Noise calibration method, device and equipment for multicolor X-ray surface detector and medium

Abstract

The embodiment of the invention discloses a noise calibration method and device for a multicolor X-ray surface detector, equipment and a medium. The method comprises the following steps: emitting multicolor X-rays with the same spectrum to the same single crystal at the same exposure time and incident angle so as to decompose the diffracted multicolor X-rays into multiple beams of monochromatic X-rays to be emitted to a surface detector, and acquiring a Laue diffraction image formed on the surface detector; the process is repeated for not less than three times; for a local image of the same target diffraction spot in each diffraction image, obtaining a gray value of each pixel point in the local image of the target diffraction spot; performing linear regression analysis on the mean value and variance of the gray value of each pixel of the target diffraction spot in multiple tests to obtain a gray gain and a first intercept; and performing linear regression analysis on the background noise of the surface detector according to the gray gains and the first intercept of all the diffraction spots, and calibrating the background noise according to an analysis result. After the parameters of the background noise are obtained, the parameters of the quantum fluctuation noise can be further decomposed from the overall noise, and decoupling calibration of the mixed noise in the prior art is achieved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of X-ray area detector noise analysis, and particularly relate to a noise calibration method, device, equipment and medium for a polychromatic X-ray area detector. BACKGROUND

[0002] X-ray area detectors are widely used in the fields of defect detection, security inspection, medical treatment and scientific research. The noise of the area detector can affect the detection accuracy and imaging quality. In order to obtain the noise parameters of the X-ray area detector, repetitive tests are used to obtain the fluctuation of the pixel gray value of the area detector.

[0003] The X-ray wavelength is one of the important factors affecting the noise level of the area detector. The noise calibration test of the area detector uses either monochromatic X-rays or polychromatic X-rays. When monochromatic X-rays are used, only the noise parameters caused by X-rays of a single wavelength can be obtained, and the noise parameters are not complete. When polychromatic X-rays are used, the obtained are the mixed noise parameters caused by X-rays of multiple wavelengths, and the mixed noise cannot be disassembled and used. SUMMARY

[0004] Embodiments of the present application provide a noise calibration method, device, equipment and medium for a polychromatic X-ray area detector, to realize disassembly and calibration of mixed noise in polychromatic X-rays.

[0005] In a first aspect, embodiments of the present application provide a noise calibration method for a polychromatic X-ray area detector, the method comprising:

[0006] The same polychromatic X-rays of the same spectrum are emitted to the same single crystal at the same exposure time and incident angle, so as to decompose the diffracted polychromatic X-rays into multiple beams of monochromatic X-rays and form Laue diffraction images on the area detector; the above process is repeated not less than three times;

[0007] For a local image of a same target diffraction spot in each diffraction image, the gray value of each pixel point in the local image of the target diffraction spot is obtained;

[0008] The mean value and variance of the gray value of each pixel of the target diffraction spot in multiple tests are subjected to linear regression analysis, to obtain a gray gain and a first intercept;

[0009] The background noise of the area detector is subjected to linear regression analysis according to the gray gain and the first intercept of all diffraction spots, and the background noise and the quantum fluctuation noise of each diffraction spot corresponding to the monochromatic X-rays are calibrated according to the analysis result.

[0010] In a second aspect, embodiments of the present application provide a noise calibration device for a polychromatic X-ray area detector, the device comprising:

[0011] an image acquisition module, configured to emit polychromatic X-rays of the same spectrum to the same single crystal, to decompose the polychromatic X-rays after diffraction into a plurality of beams of monochromatic X-rays to the area detector, and to acquire a Laue diffraction image formed on the area detector; the above process is repeated no less than three times;

[0012] a gray value acquisition module, configured to acquire a gray value of each pixel point in a local image of a same target diffraction spot in each diffraction image;

[0013] a linear regression module, configured to perform linear regression analysis on a mean value and a variance of the gray value of each pixel of the target diffraction spot in multiple tests, to obtain a gray gain and a first intercept;

[0014] a linear regression calibration module, configured to perform linear regression analysis on background noise of the area detector according to the gray gain and the first intercept of all diffraction spots, and to calibrate the background noise and quantum fluctuation noise of monochromatic X-rays corresponding to each diffraction spot according to an analysis result.

