CN114648611B - Three-dimensional reconstruction method and device for local orbit function - Google Patents
Three-dimensional reconstruction method and device for local orbit function Download PDFInfo
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Abstract
The application discloses a three-dimensional reconstruction method and device of a local orbit function, wherein the method comprises the following steps: collecting image data of samples under a plurality of inclination angles, scattering points at equal intervals in a real space, and utilizing linear accumulation to obtain a calculated image under each inclination angle of the plurality of inclination angles, further obtaining gradients of a loss function relative to parameters to be optimized, optimizing the parameters to be optimized according to the gradients, screening out atoms meeting preset conditions, recalculating a new loss function until meeting convergence conditions, reconstructing three-dimensional space coordinates of the center of a local orbit function in the real space and the shape of the local orbit function, and obtaining a three-dimensional reconstruction result. Therefore, the technical problems that in the related art, only three-dimensional coordinates of atoms can be obtained from the reconstructed three-dimensional density matrix, errors cannot be corrected, the reconstruction process has high requirements on hardware, and the accuracy of the reconstructed three-dimensional coordinates is poor are solved.
Description
Technical Field
The present disclosure relates to the field of three-dimensional imaging technologies, and in particular, to a method and an apparatus for three-dimensional reconstruction of a local orbit function.
Background
In general, the electron microscope pictures of the samples contain abundant information which is difficult to visually acquire, and the three-dimensional structure information of the samples is acquired from a series of electron microscope projection pictures of the samples by using a three-dimensional reconstruction algorithm, so that the method is greatly helpful for understanding the relationship between the material composition and the performance on a fundamental level.
In recent years, with the development of data acquisition methods, iterative three-dimensional reconstruction algorithms, and post-processing methods, AET (Atomic Electrical Tomography, atomic-scale electron tomography) has become a powerful tool for the characterization of three-and four-dimensional atomic-scale structures, which provides the ability to correlate material structures and properties at the atomic level, and AETs in the related art are able to determine three-dimensional atomic coordinates and element species with sub-angstrom accuracy, and reveal their atomic-scale time evolution in dynamic processes.
However, in the related art, the AET algorithm obtains the three-dimensional coordinates of the atoms from the reconstructed three-dimensional density matrix through peak searching, which has high requirement on hardware, and in the peak searching process, complicated human intervention is required, so that errors caused by human intervention are difficult to avoid.
Disclosure of Invention
The application provides a three-dimensional reconstruction method and device of a local orbit function, which are used for solving the technical problems that in the related technology, only three-dimensional coordinates of atoms can be obtained from a reconstructed three-dimensional density matrix, errors cannot be corrected, the requirement of a reconstruction process on hardware is high, and the accuracy of the reconstructed three-dimensional coordinates is poor.
An embodiment of a first aspect of the present application provides a three-dimensional reconstruction method of a local track function, including the following steps: collecting image data of a sample at a plurality of inclination angles; based on the image data, scattering points are carried out at equal intervals in real space, and linear accumulation is utilized to obtain a calculated image under each tilting angle of the plurality of tilting angles; and calculating a loss function according to the calculated image under each tilting angle, acquiring the gradient of the loss function relative to the parameter to be optimized, optimizing the parameter to be optimized according to the gradient, screening out atoms meeting the preset condition, and recalculating a new loss function until meeting the convergence condition, and obtaining a three-dimensional reconstruction result in the three-dimensional space coordinate of the center of the real space reconstruction local orbit function and the shape of the local orbit function.
Optionally, in one embodiment of the present application, acquiring image data of the sample at the plurality of tilt angles includes: acquiring initial image data of the sample at the plurality of tilt angles; and performing centering shaft and noise reduction processing on the initial image data, and normalizing the processed image to obtain the image data.
Optionally, in an embodiment of the present application, the calculation formula of the loss function is:
wherein W represents a loss function, M represents the total number of tilt angles, j represents the number of tilt angles, i represents the number of local track functions, P represents the side length of the image,u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, f j (u, v) represents the image calculated at the j-th angle, b j (u, v) represents an experimentally obtained image at the j-th angle.
