Crystal pixel lookup table generation method and device, computer equipment and storage medium

Abstract

The invention relates to a crystal pixel lookup table generation method and device, computer equipment and a storage medium. Determining a preset number of first pixel peak points from the first crystal pixel lookup table, performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table, and performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table, and obtaining a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table. According to the embodiment of the invention, the second crystal pixel lookup table is obtained by performing data enhancement processing on the first pixel peak point, and the phenomenon that the crystal of the medical equipment disappears along with the use time is simulated, so that the target crystal pixel lookup table is obtained based on the first crystal pixel lookup table and the second crystal pixel lookup table; the number and diversity of crystal pixel lookup table data sets are improved, and the complexity of crystal pixel lookup table generation is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medicine, in particular to a crystal pixel lookup table generation method and device, computer equipment and a storage medium. BACKGROUND

[0002] The detector of a positron emission tomography (PET) device is a crystal array coupled with a photoelectric conversion device. There are tens of thousands of crystals on the PET device. When a gamma photon is incident on a crystal, a crystal lookup table (LUT) is obtained based on the counting of the incident gamma photon on the crystal. The pixel peak point in the LUT is located to obtain the position of the crystal in the LUT. The position of the crystal can be used to correct and rectify the reconstructed image. Therefore, improving the positioning accuracy of the pixel peak point is of great significance to improving the final imaging.

[0003] Thanks to the rapid development of current deep neural networks, the positioning of the pixel peak point can be based on deep neural networks. However, a large amount of rich LUT data sets are needed to train the deep neural network, otherwise the positioning accuracy of the pixel peak point based on the deep neural network is poor.

[0004] Currently, the LUT data sets are obtained from different PET devices, but there is a problem of complex LUT data set acquisition. SUMMARY

[0005] Therefore, it is necessary to provide a crystal pixel lookup table generation method and device, computer equipment and a storage medium capable of reducing the complexity of LUT data set acquisition.

[0006] In a first aspect, the present application provides a crystal pixel lookup table generation method, comprising:

[0007] determining a preset number of first pixel peak points from a first crystal pixel lookup table;

[0008] performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0009] obtaining a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0010] In one embodiment, the method further comprises:

[0011] determining the preset number of first pixel peak points from second pixel peak points; the second pixel peak points are pixel peak points at edge positions in the first crystal pixel lookup table.

[0012] In one of the embodiments, the determining the preset number of first pixel peak points from the first crystal pixel lookup table comprises:

[0013] determining the preset number of first pixel peak points from the first crystal pixel lookup table.

[0014] In one of the embodiments, the data enhancement processing of the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table comprises:

[0015] performing Gaussian processing on the first pixel peak points to obtain first Gaussian distribution values of the first pixel peak points;

[0016] performing transformation processing on the first Gaussian distribution values according to preset values to obtain second Gaussian distribution values;

[0017] obtaining the second crystal pixel lookup table according to the second Gaussian distribution values and the first crystal pixel lookup table.

[0018] In one of the embodiments, the obtaining the second crystal pixel lookup table according to the second Gaussian distribution values and the first crystal pixel lookup table comprises:

[0019] obtaining the second crystal pixel lookup table according to the product of the second Gaussian distribution values and pixel values of each pixel point in the first crystal pixel lookup table.

[0020] In one of the embodiments, the obtaining a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table comprises:

[0021] performing image enhancement processing on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain the target crystal pixel lookup table;

[0022] the image enhancement processing comprises at least one of the following ways:

[0023] performing a flipping operation on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0024] performing a noise adding operation on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0025] performing a smoothing operation on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0026] performing a contrast processing operation on the first and second crystal pixel lookup tables.

[0027] In a second aspect, the present application provides a crystal pixel lookup table generation device, comprising:

[0028] a first determination module configured to determine a preset number of first pixel peak points from a first crystal pixel lookup table;

[0029] a processing module configured to perform data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0030] a second determination module configured to obtain a target crystal pixel lookup table based on the first and second crystal pixel lookup tables.

[0031] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] determining a preset number of first pixel peak points from a first crystal pixel lookup table;

[0033] performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0034] obtaining a target crystal pixel lookup table based on the first and second crystal pixel lookup tables.

[0035] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the following steps when executed by a processor:

[0036] determining a preset number of first pixel peak points from a first crystal pixel lookup table;

[0037] performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0038] obtaining a target crystal pixel lookup table based on the first and second crystal pixel lookup tables.