[0015] In a third aspect, an embodiment of the present application further provides a noise calibration device of a polychromatic X-ray area detector, the noise calibration device of the polychromatic X-ray area detector comprising: one or more processors and a storage device;

[0016] the storage device is configured to store one or more programs;

[0017] when the one or more programs are executed by the one or more processors, the one or more processors implement the noise calibration method of the polychromatic X-ray area detector provided in any embodiment of the present application.

[0018] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the noise calibration method of the polychromatic X-ray area detector provided in any embodiment of the present application.

[0019] The technical solution of the embodiment of the present application is to emit polychromatic X-rays of the same spectrum to the same single crystal with the same exposure time and incident angle. The diffracted polychromatic X-rays are decomposed into multiple beams of monochromatic X-rays and directed to a surface detector, forming a Laue diffraction image on the surface detector. The above process is repeated at least three times. For the local image of the same target diffraction spot in each diffraction image, the grayscale value of each pixel in the local image of the target diffraction spot is obtained. The mean and variance of the grayscale value of each pixel of the target diffraction spot in multiple experiments are linearly regressed to obtain the grayscale gain and first intercept. Based on the grayscale gain and first intercept of all diffraction spots, a linear regression analysis is performed on the background noise of the surface detector, and the background noise is calibrated according to the analysis results. After obtaining the background noise parameters, the variance of the background noise is subtracted from the grayscale variance of each pixel to obtain the variance of the quantum fluctuation noise of the pixel. Since the expectation of quantum fluctuation is the signal itself, the expectation of quantum fluctuation noise is zero. Based on this, through two rounds of linear regression analysis, the overall background noise is calibrated, and the quantum fluctuation noise of each pixel is calibrated, so as to realize the decomposition and calibration of the mixed noise in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of a noise calibration method for a polychromatic X-ray area detector provided in Example 1 of the present application;

[0021] Figure 2 A Laue diffraction diagram provided in Example 1 of the present application;

[0022] Figure 3 A schematic diagram of a diffraction image and a local diffraction spot provided in Example 1 of the present application;

[0023] Figure 4 A schematic diagram of a linear regression analysis of grayscale fluctuations of a single diffraction spot provided in Example 1 of the present application;

[0024] Figure 5 A schematic diagram of a linear regression analysis of the grayscale gains and first intercepts of all diffraction spots provided in Example 1 of the present application;

[0025] Figure 6 A schematic structural diagram of a noise calibration device for a polychromatic X-ray area detector provided in Example 2 of the present application;

[0026] Figure 7 This is a structural diagram of an electronic device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0027] The application will be described in further detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the application and not to limit the application. In addition, it should be noted that, for the sake of description, only the parts related to the application are shown in the drawings, not all the structures.

[0028] Embodiment one

[0029] Figure 1 A flowchart of a noise calibration method of a polychromatic X-ray area detector provided for embodiment one of the application. It should be noted that the method can be run on an electronic device equipped with noise calibration. As shown in Figure 1 , the method comprises:

[0030] Step 101, emit polychromatic X-rays of the same spectrum to the same single crystal at the same exposure time and incident angle, to decompose the diffracted polychromatic X-rays into multiple beams of monochromatic X-rays to the area detector, and acquire a Laue diffraction image formed on the area detector; the above process is repeated no less than three times.

[0031] In this step, the polychromatic X-rays are mainly diffracted into multiple monochromatic X-rays by Laue diffraction. It should be noted that the diffraction direction of each monochromatic X-ray can be determined by the Bragg equation, so as to determine which monochromatic X-ray each diffraction spot in the diffraction image obtained by diffraction to the area detector corresponds to.

[0032] For details, please refer to Figure 2 , Figure 2 A Laue diffraction diagram provided for embodiment one of the application. As shown in Figure 2 , the incident polychromatic X-rays are diffracted by the single crystal to obtain multiple monochromatic X-rays (for the convenience of demonstration, only three are drawn, i.e. monochromatic X-ray A, monochromatic X-ray B, and monochromatic X-ray C). The multiple monochromatic X-rays are projected onto the area detector, and after a preset exposure time, a diffraction image can be acquired.