Optionally, in one embodiment of the present application, the screening out atoms that meet a preset condition includes: after each step of iteration updating, detecting parameters of all local track functions, deleting any local track function when detecting that the parameters of any local track function are smaller than a threshold value obtained by the parameters of all local track functions, and simultaneously obtaining a local track function pair index with the center distance of the local track function smaller than a preset pixel by establishing a binary tree, and deleting any local track function in the local track function pair; after each step of iteration is completed to update parameters and delete the local track functions, reducing the parameters of each local track function to a preset multiple with a preset probability, and setting a protection time so that screening operation and deleting operation are not allowed to be executed within the protection time.
Optionally, in an embodiment of the present application, the parameter to be optimized includes at least one of three-dimensional space coordinates of a center of each local orbit function, a parameter describing a shape thereof, three euler angles corresponding to each rotation angle, a drift of the sample at each angle, and a mechanical tilting deviation of the sample stage.
An embodiment of a second aspect of the present application provides a three-dimensional reconstruction apparatus of a local orbit function, including: the acquisition module is used for acquiring image data of the sample at a plurality of inclination angles; the accumulation module is used for scattering points at equal intervals in real space based on the image data and utilizing linear accumulation to obtain a calculated image under each tilting angle of the plurality of tilting angles; and the reconstruction module is used for calculating a loss function according to the calculated image under each tilting angle, acquiring the gradient of the loss function relative to the parameter to be optimized, optimizing the parameter to be optimized according to the gradient, screening out atoms meeting the preset condition, and re-calculating a new loss function until meeting the convergence condition, and obtaining a three-dimensional reconstruction result in the three-dimensional space coordinate of the center of the real space reconstruction local orbit function and the shape of the local orbit function.
Optionally, in one embodiment of the present application, the acquisition module includes: an acquisition unit configured to acquire initial image data of the sample at the plurality of tilt angles; and the noise reduction unit is used for carrying out centering and noise reduction processing on the initial image data and normalizing the processed image to obtain the image data.
Optionally, in an embodiment of the present application, the calculation formula of the loss function is:
wherein W represents a loss function, M represents the total number of tilting angles, j represents the serial number of the tilting angle, i represents the serial number of the local orbit function, P represents the side length of the image, u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, f j (u, v) represents the image calculated at the j-th angle, b j (u, v) represents an experimentally obtained image at the j-th angle.
Optionally, in one embodiment of the present application, the reconstruction module includes: the detection unit is used for detecting the parameter deleting unit of all local track functions after the parameter is iteratively updated in each step, deleting any local track function when the parameter of any local track function is detected to be smaller than the threshold value obtained by the parameter of all local track functions, and simultaneously obtaining a local track function pair index with the center distance of the local track function smaller than a preset pixel by establishing a binary tree, and deleting any local track function in the local track function pair; and the protection unit is used for reducing the parameters of each local track function to a preset multiple by the preset probability after the parameters are iteratively updated and the local track functions are deleted in each step, and setting the protection time so that the screening operation and the deleting operation are not allowed to be executed in the protection time.
Optionally, in an embodiment of the present application, the parameter to be optimized includes at least one of three-dimensional space coordinates of a center of each local orbit function, a parameter describing a shape thereof, three euler angles corresponding to each rotation angle, a drift of the sample at each angle, and a mechanical tilting deviation of the sample stage.
An embodiment of a third aspect of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the three-dimensional reconstruction method of the local track function.
An embodiment of a fourth aspect of the present application provides a computer readable storage medium storing computer instructions for causing the computer to perform the three-dimensional reconstruction method of a local track function as described in the above embodiment.