[0039] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program implements the following steps when executed by a processor:

[0040] determining a preset number of first pixel peak points from a first crystal pixel lookup table;

[0041] perform data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0042] obtain a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0043] The crystal pixel lookup table generation method, device, computer device, and storage medium determine a preset number of first pixel peak points in a first crystal pixel lookup table, perform data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table, perform data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table, and obtain a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table. In the embodiments of the present application, the first pixel peak points in the first crystal pixel lookup table are processed by data enhancement to obtain a second crystal pixel lookup table, the phenomenon of crystal disappearance of a medical device over time is simulated, a target crystal pixel lookup table is obtained based on the first crystal pixel lookup table and the second crystal pixel lookup table, the number and diversity of the crystal pixel lookup table data set are improved, the complexity of the crystal pixel lookup table generation is reduced, and a foundation is laid for subsequent training of a deep neural network based on the crystal pixel lookup table data set. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0045] Figure 1 An application environment diagram of the crystal pixel lookup table generation method in an embodiment;

[0046] Figure 2 A flowchart of the crystal pixel lookup table generation method in an embodiment;

[0047] Figure 3 A schematic diagram of the crystal pixel lookup table generation in an embodiment;

[0048] Figure 4 A schematic diagram of the crystal pixel lookup table in another embodiment;

[0049] Figure 5 A schematic diagram of the crystal pixel lookup table in another embodiment;

[0050] Figure 6A flowchart of a method for generating a second crystal pixel lookup table in one embodiment;

[0051] Figure 7 A diagram of a first crystal pixel lookup table in one embodiment;

[0052] Figure 8 A diagram of a first crystal pixel lookup table in another embodiment;

[0053] Figure 9 A diagram of a first crystal pixel lookup table in another embodiment;

[0054] Figure 10 A diagram of a first Gaussian distribution value in one embodiment;

[0055] Figure 11 A diagram of a second Gaussian distribution value in one embodiment;

[0056] Figure 12 A diagram of a second crystal pixel lookup table in one embodiment;

[0057] Figure 13 A diagram of a comparison of pixel peak point positioning results in one embodiment;

[0058] Figure 14 A block diagram of a crystal pixel lookup table generation device in one embodiment;

[0059] Figure 15 An internal structure diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0061] Spatial resolution is the distance between two point sources that can be distinguished when the point sources are very close to each other, and is usually expressed by the full width at half maximum (FWHM) of a line spread function (LSF) in mm. The spatial resolution of a PET device is divided into axial resolution, transverse layer radial resolution and tangential resolution. The main factors affecting the resolution include the material, size, signal-to-noise ratio and probe aperture of the detector.

[0062] In addition, the point source is placed at different positions in the field of view of the PET device, and the spatial resolution is slightly different. The farther from the center of the field of view, the worse the spatial resolution. Each crystal constituting the detector can be regarded as a point source. The LUT is obtained based on the counting of gamma photons incident to each crystal. The LUT is the original data input to the reconstruction algorithm for image reconstruction. The spatial resolution is an important factor affecting the quality of the reconstructed image. The positioning accuracy of the pixel peak point directly affects the size of the spatial resolution. Therefore, improving the positioning accuracy of the pixel peak point is a sufficient condition for improving the quality of the reconstructed image.

[0063] The position of the crystal in the LUT is determined by the position of the pixel peak point, so as to complete image reconstruction according to the position of the crystal. Since the workload of pixel peak point positioning in each PET device is very large, manual correction is very labor-intensive and increases the possibility of human error, so it is necessary to develop a pixel peak point positioning algorithm applicable to each PET device. However, in reality, many objective conditions and factors limit the completion of the pixel peak point positioning task, for example, the uneven distribution of pixel peak points in the LUT is affected by the hardware structure and organization method of the PET device, and sometimes it is difficult to distinguish with the naked eye.