[0033] In order to improve the accuracy of noise calibration in the embodiment, the number of times of collecting diffraction images by Laue diffraction described above can be a preset number, which in a specific example can be 100 times, 150 times, 200 times, etc.

[0034] Taking 100 times as an example, 100 diffraction images can be acquired after this step. Since the same incident angle and exposure time are used for each diffraction, the positions of the diffraction spots in the obtained diffraction images are basically consistent, and are related to the diffraction direction determined by the aforementioned Bragg equation.

[0035] Moreover, each diffraction spot in the diffraction image corresponds to a diffracted monochromatic X-ray, and the diffraction direction of the monochromatic X-ray is determined according to the diffraction direction of the polychromatic X-ray Figure 2 For example, three monochromatic X-rays are diffracted, and three diffraction spots exist in the corresponding diffraction image. Based on the determined diffraction direction, it can be determined which monochromatic X-ray corresponds to the diffraction spot in the diffraction image. For details, refer to the related art, which will not be described here.

[0036] Step 102, for the local image of the same target diffraction spot in each diffraction image, the gray value of each pixel point in the local image of the target diffraction spot is obtained.

[0037] Since the polychromatic X-ray is emitted at the same incident angle, the distribution of the diffraction spot in each diffraction image is consistent. Once the pixel position of a target diffraction spot in a diffraction image is determined, the pixel position of the target diffraction spot in other diffraction images can be determined.

[0038] Therefore, the local image of the diffraction spot in any diffraction image can be extracted. The specific image extraction method can be analyzed according to the gray difference between the diffraction spot and the background color in the diffraction image. For example, first locate the different pixel points with the maximum gray value in the diffraction image, and then expand outward from the pixel point with the maximum gray value until the pixel gray value no longer changes, and the diffraction spot can be extracted.

[0039] For details, refer to Figure 3 , Figure 3 A diffraction image and a local diffraction spot diagram provided for Embodiment One of the present application.

[0040] As shown in Figure 3 , the diffraction image includes a plurality of diffraction spots, and one of the diffraction spots is enlarged, which includes a plurality of pixel points, Figure 3 The diffraction spot boxed on the right side of the image is the image of a diffraction spot.

[0041] It should be noted that due to the single wavelength of the photons of a single diffraction spot, the influence of the wavelength of the photons can be temporarily ignored.

[0042] After obtaining the image of the diffraction spot, the gray values of the pixel points in the image can be extracted for subsequent analysis.

[0043] Step 103, linear regression analysis is performed on the mean and variance of the gray value of each pixel of the target diffraction spot in multiple tests to obtain the gray gain and the first intercept.

[0044] Specifically, the gray scale variance value and the average gray scale value can be determined according to the gray scale values of the pixel points in the image of the target diffraction spot; then the average gray scale value is taken as the abscissa and the gray scale variance value is taken as the ordinate, linear regression analysis is performed according to the gray scale variance value and the average gray scale value of each pixel point, and a first linear slope and a first intercept are obtained; and the first linear slope is determined as the gray scale gain of the target diffraction spot.

[0045] It should be noted that, due to the uncertainty principle of quantum mechanics, the detection of photons by the detector is a random process, and the number of photons detected by the detector within the exposure time conforms to a Poisson distribution. The Poisson distribution has the characteristics that the expected value and the variance are equal. If the expected value of the number of photons detected within the exposure time is , then the variance is also The increase in the gray scale value caused by one photon is called gain, denoted by γ, so the expected increase in the gray scale value within the exposure time is , and the variance is

[0046] In addition, according to the central limit theorem, after a sufficient number of exposure times, the Poisson distribution of quantum fluctuations can be approximated by a Gaussian distribution with an expected value of and a variance of . Again, due to the superposition of Gaussian distributions, after the quantum fluctuations are superimposed with the background noise of the detector, the expected value and the variance of the gray scale are and

[0047] , respectively. The background noise of the detector is caused by the Brownian motion of the internal circuit of the detector, the analog-to-digital conversion of the detector, and other factors, and exists even without photons entering, conforming to a Gaussian distribution and can be described by two parameters: the expected value of the background gray scale and the variance of the gray scale σ 2 .