According to the method and the device, based on the collected image data of the samples at the plurality of tilting angles, the calculated image at the plurality of tilting angles is obtained, then the loss function of the calculated image is calculated, the gradient of the optimized parameter is calculated, so that the optimized parameter is obtained, after repeated screening and calculation, the loss function meets the convergence condition, then the three-dimensional reconstruction result is obtained, the three-dimensional coordinate reconstruction process can be simplified, the requirement on hardware is reduced, the complicated human intervention can be reduced, the labor cost is saved, meanwhile, the sample drift and the mechanical tilting error of the sample table can be corrected in the iteration process, and the three-dimensional coordinate reconstruction accuracy is improved. Therefore, the technical problems that in the related art, only three-dimensional coordinates of atoms can be obtained from the reconstructed three-dimensional density matrix, errors cannot be corrected, the reconstruction process has high requirements on hardware, and the accuracy of the reconstructed three-dimensional coordinates is poor are solved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for three-dimensional reconstruction of a local orbit function according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of three-dimensional reconstruction of a local orbit function according to one embodiment of the present application;
FIG. 3 is a schematic representation of a simulation of small particles of 10000 atoms to be reconstituted at 25, 0, and-25 according to one embodiment of the present application;
FIG. 4 is a schematic view of a scatter plot of atomic coordinates of an initial input of a three-dimensional reconstruction method of a local orbit function according to one embodiment of the present application;
FIG. 5 is a schematic representation of the calculation of the initial input local orbit function at tilt angles of 25, 0 and-25, respectively, for a three-dimensional reconstruction method of local orbit functions according to one embodiment of the present application;
FIG. 6 is a broken line schematic diagram of the values of a loss function during an iteration of a method of three-dimensional reconstruction of a local orbit function according to one embodiment of the present application;
FIG. 7 is a schematic diagram of an atomic model obtained after convergence of a method for three-dimensional reconstruction of a local orbit function according to one embodiment of the present application;
FIG. 8 is a difference between a calculated image and an experimental image at convergence of a three-dimensional reconstruction method of a local orbit function according to one embodiment of the present application;
FIG. 9 is a distance histogram between atomic coordinates and real coordinates calculated by convergence of a three-dimensional reconstruction method of a local orbit function according to one embodiment of the present application;
fig. 10 is a schematic structural diagram of a three-dimensional reconstruction device of a local orbit function according to an embodiment of the present application;
fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes a three-dimensional reconstruction method and device of a local orbit function according to an embodiment of the present application with reference to the accompanying drawings. Aiming at the technical problems that the related technology mentioned in the background center can only acquire the three-dimensional coordinates of atoms from the reconstructed three-dimensional density matrix and cannot correct errors, so that the reconstruction process has higher requirements on hardware and the reconstructed three-dimensional coordinates have poorer precision, the application provides a three-dimensional reconstruction method of a local orbit function. Therefore, the technical problems that in the related art, only three-dimensional coordinates of atoms can be obtained from the reconstructed three-dimensional density matrix, errors cannot be corrected, the reconstruction process has high requirements on hardware, and the accuracy of the reconstructed three-dimensional coordinates is poor are solved.
Specifically, fig. 1 is a schematic flow chart of a three-dimensional reconstruction method of a local orbit function according to an embodiment of the present application.
As shown in fig. 1, the three-dimensional reconstruction method of the local orbit function includes the following steps:
in step S101, image data of a sample at a plurality of tilt angles is acquired.
In the actual implementation process, the embodiment of the application can shoot HAADF (High-Angle Annular Dark-Field imaging) images of the sample at a series of different angles, so as to obtain image data of the sample at a plurality of tilting angles, facilitate the subsequent processing of the image data and reconstruct three-dimensional coordinates.
Optionally, in one embodiment of the present application, acquiring image data of the sample at a plurality of tilt angles includes: acquiring initial image data of a sample at a plurality of inclination angles; and carrying out centering shaft and noise reduction treatment on the initial image data, and normalizing the treated image to obtain image data.
Specifically, after initial image data of a sample under a plurality of inclination angles are obtained through shooting, in order to ensure accuracy of subsequent three-dimensional coordinate reconstruction, neutralization axis and noise reduction treatment can be performed on the initial image data, the influence of noise points on the three-dimensional coordinate reconstruction is avoided while subsequent calculation is facilitated, and then the image data which can be used for subsequent calculation is obtained through linear normalization.
The specific process of the linear normalization is as follows:
wherein M represents the total number of tilting angles, j represents the serial number of the tilting angles, u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, b j (u, v) represents an experimentally obtained image at the j-th angle.
In step S102, based on the image data, scattering points are performed at equal intervals in real space and linear accumulation is used to obtain a calculated image at each of a plurality of tilt angles.