[0064] In the related art, there are generally three methods for pixel peak point positioning of the crystal pixel lookup table. The first method is based on a deep neural network. The deep neural network is modeled using crystal pixel lookup table samples, and the modeled deep neural network is applied to the positioning of the pixel peak point of the crystal pixel lookup table. However, this modeling method is extremely dependent on the number and quality of the crystal pixel lookup table dataset. Without a sufficient amount of crystal pixel lookup table dataset as support, the obtained model is prone to overfitting and has poor generalization, making it difficult to achieve the best performance of the method. In reality, the acquisition of the crystal pixel lookup table dataset requires cost, and the crystal pixel lookup table dataset used for training needs to meet certain diversity requirements, which is reflected in the use site of the PET device, the use time of the PET device, and the maintenance status of the PET device. With the different use time and maintenance status of the PET device, the crystal pixel lookup table will present different states. Due to the particularity of the PET device, it is very difficult to obtain a sufficient amount of crystal pixel lookup table dataset for optimization algorithm training. First, the number of PET devices is relatively small. Moreover, the use status of different PET devices is different, but the PET device of each status can only collect data once. It is meaningless to collect data multiple times for the PET device of the same status, because the data of each PET device in a short time is homogeneous.

[0065] The second method is based on iterative classification, which is large in calculation amount, needs multiple iterations, is sensitive to initialization, and is low in efficiency and reliability; the third method is to perform two-dimensional statistics on incident photon event position information to obtain a photon event position histogram, then obtain a crystal center position map by detecting a crystal position, obtain an initialization position of the crystal center position map, then recursively obtain a row-column crystal center position map according to the initialization position, establish a grid template by concatenating the row-column crystal center position map by using a spline method, and finally generate a crystal pixel lookup table. This method cannot well cope with the situation that the incident photon event position information is poor when the PET device is aging, and the method often causes positioning failure. Therefore, the present application provides a crystal pixel lookup table generation method, device, computer equipment and storage medium which can solve the above technical problems.

[0066] The crystal pixel lookup table generation method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 . The application environment includes a computer equipment, which can be a server, and the internal structure diagram of the computer equipment can be as shown in Figure 1 . The computer equipment includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer equipment is used to provide calculation and control capability. The memory of the computer equipment includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer equipment is used to store related data of the crystal pixel lookup table generation. The input / output interface of the computer equipment is used to exchange information between the processor and external devices. The communication interface of the computer equipment is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a crystal pixel lookup table generation method. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0067] In an exemplary embodiment, as shown in Figure 2 , a crystal pixel lookup table generation method is provided. Taking the computer equipment in Figure 1 as an example, the method includes the following S201 to S203. Wherein:

[0068] S201, determining a preset number of first pixel peak points from a first crystal pixel lookup table.

[0069] In the embodiment, the spatial resolution indicates the resolution capability of the PET device for two "points" in space, an ideal radioactive point source is placed in the field of view (FOV) of the PET device, and the radioactive distribution image obtained by the PET device is not a point but has a certain extension, and a "ball" is obtained, and the size of the ball reflects the spatial resolution capability of the PET device. Therefore, a first crystal pixel lookup table of a plurality of crystals is obtained by using the PET device, and each crystal also has a certain range on the first crystal pixel lookup table. The pixel peak points of each crystal are marked in the first crystal pixel lookup table in a manual marking manner, and the pixel peak points are used to represent the position information of the crystal in the first crystal pixel lookup table. The result after marking is as shown in Figure 3

[0070] The preset number is less than or equal to the number of pixel peak points in the first crystal pixel lookup table.

[0071] The preset number of first pixel peak points can be determined randomly from the marked pixel peak points, or the preset number of first pixel peak points can be determined from the pixel peak points located at the edges.

[0072] S202, performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table.

[0073] In the embodiment, as the use time of the PET device increases, the original crystal position lookup table will present a certain regular damage and change, and the crystal imaging located at the edges of the crystal lookup table will gradually become blurred or even disappear. Therefore, the data enhancement processing is performed on the first pixel peak points in the first crystal pixel lookup table to simulate the phenomenon that the crystal imaging in the first crystal pixel lookup table becomes blurred, and the second crystal pixel lookup table is obtained. For example, the first pixel peak points are subjected to Gaussian processing to obtain a Gaussian processing result, and the second crystal pixel lookup table is obtained according to the Gaussian processing result and the first crystal pixel lookup table; or a smaller weight is assigned to the first pixel peak points, the pixel value of the first pixel peak points is changed based on the weight, and the second crystal pixel lookup table is obtained according to the changed pixel value and the first crystal pixel lookup table.

[0074] Optionally, the blurred crystal imaging can refer to the phenomenon that the crystal in the crystal lookup table completely disappears or the crystal imaging is weakened.

