscale difference With tube current adjustment By understanding the correspondence between them, we can substitute the known grayscale difference values ​​to solve for the corresponding tube voltage adjustment and / or tube current adjustment, thus obtaining the shooting parameter adjustment.

[0097] The relationship curve model is represented as described above. First, the actual shooting parameters of the X-ray image taken by the device to be calibrated are obtained. These actual shooting parameters and the grayscale difference are then substituted into the relationship curve model, and the adjustment amount of the shooting parameters is obtained through a reverse algorithm. For example, the actual grayscale value... Actual shooting parameters (actual tube current) Actual tube voltage Substituting the relationship between tube voltage and tube current into the curve model yields... and The correspondence between them can then be obtained. and The correspondence between them is determined by substituting the grayscale difference. The tube current adjustment can be obtained by solving. This allows us to obtain the tube voltage adjustment amount.

[0098] In some embodiments, when the electronic device utilizes the computational model trained in the foregoing embodiments, the electronic device can input the obtained grayscale difference into the computational model and output the tube voltage adjustment amount and / or tube current adjustment amount predicted by the model as the shooting parameter adjustment amount.

[0099] S204. Adjust the tube voltage and / or tube current of the device to be calibrated according to the shooting parameter adjustment amount to correct the imaging grayscale of the device to be calibrated to the target grayscale value.

[0100] In one example, the electronic device adds the actual shooting parameters of the device to be calibrated to obtain the calibrated shooting parameters, and then adjusts the transistor voltage and / or transistor current of the device to be calibrated based on the calibrated shooting parameters. It should be noted that in practical applications, it is possible to determine whether to adjust the transistor voltage or the transistor current of the device to be calibrated based on actual needs. For example, the transistor voltage can be kept constant, and the transistor current can be adjusted by changing the values ​​of components such as resistors and capacitors.

[0101] Optionally, the electronic device can verify whether the calibrated shooting parameters are within the allowable operating extreme range of the device to be calibrated; if they are within the range, it can send a parameter update command to the device to be calibrated and control it to load the calibrated shooting parameters.

[0102] The X-ray equipment imaging grayscale correction method provided in this application involves an electronic device acquiring an X-ray image captured by the device to be corrected and determining the actual grayscale value. The grayscale difference between the actual and target grayscale values ​​is calculated, and then the adjustment amount of the imaging parameters corresponding to the grayscale difference is calculated based on a relationship curve model. The tube voltage and / or tube current of the device to be corrected are adjusted according to the adjustment amount. This method improves the accuracy and efficiency of X-ray equipment imaging grayscale correction by utilizing quantified grayscale deviation analysis and reliable relationship curve model calculations, achieving automated grayscale correction of X-ray equipment imaging. This reduces reliance on manual experience and ensures the consistency and accuracy of correction precision. Simultaneously, it can respond in real-time to grayscale drift caused by equipment performance degradation or environmental changes, accurately maintaining the stability of the imaging grayscale. This reduces misjudgments and missed detections that may occur with subsequent detection algorithms that rely on fixed grayscale thresholds, effectively improving the long-term reliability and detection accuracy of the X-ray detection system.

[0103] Figure 4 A flowchart illustrating the imaging grayscale correction method for X-ray equipment provided in this application. Figure 3 ,like Figure 4 As shown, in this embodiment... Figure 3 Based on the embodiments, the imaging grayscale correction method of X-ray equipment is described in detail. The method includes:

[0104] S301. Acquire the X-ray image captured by the device to be calibrated.

[0105] It should be noted that this step is similar to the aforementioned step S201, and will not be repeated here.

[0106] S302. On an X-ray image, using the center point of the X-ray image as a reference, divide the image into multiple regions of interest along the intersection of the two diagonals of the X-ray image.

[0107] The center point of an X-ray image refers to the geometric center of the X-ray image in both the horizontal and vertical directions.

[0108] A path where two diagonals intersect refers to a path that extends along the diagonal of an image, with the center point as the intersection. For example... Figure 2 As shown, nine regions of interest were divided along the intersection of the two diagonals of the X-ray image.

[0109] Each region of interest (ROI) has a weighting coefficient, which is set based on its distance from the center point of the X-ray image. In other words, the weighting coefficient is a quantization coefficient assigned based on the distance between each ROI and the center point. The closer the distance, the higher the weight, which is used to compensate for the "halo effect" in X-ray imaging.

[0110] S303. For each region of interest, average all pixel values ​​within that region of interest to obtain the average grayscale value of that region of interest.