[0048] The coordinate graph obtained by taking the average gray scale value as the abscissa and the gray scale variance value as the ordinate can be referred to as Figure 4 , Figure 4 , which is a linear regression analysis diagram of the gray scale fluctuation of a single diffraction spot provided by Embodiment One of the present application.

[0049] The average gray scale value and the variance of each pixel point are filled into the coordinate graph, and the average gray scale value is taken as the abscissa and the gray scale variance is taken as the ordinate. After linear regression, the relevant parameters of the regression are obtained, i.e. the slope and the y-axis intercept, and the slope is exactly the gray scale gain γ. It should be noted that the y-axis intercept (i.e. the first intercept) can be expressed by the expression . The meanings of the parameters therein can be referred to the foregoing steps and will not be described herein again.

[0050] After analyzing all diffraction spots by the above steps, the gray scale gain and the first intercept of each diffraction spot can be obtained.

[0051] In step 104, the background noise of the area detector is linearly regressed according to the gray scale gain and the first intercept of all diffraction spots, and the background noise and the quantum fluctuation noise of monochromatic X-ray corresponding to each diffraction spot are calibrated according to the analysis result.

[0052] In this step, the gray scale gain of each diffraction spot is taken as the horizontal coordinate, and the first intercept of each diffraction spot is taken as the vertical coordinate. Linear regression analysis is performed according to the gray scale gain and the first intercept of each diffraction spot to obtain the second linear slope and the second intercept. The second linear slope and the second intercept are determined as the analysis result.

[0053] Since the first intercept can be expressed as The gray scale gain can be expressed as γ. From the two expressions, the slope in the linear relationship between them is The y-axis intercept is σ 2 , which is the opposite of the background gray scale expectation of the aforementioned background noise and the variance σ 2 of the gray scale, respectively.

[0054] For details, please refer to Figure 5 , Figure 5 a linear regression diagram of the gray scale gain and the first intercept of all diffraction spots provided by embodiment one of the present application.

[0055] Based on the above characteristics, when calibrating the background noise, the distribution of the background noise can be constructed as a Gaussian distribution. The background gray scale expectation of the background noise Gaussian distribution is determined according to the second linear slope, and the second intercept is determined as the gray scale variance of the background noise Gaussian distribution. The background gray scale expectation and the gray scale variance are calibrated as the noise parameters of the background noise.

[0056] That is, the opposite of the slope obtained by the above linear regression analysis is the background gray scale expectation, and the second intercept obtained is the gray scale variance.

[0057] After obtaining the parameters of the background noise, the gray scale variance of each pixel is subtracted from the variance of the background noise to obtain the variance of the quantum fluctuation noise of the pixel. Since the expectation of the quantum fluctuation is the signal itself, the expectation of the quantum fluctuation noise is zero.

[0058] Specifically, the first linear slope (gray scale gain) can be taken as the abscissa, and the first intercept can be taken as the ordinate to perform linear regression analysis, to obtain the second linear slope and the second intercept; then the reciprocal of the second linear slope is determined as the expectation of the background noise, and the second intercept is determined as the variance of the background noise; finally, the gray scale variance of each pixel is subtracted by the variance of the background noise to determine the variance of the quantum fluctuation noise, and the expectation of the quantum fluctuation noise is determined as zero.

[0059] In the embodiment, the same monochromatic X-rays of the same spectrum are emitted to the same single crystal at the same exposure time and at the same incident angle, the diffracted polychromatic X-rays are decomposed into multiple beams of monochromatic X-rays to irradiate the area detector, and a Laue diffraction image is formed on the area detector; the above process is repeated for not less than three times; the gray scale value of each pixel point in the local image of the target diffraction spot is obtained for the local image of the same target diffraction spot in each diffraction image; the mean value and the variance of the gray scale value of the target diffraction spot in multiple tests are subjected to linear regression analysis to obtain the gray scale gain and the first intercept; the background noise of the area detector is subjected to linear regression analysis according to the gray scale gain and the first intercept of all diffraction spots, and the background noise is calibrated according to the analysis result; after the parameters of the background noise are obtained, the gray scale variance of each pixel is subtracted by the variance of the background noise to obtain the variance of the quantum fluctuation noise of the pixel; since the expectation of the quantum fluctuation is the signal itself, the expectation of the quantum fluctuation noise is zero. Based on this, the two-round linear regression analysis calibrates the overall background noise and the quantum fluctuation noise of each pixel, so that the mixed noise in the prior art is disassembled and calibrated.