[0075] S203, obtaining a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0076] In the embodiment, the image enhancement processing can be performed on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain the target crystal pixel lookup table.​

[0077] In a possible implementation, the first crystal pixel lookup table and the second crystal pixel lookup table can also be directly used as the target crystal pixel lookup table. The first crystal pixel lookup table, the second crystal pixel lookup table and the target crystal pixel lookup table obtained based on the present application are used to train a deep neural network, a trained deep neural network is obtained, and the trained deep neural network is applied to positioning of a pixel peak point of a crystal pixel lookup table in practice, thereby solving the problems of the second pixel peak point positioning method, such as large amount of calculation, multiple iterative calculation, sensitivity to initialization, low efficiency and low reliability, and the problems of the third pixel peak point positioning method, such as poor response to the situation that the incident photon event position information is poor when the PET device is aging, and the method often fails to position.

[0078] In the crystal pixel lookup table generation method, a preset number of first pixel peak points are determined from the first crystal pixel lookup table, data augmentation processing is performed on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table, data augmentation processing is performed on the first pixel peak points in the first crystal pixel lookup table to obtain the second crystal pixel lookup table, and a target crystal pixel lookup table is obtained based on the first crystal pixel lookup table and the second crystal pixel lookup table. In the embodiment of the present application, the first pixel peak points in the first crystal pixel lookup table are subjected to data augmentation processing to obtain the second crystal pixel lookup table, the phenomenon of crystal disappearance of the medical device with the use time is simulated, and then the target crystal pixel lookup table is obtained based on the first crystal pixel lookup table and the second crystal pixel lookup table, thereby increasing the number and diversity of the crystal pixel lookup table data set, reducing the complexity of the crystal pixel lookup table generation, and laying a foundation for subsequent training of the deep neural network based on the crystal pixel lookup table data set.

[0079] In an embodiment, determining a preset number of first pixel peak points from the first crystal pixel lookup table includes the following two ways

[0080] The first way is to determine a preset number of first pixel peak points from the second pixel peak points; the second pixel peak points are pixel peak points at edge positions in the first crystal pixel lookup table.

[0081] In the present embodiment, according to the observation of the PET devices with different use times and different states, it can be found that the crystal pixel lookup table will present a certain regular damage and change with the use and wear of the PET device. For example, the following Figure 4 and Figure 5The two crystal pixel lookup tables are obtained from different usage times of the PET device. It can be observed that as the usage time increases, the crystal imaging at the edges of the crystal pixel lookup table gradually becomes blurred (this is determined by the hardware design). Therefore, a preset number of first pixel peak points can be determined from the pixel peak points at the edge of the first crystal pixel lookup table, which is more in line with the actual usage of the PET device. As mentioned above Figure 3 As shown, there are 22 pixel peak points at the four edges, and 7 first pixel peak points are randomly selected from the 22 pixel peak points.

[0082] The second method is to determine a preset number of first pixel peak points from each pixel peak point in the first crystal pixel lookup table.

[0083] In this embodiment, a preset number of first pixel peak points can be randomly determined from each pixel peak point in the first crystal pixel lookup table, or a preset number of first pixel peak points can be determined from the marked pixel peak points at equal intervals. Figure 3 As shown, the first crystal pixel lookup table includes 42 pixel peak points in total, and 15 pixel peak points are randomly selected from the 42 pixel peak points; or 3 pixel peak points can be selected from the pixel peak points in each row.

[0084] In an embodiment of the present application, a preset number of first pixel peak points are determined from pixel peak points at edge positions in the first crystal pixel lookup table, or a preset number of first pixel peak points are determined from each pixel peak point in the first crystal pixel lookup table. In an embodiment of the present application, two methods for determining the first pixel peak points are provided, making the method for determining the first pixel peak points more flexible and increasing the diversity of determining the target crystal pixel lookup table based on the first pixel peak points.

[0085] Figure 6 FIG. 1 is a flow chart of a method for generating a second crystal pixel lookup table in one embodiment. Figure 6 As shown, the embodiment of the present application relates to a possible implementation method of how to perform data enhancement processing on the first pixel peak point in the first crystal pixel lookup table to obtain a second crystal pixel lookup table, including the following steps:

[0086] S601 , performing Gaussian processing on a first pixel peak point to obtain a first Gaussian distribution value of the first pixel peak point.