[0111] For example, a pixel value refers to the quantized brightness value of a single pixel in an X-ray image. The average gray value is the arithmetic mean of all pixel values ​​within a single region of interest, which characterizes the brightness of that region of interest.

[0112] In one example, the electronic device iterates through all pixels in each region of interest, reads the grayscale value of each pixel, and calculates the average grayscale value corresponding to each region of interest by summing the values ​​and dividing the sum by the total number of pixels in that region of interest.

[0113] S304. Based on the weight coefficient of the region of interest, perform weighted averaging on the average gray value of the region of interest to obtain the actual gray value of the X-ray image.

[0114] For example, weighted average processing refers to a calculation method that combines the weight coefficients of each ROI, sums the average gray values ​​in a weighted manner, and then normalizes them. The actual gray value refers to a quantitative value that can accurately represent the brightness of the entire X-ray image, which is different from the unweighted global average gray value and has higher accuracy.

[0115] For example, electronic devices can be configured according to the formula. The actual grayscale value of the X-ray image is calculated. Among them, This represents the actual grayscale value of the X-ray image; This represents the average gray value of the k-th region of interest; represents the weight coefficient of the k-th region of interest; n represents the total number of regions of interest.

[0116] S305. Determine the grayscale difference between the actual grayscale value and the target grayscale value.

[0117] It should be noted that this step is similar to the aforementioned step S202, and will not be repeated here.

[0118] S306. Determine the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model corresponding to the device to be calibrated.

[0119] It should be noted that this step is similar to the aforementioned step S203, and will not be repeated here.

[0120] S307. Adjust the tube voltage and / or tube current of the device to be calibrated according to the shooting parameter adjustment amount to correct the imaging grayscale of the device to be calibrated to the target grayscale value.

[0121] It should be noted that this step is similar to the aforementioned step S204, and will not be repeated here.

[0122] The imaging grayscale correction method for X-ray equipment provided in this application involves an electronic device acquiring an X-ray image captured by the device to be corrected, dividing the X-ray image into weighted regions of interest (ROIs) along a diagonal intersection path with the center point as a reference, calculating the average grayscale value of a single ROI and then weighting the average to obtain the actual grayscale value of the X-ray image, calculating the grayscale difference between the actual grayscale value and the target grayscale value, retrieving the relationship curve model specific to the device to be corrected, solving for the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model, and adjusting the tube voltage and / or tube current of the device to be corrected according to the shooting parameter adjustment amount, ultimately restoring the imaging grayscale of the device to the target grayscale value. This method accurately compensates for the halo effect in X-ray imaging through weighted ROI design, improving the accuracy of grayscale calculation. It also achieves quantitative derivation of parameter adjustment based on the device-specific relationship curve model, reducing reliance on human experience and ensuring the consistency and accuracy of correction. At the same time, it can respond in real time to grayscale drift caused by device performance degradation or environmental changes, effectively reducing misjudgments and missed detections caused by grayscale instability in subsequent detection algorithms. This significantly improves the long-term reliability, detection accuracy, and automation adaptability of the X-ray detection system.

[0123] In some embodiments, for the device to be calibrated, a target grayscale value can be set according to the detection task to be performed. In other words, the target grayscale value is the preset imaging grayscale value for the detection task to be performed. During the daily use of the device to be calibrated, after each image is captured, the actual grayscale value of the current X-ray image is automatically calculated according to the steps described in the aforementioned embodiments. Furthermore, the grayscale difference value is calculated. ,Will Substituting into the preset relationship curve model G=f(U, In I), the shooting parameter adjustment amount is calculated in reverse, that is, how much tube current adjustment is required ( ), tube voltage adjustment ( Only then can this grayscale difference be compensated. This restores the image grayscale to the target grayscale value. Finally, the electronic device will set the initial shooting parameters ( , The corrected shooting parameters are as follows: , ).

[0124] Through this method, based on a closed-loop feedback process of real-time monitoring of the actual grayscale value of each shot, calculating the grayscale difference, back-calculating the shooting parameter adjustment amount based on the preset relationship curve model, and correcting the initial shooting parameters, the device to be corrected can automatically and in real time offset the grayscale drift caused by its own performance degradation, achieve self-stabilization of imaging grayscale, and ensure long-term consistency of image quality.

[0125] In some embodiments, the image grayscale value of the calibrated device and the shooting parameters corresponding to the image grayscale value of the calibrated device are obtained; the shooting parameters are used as the initial shooting parameters of the device to be calibrated, and the image grayscale value of the calibrated device is used as the target grayscale value of the device to be calibrated. An X-ray image of the device to be calibrated is obtained according to the initial shooting parameters, and then the image grayscale of the device to be calibrated can be calibrated according to the steps described in the foregoing embodiments. The calibrated device can serve as a reference device for grayscale consistency calibration of multiple X-ray devices.