[0060] Embodiment Two

[0061] Figure 6 A structural schematic diagram of a noise calibration device of a polychromatic X-ray area detector is provided for Embodiment Two of the present application. The noise calibration device of the polychromatic X-ray area detector provided by the embodiments of the present application can execute the noise calibration method of the polychromatic X-ray area detector provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method. The device can be realized in the software and / or hardware mode, such as Figure 6 As shown in the figure, the noise calibration device of the polychromatic X-ray area detector specifically comprises: an image acquisition module 601, a gray scale value acquisition module 602, a linear regression module 603, and a linear regression calibration module 604.

[0062] The image acquisition module is configured to emit the same polychromatic X-rays of the same spectrum to the same single crystal to decompose the diffracted polychromatic X-rays into multiple beams of monochromatic X-rays to irradiate the area detector, and acquire the Laue diffraction image formed on the area detector; the above process is repeated for not less than three times;

[0063] The gray value acquisition module is configured to acquire a gray value of each pixel point in a local image of a target diffraction spot for each diffraction image.

[0064] The linear regression module is configured to perform linear regression analysis on the mean value and variance of the gray value of each pixel of the target diffraction spot in multiple tests to obtain a gray gain and a first intercept.

[0065] The linear regression calibration module is configured to perform linear regression analysis on the background noise of the area detector according to the gray gain and the first intercept of all diffraction spots, and calibrate the background noise and the quantum fluctuation noise of the monochromatic X-ray corresponding to each diffraction spot according to the analysis result.

[0066] Further, the linear regression module comprises:

[0067] The first determination unit is configured to determine a gray variance value and a mean gray value according to the gray value of each pixel point in the image of the target diffraction spot.

[0068] The regression analysis unit is configured to perform linear regression analysis on the gray variance value and the mean gray value of each pixel point with the mean gray value as the abscissa and the gray variance value as the ordinate to obtain a first linear slope and a first intercept.

[0069] The second determination unit is configured to determine the first linear slope as the gray gain of the target diffraction spot.

[0070] Further, the linear regression calibration module comprises:

[0071] The construction unit is configured to construct the distribution of the background noise as a Gaussian distribution.

[0072] The third determination unit is configured to determine a background gray expectation of the background noise Gaussian distribution according to the second linear slope, and determine the second intercept as a gray variance of the background noise Gaussian distribution.

[0073] The background noise calibration unit is configured to calibrate the background gray expectation and the gray variance as noise parameters of the background noise.

[0074] The quantum fluctuation noise variance determination unit is configured to determine, for any diffraction spot, a variance of the quantum fluctuation noise of the monochromatic X-ray corresponding to the diffraction spot by using the gray variance of each pixel of the diffraction spot and the gray variance of the background noise.

[0075] The quantum fluctuation noise expectation determination unit is configured to determine the expectation of the quantum fluctuation noise of the monochromatic X-ray corresponding to the diffraction spot as zero.

[0076] Embodiment three

[0077] Figure 7A structural schematic diagram of an electronic device provided in Embodiment Three of the present application is shown in Figure 7 The electronic device includes a processor 710, a memory 720, an input device 730, and an output device 740; the number of processors 710 in the electronic device can be one or more, Figure 6 and an example of one processor 710 is provided in the present embodiment; the processor 710, the memory 720, the input device 730, and the output device 740 in the electronic device can be connected through a bus or other means, Figure 7 and an example of connection through a bus is provided in the present embodiment.