[0087] In this embodiment, a hyperparameter can be set for each first pixel peak point value, The value is a floating point number, and the value range can be set as [3, 50] in general. The first pixel peak point can be processed by using the following formula to obtain the first Gaussian distribution value with the first pixel peak point as the center:

[0088]

[0089] wherein (x, y) is the coordinate information of the first pixel peak point, and a and b are the double-sided critical values of the Gaussian processing. Optionally, the value range of a can be (-3 , 3 ), and the value range of b can be (-3 , 3 ).

[0090] As shown in FIG. 1, FIG. 2, and FIG. 3, a first crystal pixel lookup table is provided, and each pixel peak point in the first crystal pixel lookup table is marked. As shown in FIG. 4, a pixel peak point is randomly selected from the first crystal pixel lookup table with the marking result of each pixel peak point as the first pixel peak point (indicated by a pentagram), and the first Gaussian distribution value of the first pixel peak point is obtained by using the above formula. As shown in FIG. 5, the first Gaussian distribution value is obtained by using the above formula. Figure 7 Figure 7 As shown in FIG. 1, FIG. 2, and FIG. 3, a first crystal pixel lookup table is provided, and each pixel peak point in the first crystal pixel lookup table is marked. As shown in FIG. 4, a pixel peak point is randomly selected from the first crystal pixel lookup table with the marking result of each pixel peak point as the first pixel peak point (indicated by a pentagram), and the first Gaussian distribution value of the first pixel peak point is obtained by using the above formula. As shown in FIG. 5, the first Gaussian distribution value is obtained by using the above formula. Figure 8 Figure 8 As shown in FIG. 1, FIG. 2, and FIG. 3, a first crystal pixel lookup table is provided, and each pixel peak point in the first crystal pixel lookup table is marked. As shown in FIG. 4, a pixel peak point is randomly selected from the first crystal pixel lookup table with the marking result of each pixel peak point as the first pixel peak point (indicated by a pentagram), and the first Gaussian distribution value of the first pixel peak point is obtained by using the above formula. As shown in FIG. 5, the first Gaussian distribution value is obtained by using the above formula. Figure 9 Figure 10 As shown in FIG. 1, FIG. 2, and FIG. 3, a first crystal pixel lookup table is provided, and each pixel peak point in the first crystal pixel lookup table is marked. As shown in FIG. 4, a pixel peak point is randomly selected from the first crystal pixel lookup table with the marking result of each pixel peak point as the first pixel peak point (indicated by a pentagram), and the first Gaussian distribution value of the first pixel peak point is obtained by using the above formula. As shown in FIG. 5, the first Gaussian distribution value is obtained by using the above formula.

[0091] S602, the first Gaussian distribution value is transformed according to a preset value to obtain a second Gaussian distribution value.

[0092] In this embodiment, the first Gaussian distribution value can be inverted by using the following formula to obtain the second Gaussian distribution value G1:

[0093]

[0094] In one example, the first Gaussian distribution value in the above formula is inverted to obtain the second Gaussian distribution value, and the result of the second Gaussian distribution value is shown in FIG. 6. Figure 10 Figure 11

[0095] S603, the second Gaussian distribution value and the first crystal pixel lookup table are used to obtain a second crystal pixel lookup table.

[0096] In this embodiment, the second Gaussian distribution value and each pixel value in the first crystal pixel lookup table can be multiplied to obtain the second crystal pixel lookup table.

[0097] ​​​​​In a possible implementation, the second Gaussian distribution value can also be finely adjusted, and the second crystal pixel lookup table is obtained according to the adjusted second Gaussian distribution value and the first crystal pixel lookup table.

[0098] Further, the second crystal pixel lookup table is obtained according to the second Gaussian distribution value and the first crystal pixel lookup table, including: the second crystal pixel lookup table is obtained according to a product result of the second Gaussian distribution value and pixel values of each pixel point in the first crystal pixel lookup table.

[0099] In the embodiment, the second Gaussian distribution value is multiplied by the pixel values of each pixel point in the first crystal pixel lookup table to obtain the second crystal pixel lookup table Aug, that is, the second crystal pixel lookup table is obtained according to Aug. In an example, the first crystal pixel lookup table represented by Figure 7 and the second Gaussian distribution value represented by Figure 11 are multiplied to obtain the second crystal pixel lookup table as shown in Figure 12 . By comparing Figure 7 and Figure 12 , it can be seen that the crystal at the position of the first pixel peak point in Figure 12 has disappeared, Figure 7 and Figure 12 the difference between the first pixel peak point data before and after enhancement is shown by the arrow.