[0126] This method selects a high-performance, finely tuned calibrated device, extracts its optimal imaging parameters and corresponding imaging grayscale values ​​as the gold calibration benchmark, uses these optimal imaging parameters as the initial imaging parameters of the device to be calibrated, and uses the imaging grayscale values ​​as the target grayscale values ​​of the device to be calibrated. Simultaneously, the device to be calibrated is controlled to capture X-ray images of the same standard carrier as the calibrated device to eliminate interference from differences in the detected object on grayscale contrast. This provides accurate and reliable basic data for subsequent grayscale difference calculation and imaging parameter adjustment. Ultimately, this achieves the goal of two different devices outputting images with highly consistent grayscale levels when photographing the same object. This greatly simplifies the multi-device deployment process, reduces system deployment costs, ensures consistent imaging effects at different inspection sites or production lines, and significantly improves the cross-device compatibility and uniformity of inspection results of the inspection system.

[0127] Figure 5 This is a schematic diagram of the imaging grayscale correction device for the X-ray equipment provided in this application, as shown below. Figure 5 As shown, the imaging grayscale correction device 400 of the X-ray equipment provided in this embodiment includes:

[0128] The acquisition module 401 is used to acquire the X-ray image captured by the device to be calibrated and determine the actual grayscale value of the X-ray image;

[0129] The first determining module 402 is used to determine the grayscale difference between the actual grayscale value and the target grayscale value;

[0130] The second determining module 403 is used to determine the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model corresponding to the device to be calibrated; wherein, the relationship curve model characterizes the mapping relationship between the shooting parameters of the device to be calibrated and the imaging grayscale, and the shooting parameter adjustment amount includes tube voltage adjustment amount and / or tube current adjustment amount;

[0131] The correction module 404 is used to adjust the tube voltage and / or tube current of the device to be corrected according to the shooting parameter adjustment amount, so as to correct the imaging grayscale of the device to be corrected to the target grayscale value.

[0132] In one possible implementation, the first determining module 402 is configured to:

[0133] On an X-ray image, multiple regions of interest are divided along the intersection of the two diagonals of the X-ray image, with the center point of the X-ray image as the reference. Each region of interest has a weight coefficient, which is set based on the distance from the center point of the X-ray image.

[0134] For each region of interest, the average value of all pixels within that region of interest is obtained by averaging the values ​​of the pixels in that region of interest.

[0135] Based on the weight coefficients of the region of interest, the average gray value of the region of interest is weighted and averaged to obtain the actual gray value of the X-ray image.

[0136] In one possible implementation, the second determining module 403 is configured to:

[0137] Based on the grayscale difference, the relationship curve model, and the actual imaging parameters of the X-ray images captured by the device to be calibrated, the adjustment amount of the imaging parameters is obtained; or...

[0138] Substituting the grayscale difference into the relationship curve model, the adjustment amount of the shooting parameters is obtained by solving.

[0139] In one possible implementation, the device further includes a building module for:

[0140] Obtain the allowable operating extreme values ​​of the tube voltage and tube current of the device to be calibrated;

[0141] Based on the allowable operating extreme values ​​of the tube voltage and the tube current, multiple sets of experimental parameter pairs are obtained; each set of experimental parameter pairs includes the experimental tube voltage value and the experimental tube current value.

[0142] For each pair of experimental parameters, acquire the experimental images taken by the device to be calibrated according to that pair of experimental parameters, determine the experimental grayscale value of the experimental images, and obtain the experimental grayscale value corresponding to that pair of experimental parameters.

[0143] Based on each set of experimental parameter pairs and the corresponding experimental grayscale values, the relationship curve model corresponding to the device to be calibrated is determined.

[0144] In one possible implementation, the building module is used for:

[0145] For any two pairs of experimental parameters, multiple sets of experimental differences are determined based on the two pairs of experimental parameters and the corresponding experimental grayscale values. Each set of experimental differences includes the experimental voltage difference, the experimental current difference, and the experimental grayscale difference.

[0146] According to the preset nonlinear function, the difference between each group of experiments is fitted to obtain the relationship curve model corresponding to the device to be calibrated.

[0147] In one possible implementation, the acquisition module 401 is used for:

[0148] Determine the target grayscale value of the device to be calibrated; wherein, the target grayscale value is the imaging grayscale value of the calibrated device or the preset imaging grayscale value of the detection task to be performed.