As a possible implementation manner, the embodiment of the present application may firstly set a discretized, limited number of local track functions in real space as initial inputs of an iterative process according to a certain rule based on the image data obtained by the steps, where the intensity H of each local track function may be set to 1e -5 The width can be set to be 1.4, and the sample drift amount under each angle(u j ,v j ) And offset of Euler angleMay be set to 0.
Specifically, the embodiment of the application can represent the three-dimensional coordinates of the center of the local orbit function under different angles as:
wherein, psi is j 、θ j 、Euler angles around the x-axis, y-axis, z-axis for the distribution corresponding to the jth angle, (x) i ,y i ,z i ) For the central three-dimensional position coordinates of the ith local orbit function when not rotated (three Euler angles are all 0), (u) ij ,v ij ,w ij ) The central three-dimensional position coordinate of the ith local orbit function at the jth angle.
Further, the embodiment of the application can calculate the coordinates of the local track center under each angle:
furthermore, according to the coordinates, the embodiment of the application can represent the local orbit function as a three-dimensional gaussian function, and calculate the value of each local orbit function under each angle:
wherein D is ij Representing the value of the ith local track function at the (u, v, w) position in real space at the jth angle, H i And B i For the parameters to be optimized, the intensity and width of the ith local track function are represented respectively, (u, v, w)Is a real space three-dimensional position coordinate, (u) ij ,v ij ,w ij ) A central three-dimensional position coordinate at the jth angle for the ith local orbit function, (u) j ,v j ,w j ) For the j-th angle the local orbit function center is relative to the true position (u ij ,v ij ,w ij ) Is shifted in three dimensions.
Through linear accumulation, the embodiment of the application can obtain a calculated image:
wherein N represents the total number of the local track functions, D ij Representing the value of the ith local track function at the (u, v, w) position in real space at the jth angle.
In step S103, a loss function is calculated according to the calculated image at each tilting angle, a gradient of the loss function with respect to the parameter to be optimized is obtained, the parameter to be optimized is optimized according to the gradient, atoms meeting the preset condition are screened out, a new loss function is recalculated until the convergence condition is met, and a three-dimensional reconstruction result is obtained in real space by reconstructing the three-dimensional space coordinates of the center of the local orbit function and the shape of the local orbit function.
In the actual execution process, the embodiment of the application can solve parameters by utilizing a gradient optimization algorithm, and reconstruct the three-dimensional space coordinates of the center of the local orbit function and the shape of the local orbit function in real space through multiple times of calculation and screening to obtain a three-dimensional reconstruction result.
Specifically, the embodiment of the application may calculate the loss function by using the calculated image obtained by calculation in the above step, further obtain a gradient of the loss function with respect to the parameter to be optimized, optimize the parameter to be optimized by using an optimization algorithm of the gradient, further screen out atoms meeting the preset condition, and repeatedly calculate the loss function until meeting the convergence condition, thereby reconstructing the three-dimensional space coordinates of the center of the local orbit function and the shape of the local orbit function in real space, and obtaining the three-dimensional reconstruction result.
According to the embodiment of the application, the three-dimensional coordinates of the sample atoms can be directly obtained, the process of obtaining the three-dimensional density matrix and then searching peaks to obtain the three-dimensional coordinates is skipped, the requirements on hardware are reduced, tedious human intervention in the peak searching process is eliminated, labor cost is saved, meanwhile, in the iteration process, the sample drift and the mechanical tilting error of the sample table can be corrected, and the accuracy of three-dimensional coordinate reconstruction is improved.
It should be noted that the preset conditions may be set by those skilled in the art according to actual situations, and are not particularly limited herein.
Optionally, in one embodiment of the present application, the calculation formula of the loss function is:
wherein W represents a loss function, M represents the total number of tilting angles, j represents the serial number of the tilting angle, i represents the serial number of the local orbit function, P represents the side length of the image, u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, f j (u, v) represents the image calculated at the j-th angle, b j (u, v) represents an experimentally obtained image at the j-th angle.