[0100] In the embodiment, the first pixel peak point is Gaussian processed to obtain a first Gaussian distribution value of the first pixel peak point, the first Gaussian distribution value is transformed according to a preset value to obtain a second Gaussian distribution value, and the second crystal pixel lookup table is obtained according to the second Gaussian distribution value and the first crystal pixel lookup table. In the embodiment, the first pixel peak point is Gaussian processed, a few hyperparameters can be set to obtain the second crystal pixel lookup table, and the generation efficiency of the second crystal pixel lookup table is improved.

[0101] In an embodiment, how to obtain a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table is related, including: performing image enhancement processing on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain the target crystal pixel lookup table; the image enhancement processing includes at least one of the following modes: performing a flip operation on the first crystal pixel lookup table and the second crystal pixel lookup table; performing a noise adding operation on the first crystal pixel lookup table and the second crystal pixel lookup table; performing a smoothing operation on the first crystal pixel lookup table and the second crystal pixel lookup table; and performing a contrast processing operation on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0102] In this embodiment, a flipping operation can be performed on the first crystal pixel lookup table and the second crystal pixel lookup table. It should be noted that both the first crystal pixel lookup table and the second crystal pixel lookup table include corresponding pixel peak points. When the first crystal pixel lookup table and the second crystal pixel lookup table are flipped, the pixel peak points are also flipped.

[0103] Image enhancement measures such as adding noise, enhancing and reducing contrast, smoothing, cropping, rotating, and translating may also be used to the first crystal pixel lookup table and the second crystal pixel lookup table to obtain a target crystal pixel lookup table.

[0104] Furthermore, the pixel peak point positioning model is trained using the same pixel peak point positioning model, the same parameter configuration and the same number of training times, using the original crystal pixel lookup table samples and the crystal pixel lookup table samples enhanced by this application. Figure 13 As shown, the trained pixel peak point positioning model is tested on the crystal pixel lookup tables of different product models (product model 1 and product model 2), and the test results are evaluated using the root mean square error (RMSE). The smaller the RMSE value, the better the prediction performance of the pixel peak point positioning model. It can be seen that the algorithm performance can be improved by about 20% by using the method proposed in this application compared to not using it.

[0105] Among them, the calculation formula of RMSE evaluation index is: , (x label ,y label ) is the manually marked pixel peak point, (x pred ,y pred ) is the pixel peak point predicted by the pixel peak point positioning model.

[0106] In an embodiment of the present application, image enhancement processing is performed on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain a target crystal pixel lookup table. In an embodiment of the present application, image enhancement is further performed on the first crystal pixel lookup table and the second crystal pixel lookup table to further increase the number and data diversity of the crystal pixel lookup tables.

[0107] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in sequence, and the steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0108] Based on the same inventive concept, the embodiments of the present application also provide a crystal pixel lookup table generation device for implementing the above-mentioned crystal pixel lookup table generation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more crystal pixel lookup table generation device embodiments provided below can refer to the limitations of the crystal pixel lookup table generation method in the above text, which will not be repeated here.

[0109] In one exemplary embodiment, as shown in Figure 14 a crystal pixel lookup table generation device is provided, comprising: a first determination module 11, a processing module 12 and a second determination module 13, wherein:

[0110] The first determination module 11 is configured to determine a preset number of first pixel peak points from the first crystal pixel lookup table.

[0111] The processing module 12 is configured to perform data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table.

[0112] The second determination module 13 is configured to obtain a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0113] In one embodiment, the first determination module comprises:

[0114] The first determination unit is configured to determine a preset number of first pixel peak points from the second pixel peak points; the second pixel peak points are pixel peak points at edge positions in the first crystal pixel lookup table.

[0115] In one embodiment, the first determination module comprises:

[0116] The second determination unit is configured to determine a preset number of first pixel peak points from the pixel peak points of the first crystal pixel lookup table.