[0149] The imaging grayscale correction device for X-ray equipment provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0150] Figure 6 A schematic diagram of the structure of the electronic device provided in this application. Figure 6 As shown, the electronic device 500 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 500 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus.

[0151] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0152] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0153] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0154] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0155] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0156] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0157] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0158] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0159] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0160] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0161] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0162] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0163] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0165] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for grayscale correction of X-ray equipment, characterized in that, include: Acquire the X-ray image captured by the device to be calibrated, and determine the actual grayscale value of the X-ray image; Determine the grayscale difference between the actual grayscale value and the target grayscale value; The adjustment amount of the shooting parameters corresponding to the gray difference is determined based on the relationship curve model corresponding to the device to be calibrated; wherein, the relationship curve model characterizes the mapping relationship between the shooting parameters of the device to be calibrated and the imaging gray level, and the shooting parameter adjustment amount includes tube voltage adjustment amount and / or tube current adjustment amount; Adjust the tube voltage and / or tube current of the device to be calibrated according to the shooting parameter adjustment amount, so as to correct the imaging grayscale of the device to be calibrated to the target grayscale value.

2. The method according to claim 1, characterized in that, Determining the actual grayscale value of the X-ray image includes: On the X-ray image, taking the center point of the X-ray image as a reference, multiple regions of interest are divided along the intersection of the two diagonals of the X-ray image; wherein, each region of interest has a weight coefficient, which is set based on the distance from the center point of the X-ray image; For each region of interest, the average value of all pixels within that region of interest is obtained by averaging the values ​​of the region of interest. Based on the weight coefficients of the region of interest, a weighted average is performed on the average gray value of the region of interest to obtain the actual gray value of the X-ray image.

3. The method according to claim 1, characterized in that, The step of determining the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model corresponding to the device to be corrected includes: Based on the grayscale difference, the relationship curve model, and the actual shooting parameters when the X-ray image was captured by the device to be corrected, the adjustment amount of the shooting parameters is calculated; or... Substituting the grayscale difference into the relationship curve model, the adjustment amount of the shooting parameters is obtained by solving.

4. The method according to claim 1, characterized in that, The method further includes: Obtain the allowable operating extreme values ​​of the tube voltage and tube current of the device to be calibrated; Based on the allowable operating extreme values ​​of the tube voltage and the tube current, multiple sets of experimental parameter pairs are obtained; each set of experimental parameter pairs includes an experimental tube voltage value and an experimental tube current value. For each pair of experimental parameters, the experimental image captured by the device to be calibrated according to the pair of experimental parameters is obtained, and the experimental grayscale value of the experimental image is determined to obtain the experimental grayscale value corresponding to the pair of experimental parameters. Based on each set of experimental parameter pairs and the corresponding experimental grayscale values, the relationship curve model corresponding to the device to be calibrated is determined.

5. The method according to claim 4, characterized in that, The step of determining the relationship curve model corresponding to the device to be calibrated based on each set of experimental parameter pairs and the corresponding experimental grayscale values ​​includes: For any two pairs of experimental parameters, multiple sets of experimental differences are determined based on the two pairs of experimental parameters and the corresponding experimental grayscale values. Each set of experimental differences includes the experimental voltage difference, the experimental current difference, and the experimental grayscale difference. According to the preset nonlinear function, the difference between each group of experiments is fitted to obtain the relationship curve model corresponding to the device to be calibrated.

6. The method according to claim 1, characterized in that, The method further includes: Determine the target grayscale value of the device to be calibrated; wherein the target grayscale value is the imaging grayscale value of the calibrated device or the preset imaging grayscale value of the detection task to be performed.

7. An imaging grayscale correction device for an X-ray equipment, characterized in that, include: The acquisition module is used to acquire the X-ray image captured by the device to be calibrated and determine the actual grayscale value of the X-ray image; The first determining module is used to determine the grayscale difference between the actual grayscale value and the target grayscale value; The second determining module is used to determine the shooting parameter adjustment amount corresponding to the grayscale difference based on the relationship curve model corresponding to the device to be calibrated; wherein, the relationship curve model characterizes the mapping relationship between the shooting parameters of the device to be calibrated and the imaging grayscale, and the shooting parameter adjustment amount includes tube voltage adjustment amount and / or tube current adjustment amount; The correction module is used to adjust the tube voltage and / or tube current of the device to be corrected according to the shooting parameter adjustment amount, so as to correct the imaging grayscale of the device to be corrected to the target grayscale value.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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