[0078] The memory 720, as a kind of computer readable storage medium, can be used to store software programs, computer executable programs, and modules, such as program instructions / modules corresponding to the cross-application task arousal method in the present embodiment. The processor 710 performs various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 720, that is, implements the noise calibration method of the multi-color X-ray area detector as described above:

[0079] With the same exposure time and incident angle, the same spectrum of multi-color X-rays is emitted to the same single crystal to decompose the diffracted multi-color X-rays into multiple beams of monochromatic X-rays to the area detector, and a Laue diffraction image formed on the area detector is acquired; the above process is repeated not less than three times;

[0080] For a local image of the same target diffraction spot in each diffraction image, the gray value of each pixel point in the local image of the target diffraction spot is acquired;

[0081] The mean value and the variance of the gray value of each pixel of the target diffraction spot in multiple tests are subjected to linear regression analysis to obtain the gray gain and the first intercept;

[0082] According to the gray gain and the first intercept of all diffraction spots, the background noise of the area detector is subjected to linear regression analysis, and the background noise and the quantum fluctuation noise of each diffraction spot corresponding to the monochromatic X-rays are calibrated according to the analysis result.

[0083] Further, the mean value and the variance of the gray value of each pixel of the target diffraction spot in multiple tests are subjected to linear regression analysis to obtain the gray gain and the first intercept, including:

[0084] The gray variance value and the average gray value are determined according to the gray value of each pixel point in the image of the target diffraction spot;

[0085] The average gray value is taken as the abscissa, the gray variance value is taken as the ordinate, and the gray variance value and the average gray value of each pixel point are subjected to linear regression analysis to obtain the first linear slope and the first intercept;

[0086] determine the first linear slope as a gray scale gain of the target diffraction spot.

[0087] Further, according to the gray scale gain and the first intercept of all diffraction spots, a linear regression analysis is performed on the background noise of the area detector, including:

[0088] Taking the gray scale gain of each diffraction spot as the abscissa and the first intercept of each diffraction spot as the ordinate, a linear regression analysis is performed according to the gray scale gain and the first intercept of each diffraction spot, to obtain a second linear slope and a second intercept;

[0089] The second linear slope and the second intercept are determined as the analysis result.

[0090] Further, according to the analysis result, the background noise is calibrated, including:

[0091] The distribution of the background noise is constructed as a Gaussian distribution;

[0092] According to the second linear slope, a background gray scale expectation of the background noise Gaussian distribution is determined, and the second intercept is determined as a gray scale variance of the background noise Gaussian distribution;

[0093] The background gray scale expectation and the gray scale variance are calibrated as noise parameters of the background noise.

[0094] Further, according to the analysis result, the quantum fluctuation noise of the corresponding monochromatic X-ray of each diffraction spot is calibrated, including:

[0095] For any diffraction spot, the gray scale variance of each pixel of the diffraction spot and the gray scale variance of the background noise are used to determine the variance of the quantum fluctuation noise of the corresponding monochromatic X-ray of the diffraction spot;

[0096] The expectation of the quantum fluctuation noise of the corresponding monochromatic X-ray of the diffraction spot is determined as zero.

[0097] The memory 720 can mainly include a storage program area and a storage data area, wherein the storage program area can store application programs required by the operation of the device, at least one function; the storage data area can store data created according to the use of the terminal and the like. In addition, the memory 720 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some examples, the memory 720 can further include a memory remotely arranged with respect to the processor 710, which can be connected to the electronic device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0098] Embodiment four

[0099] The embodiment four further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to perform a noise calibration method of a polychromatic X-ray area detector, the method comprising:

[0100] with the same exposure time and incident angle, emitting the same spectrum of polychromatic X-rays to the same single crystal to decompose the diffracted polychromatic X-rays into multiple beams of monochromatic X-rays to the area detector, and acquiring a Laue diffraction image formed on the area detector; the above process is repeated not less than three times;

[0101] for the local image of the same target diffraction spot in each diffraction image, acquiring the gray value of each pixel point in the local image of the target diffraction spot;

[0102] linearly regressing the mean value and the variance of the gray value of each pixel of the target diffraction spot in multiple tests to obtain the gray gain and the first intercept;

[0103] linearly regressing the background noise of the area detector according to the gray gain and the first intercept of all diffraction spots, and calibrating the background noise and the quantum fluctuation noise of the monochromatic X-rays corresponding to each diffraction spot according to the analysis result.