[0117] In one embodiment, the processing module comprises:

[0118] a first processing unit configured to perform Gaussian processing on the first pixel peak point to obtain a first Gaussian distribution value of the first pixel peak point;

[0119] a second processing unit configured to perform transformation processing on the first Gaussian distribution value according to a preset value to obtain a second Gaussian distribution value;

[0120] a third determination unit configured to obtain a second crystal pixel lookup table according to the second Gaussian distribution value and the first crystal pixel lookup table.

[0121] In one embodiment, the third determination unit is further configured to obtain the second crystal pixel lookup table according to a product result of the second Gaussian distribution value and pixel values of each pixel point in the first crystal pixel lookup table.

[0122] In one embodiment, the first determination module is specifically configured to perform image enhancement processing on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain a target crystal pixel lookup table.

[0123] The image enhancement processing comprises at least one of the following modes:

[0124] performing a flip operation on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0125] performing a noise addition operation on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0126] performing a smoothing operation on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0127] performing a contrast processing operation on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0128] Each module in the above crystal pixel lookup table generation apparatus can be realized by software, hardware and a combination thereof in whole or in part. The above modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to the above modules.

[0129] In one exemplary embodiment, a computer device is provided, which can be a terminal, and an internal structure diagram thereof can be as shown in Figure 15The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected through a system bus. The communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program is executed by the processor to implement a crystal pixel lookup table generation method. The display unit of the computer device is configured to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball, or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0130] Those skilled in the art can understand that Figure 15 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0131] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the following steps:

[0132] determining a preset number of first pixel peak points from the first crystal pixel lookup table;

[0133] performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0134] obtaining a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0135] In one embodiment, the processor executing the computer program further implements the following steps:

[0136] Determine a preset number of first pixel peak points from the second pixel peak points; the second pixel peak points are pixel peak points at edge positions in the first crystal pixel lookup table.

[0137] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0138] Determine a preset number of first pixel peak points from the second pixel peak points; the second pixel peak points are pixel peak points at edge positions in the first crystal pixel lookup table.

[0139] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0140] Gaussian process the first pixel peak points to obtain first Gaussian distribution values of the first pixel peak points;

[0141] According to the preset value, the first Gaussian distribution values are transformed to obtain second Gaussian distribution values;

[0142] According to the second Gaussian distribution values and the first crystal pixel lookup table, a second crystal pixel lookup table is obtained.

[0143] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0144] According to the product of the second Gaussian distribution values and the pixel values of each pixel point in the first crystal pixel lookup table, a second crystal pixel lookup table is obtained.

[0145] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0146] Image enhancement processing is performed on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain a target crystal pixel lookup table;

[0147] The image enhancement processing includes at least one of the following ways:

[0148] Flip operation is performed on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0149] Noise addition operation is performed on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0150] Smoothing operation is performed on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0151] Contrast processing operation is performed on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0152] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0153] determining a preset number of first pixel peak points from the first crystal pixel lookup table;

[0154] performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0155] obtaining a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table.

[0156] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0157] determining a preset number of first pixel peak points from the second pixel peak points; the second pixel peak points are pixel peak points at edge positions in the first crystal pixel lookup table.

[0158] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0159] determining a preset number of first pixel peak points from the first pixel peak points in the first crystal pixel lookup table.

[0160] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0161] performing Gaussian processing on the first pixel peak points to obtain first Gaussian distribution values of the first pixel peak points;

[0162] performing transformation processing on the first Gaussian distribution values according to a preset value to obtain second Gaussian distribution values;

[0163] obtaining a second crystal pixel lookup table according to the second Gaussian distribution values and the first crystal pixel lookup table.

[0164] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0165] obtaining the second crystal pixel lookup table according to a product result of the second Gaussian distribution values and pixel values of each pixel point in the first crystal pixel lookup table.

[0166] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0167] performing image enhancement processing on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain a target crystal pixel lookup table;

[0168] The image enhancement processing includes at least one of the following modes:

[0169] performing a flip operation on the first crystal pixel lookup table and the second crystal pixel lookup table;

[0170] performing a noise adding operation on the first and second crystal pixel lookup tables;

[0171] performing a smoothing operation on the first and second crystal pixel lookup tables;

[0172] performing a contrast processing operation on the first and second crystal pixel lookup tables.

[0173] In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the following steps:

[0174] determining a preset number of first pixel peak points from the first crystal pixel lookup table;

[0175] performing data enhancement processing on the first pixel peak points in the first crystal pixel lookup table to obtain a second crystal pixel lookup table;

[0176] obtaining a target crystal pixel lookup table based on the first and second crystal pixel lookup tables.

[0177] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0178] determining a preset number of first pixel peak points from the second pixel peak points; the second pixel peak points being pixel peak points at edge positions in the first crystal pixel lookup table.

[0179] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0180] determining a preset number of first pixel peak points from the pixel peak points of the first crystal pixel lookup table.

[0181] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0182] performing Gaussian processing on the first pixel peak points to obtain first Gaussian distribution values of the first pixel peak points;

[0183] performing transformation processing on the first Gaussian distribution values according to a preset value to obtain second Gaussian distribution values;

[0184] obtaining the second crystal pixel lookup table according to the second Gaussian distribution values and the first crystal pixel lookup table.

[0185] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0186] obtaining the second crystal pixel lookup table according to a product of the second Gaussian distribution values and pixel values of the pixel points in the first crystal pixel lookup table.

[0187] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0188] The first crystal pixel lookup table and the second crystal pixel lookup table are subjected to image enhancement processing to obtain a target crystal pixel lookup table;

[0189] The image enhancement processing includes at least one of the following manners:

[0190] The first crystal pixel lookup table and the second crystal pixel lookup table are subjected to a flipping operation;

[0191] The first crystal pixel lookup table and the second crystal pixel lookup table are subjected to a noise adding operation;

[0192] The first crystal pixel lookup table and the second crystal pixel lookup table are subjected to a smoothing operation;

[0193] The first crystal pixel lookup table and the second crystal pixel lookup table are subjected to a contrast processing operation.

[0194] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0195] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0196] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0197] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for generating a crystal pixel lookup table, characterized in that: The method comprises: determining a preset number of first pixel peak points from a first crystal pixel lookup table; Performing data enhancement processing on the first pixel peak point in the first crystal pixel lookup table to obtain a second crystal pixel lookup table; A target crystal pixel lookup table is obtained based on the first crystal pixel lookup table and the second crystal pixel lookup table.

2. The method according to claim 1, characterized in that The step of determining a preset number of first pixel peak points from a first crystal pixel lookup table includes: The preset number of first pixel peak points are determined from the second pixel peak points; the second pixel peak points are pixel peak points at edge positions in the first crystal pixel lookup table.

3. The method according to claim 1, characterized in that The step of determining a preset number of first pixel peak points from a first crystal pixel lookup table includes: The preset number of first pixel peak points are determined from the pixel peak points in the first crystal pixel lookup table.

4. The method according to any one of claims 1 to 3, characterized in that The performing data enhancement processing on the first pixel peak point in the first crystal pixel lookup table to obtain a second crystal pixel lookup table includes: Performing Gaussian processing on the first pixel peak point to obtain a first Gaussian distribution value of the first pixel peak point; Transforming the first Gaussian distribution value according to a preset value to obtain a second Gaussian distribution value; The second crystal pixel lookup table is obtained according to the second Gaussian distribution value and the first crystal pixel lookup table.

5. The method according to claim 4, characterized in that The step of obtaining the second crystal pixel lookup table according to the second Gaussian distribution value and the first crystal pixel lookup table includes: The second crystal pixel lookup table is obtained according to a product result of the second Gaussian distribution value and the pixel value of each pixel point in the first crystal pixel lookup table.

6. The method according to claim 1, characterized in that The step of obtaining a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table includes: performing image enhancement processing on the first crystal pixel lookup table and the second crystal pixel lookup table to obtain the target crystal pixel lookup table; The image enhancement processing includes at least one of the following methods: performing a flip operation on the first crystal pixel lookup table and the second crystal pixel lookup table; performing a noise adding operation on the first crystal pixel lookup table and the second crystal pixel lookup table; performing a smoothing operation on the first crystal pixel lookup table and the second crystal pixel lookup table; A contrast processing operation is performed on the first crystal pixel lookup table and the second crystal pixel lookup table.

7. A crystal pixel lookup table generation device, characterized in that: The device comprises: A first determining module, configured to determine a preset number of first pixel peak points from a first crystal pixel lookup table; A processing module, configured to perform data enhancement processing on the first pixel peak point in the first crystal pixel lookup table to obtain a second crystal pixel lookup table; The second determining module is configured to obtain a target crystal pixel lookup table based on the first crystal pixel lookup table and the second crystal pixel lookup table.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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