[0104] Further, linearly regressing the mean value and the variance of the gray value of each pixel of the target diffraction spot in multiple tests to obtain the gray gain and the first intercept, comprising:

[0105] determining the gray variance value and the average gray value according to the gray value of each pixel point in the image of the target diffraction spot;

[0106] taking the average gray value as the abscissa and the gray variance value as the ordinate, linearly regressing the gray variance value and the average gray value of each pixel point to obtain the first linear slope and the first intercept;

[0107] determining the first linear slope as the gray gain of the target diffraction spot.

[0108] Further, linearly regressing the background noise of the area detector according to the gray gain and the first intercept of all diffraction spots, comprising:

[0109] taking the gray gain of each diffraction spot as the abscissa and the first intercept of each diffraction spot as the ordinate, linearly regressing the gray gain and the first intercept of each diffraction spot to obtain the second linear slope and the second intercept;

[0110] determining the second linear slope and the second intercept as the analysis result.

[0111] Further, calibrating the background noise according to the analysis result, comprising:

[0112] The distribution of the background noise is constructed as a Gaussian distribution;

[0113] The background gray expectation of the background noise Gaussian distribution is determined according to the second linear slope, and the second intercept is determined as the gray variance of the background noise Gaussian distribution;

[0114] The background gray expectation and the gray variance are calibrated as the noise parameters of the background noise.

[0115] Further, the quantum fluctuation noise of the monochromatic X-ray corresponding to each diffraction spot is calibrated according to the analysis result, including:

[0116] For any diffraction spot, the variance of the quantum fluctuation noise of the monochromatic X-ray corresponding to the diffraction spot is determined by using the gray variance of each pixel of the diffraction spot and the gray variance of the background noise;

[0117] The expectation of the quantum fluctuation noise of the monochromatic X-ray corresponding to the diffraction spot is determined as zero.

[0118] Of course, the storage medium provided by the embodiment of the present application includes computer executable instructions, which are not limited to the method operations as above, and can also perform related operations in the noise calibration method of the polychromatic X-ray area detector provided by any embodiment of the present application.

[0119] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0120] It is worth noting that in the above embodiment of the device, each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy distinction from each other, and do not limit the protection scope of the present application.

[0121] Note that the above merely describes preferred embodiments of the application and the principles of the application. It will be understood by those skilled in the art that the application is not limited to the specific embodiments described herein, and that changes, modifications and substitutions can be made by those skilled in the art without departing from the scope of the application. Therefore, although the application has been described in detail by the above embodiments, the application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the application, and the scope of the application is determined by the appended claims.

Claims

1. A noise calibration method for a polychromatic X-ray area detector, characterized in that: The method comprises: Emitting polychromatic X-rays of the same spectrum at the same single crystal with the same exposure time and incident angle, decomposing the diffracted polychromatic X-rays into multiple beams of monochromatic X-rays directed toward an area detector, and acquiring a Laue diffraction image formed on the area detector; repeating the above process at least three times; For a partial image of the same target diffraction spot in each diffraction image, obtaining a grayscale value of each pixel in the partial image of the target diffraction spot; Performing linear regression analysis on the mean and variance of the grayscale value of each pixel of the target diffraction spot in multiple experiments to obtain the grayscale gain and the first intercept; According to the grayscale gain and the first intercept of all diffraction spots, a linear regression analysis is performed on the background noise of the area detector, and according to the analysis results, the background noise and the quantum fluctuation noise of the monochromatic X-ray corresponding to each diffraction spot are calibrated.

2. The method according to claim 1, characterized in that The method of performing linear regression analysis on the mean and variance of the grayscale value of each pixel of the target diffraction spot in multiple experiments to obtain the grayscale gain and the first intercept includes: Determine the grayscale variance value and the average grayscale value according to the grayscale value of each pixel point in the image of the target diffraction spot; The average grayscale value is used as the horizontal coordinate, and the grayscale variance value is used as the vertical coordinate. A linear regression analysis is performed based on the grayscale variance value and the average grayscale value of each pixel to obtain a first linear slope and a first intercept; The first linear slope is determined as the grayscale gain of the target diffraction spot.

3. The method according to claim 1, characterized in that The linear regression analysis of the background noise of the area detector is performed based on the grayscale gain and the first intercept of all diffraction spots, including: Taking the grayscale gain of each diffraction spot as the abscissa and the first intercept of each diffraction spot as the ordinate, a linear regression analysis is performed based on the grayscale gain and the first intercept of each diffraction spot to obtain a second linear slope and a second intercept; The second linear slope and the second intercept are determined as analysis results.

4. The method according to claim 3, characterized in that Calibrate the background noise according to the analysis result, including: Constructing the distribution of the background noise as a Gaussian distribution; Determine the background grayscale expectation of the background noise Gaussian distribution according to the second linear slope, and determine the second intercept as the grayscale variance of the background noise Gaussian distribution; The background grayscale expectation and the grayscale variance are calibrated as noise parameters of the background noise.

5. The method according to claim 4, characterized in that Based on the analysis results, the quantum fluctuation noise of the monochromatic X-ray corresponding to each diffraction spot is calibrated, including: For any diffraction spot, using the grayscale variance of each pixel of the diffraction spot and the grayscale variance of the background noise, determine the variance of the quantum fluctuation noise of the monochromatic X-ray corresponding to the diffraction spot; The expectation of the monochromatic X-ray quantum fluctuation noise corresponding to the diffraction spot is determined to be zero.

6. A noise calibration device for a polychromatic X-ray area detector, characterized in that: The device comprises: An image acquisition module is configured to emit polychromatic X-rays of the same spectrum toward the same single crystal, decompose the diffracted polychromatic X-rays into multiple beams of monochromatic X-rays, emit them toward an area detector, and acquire a Laue diffraction image formed on the area detector; the above process is repeated at least three times; a grayscale value acquisition module, configured to acquire, for a partial image of the same target diffraction spot in each diffraction image, a grayscale value of each pixel in the partial image of the target diffraction spot; A linear regression module is used to perform linear regression analysis on the mean and variance of the grayscale value of each pixel of the target diffraction spot in multiple experiments to obtain a grayscale gain and a first intercept; The linear regression calibration module is used to perform linear regression analysis on the background noise of the area detector based on the grayscale gain and first intercept of all diffraction spots, and calibrate the background noise and the quantum fluctuation noise of the monochromatic X-ray corresponding to each diffraction spot based on the analysis results.

7. The device according to claim 6, characterized in that The linear regression module includes: a first determining unit, configured to determine a grayscale variance value and an average grayscale value according to the grayscale value of each pixel point in the image of the target diffraction spot; A regression analysis unit, configured to use the average grayscale value as the horizontal coordinate and the grayscale variance value as the vertical coordinate, and perform a linear regression analysis based on the grayscale variance value and the average grayscale value of each pixel to obtain a first linear slope and a first intercept; The second determining unit is configured to determine the first linear slope as the grayscale gain of the target diffraction spot.

8. The device according to claim 6, characterized in that The linear regression calibration module includes: A construction unit, configured to construct the distribution of the background noise into a Gaussian distribution; a third determining unit, configured to determine a background grayscale expectation of the background noise Gaussian distribution according to the second linear slope, and determine the second intercept as the grayscale variance of the background noise Gaussian distribution; A background noise calibration unit, configured to calibrate the background grayscale expectation and the grayscale variance as noise parameters of the background noise; a quantum fluctuation noise variance determining unit, configured to determine, for any diffraction spot, the variance of the quantum fluctuation noise of the monochromatic X-ray corresponding to the diffraction spot by using the grayscale variance of each pixel of the diffraction spot and the grayscale variance of the background noise; The quantum fluctuation noise expectation determining unit is used to determine the expectation of the monochromatic X-ray quantum fluctuation noise corresponding to the diffraction spot to be zero.

9. An electronic device, characterized in that: include: one or more processors and memory devices; The storage device is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the noise calibration method for a polychromatic X-ray area detector as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the noise calibration method for a polychromatic X-ray area detector as claimed in any one of claims 1 to 5 is implemented.

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