Specifically, the embodiment of the present application can calculate the loss function by the calculation image in the above step, and write the loss function as a function with respect to the calculation image and the experimental image:
wherein W represents a loss function, M represents the total number of tilting angles, j represents the serial number of the tilting angle, i represents the serial number of the local orbit function, P represents the side length of the image, u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, f j (u, v) represents the image calculated at the j-th angle, b j (u, v) represents the graph obtained by experiments at the j-th angle Image, m j (u, v) is the difference between the calculated image and the experimental image at the j-th angle.
Optionally, in an embodiment of the present application, the parameters to be optimized include at least one of three-dimensional space coordinates of a center of each local orbit function, parameters describing a shape thereof, three euler angles corresponding to each corner, drift of the sample at each angle, and mechanical tilting deviation of the sample stage.
After obtaining the loss function, embodiments of the present application may find the three-dimensional coordinates (x i ,y i ,z i ) Strength H i Width B i Amount of sample drift at each angle (u j ,u j ) Angle deviation psi of sample stage j 、θ j 、A gradient of an isoparametric.
Further, the embodiment of the application may update the target parameter with the gradient obtained by calculation:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Is the learning rate of each parameter.
It should be noted that the gradient may be obtained by using a software library having an automatic deriving function, or by analyzing an expression.
Optionally, in one embodiment of the present application, screening out atoms that meet a preset condition includes: after each step of iteration updating, detecting parameters of all local track functions, deleting any local track function when detecting that the parameters of any local track function are smaller than a threshold value obtained by the parameters of all local track functions, and simultaneously obtaining a local track function pair index with the center distance of the local track function smaller than a preset pixel by establishing a binary tree, and deleting any local track function in the local track function pair; after each step of iteration is completed and the local track function is deleted, reducing the parameters of each local track function to a preset multiple by using a preset probability, and setting a protection time so that the screening operation and the deleting operation are not allowed to be executed within the protection time.
It will be appreciated that screening out atoms that meet the preset condition includes deleting redundant local track functions and screening local track functions.
The method for deleting the redundant local track function comprises the following steps: after each step of iteration updating parameters, checking parameters H of all local track functions, deleting the local track function if the parameters H of a local track function are smaller than a threshold value obtained by all local track function parameters, obtaining a local track function pair index of a local track function center from a preset pixel by establishing a binary tree, and deleting any local track function in the local track function pair.
It should be noted that the threshold may be set by a person skilled in the art according to the actual situation, or may be set to a reference value, for example, 0.01 times of the maximum value of all local orbit function parameters H; the preset pixels may be set by those skilled in the art according to actual situations, and may also be set to a reference value, such as 2 pixels.
The method for screening the local track function comprises the following steps: after each step of iteration is completed to update the parameters and delete the local track functions, the preset probability of the parameter H of each local track function is reduced to be a preset multiple, and the protection time is set, so that the local track functions cannot be screened and deleted within the protection time.
It should be noted that the preset probability and the preset multiple may be set by a person skilled in the art according to the actual situation, or may be set as a reference value, for example, the parameter H of each local track function is reduced to 0.1 times with a probability of 0.02; the protection event may be set by those skilled in the art according to the actual situation, and may also be set to a reference value, for example, the protection time t=50 is set.
The working principle of the three-dimensional reconstruction method of the local orbit function according to the embodiment of the present application will be described in detail with reference to fig. 2 to 9.
As shown in fig. 2, taking the example of reconstructing a small particle composed of 10000 atoms, the embodiment of the present application includes the following steps:
step S201: image data is acquired. As shown in fig. 3, the example of the present application requires reconstruction of small particles composed of 10000 atoms, and by tilting at angles of ±25°, ±20°, ±15°, ±5°, 0°, a simulated image thereof is obtained, and columns 1, 2, 3 in fig. 3 correspond to images of 25 °, 0 ° and-25 °, respectively.
Step S202: an image is calculated. According to the embodiment of the application, based on the image data, scattering points can be carried out at equal intervals in real space, and the calculated image under each tilting angle of a plurality of tilting angles can be obtained by linear accumulation. As shown in FIG. 4, the initial set of scattering points is a cylinder of equally spaced atoms, and as shown in FIG. 5, the formula is used And obtaining calculated images under each angle of iteration initiation, wherein the 1 st, 2 nd and 3 rd columns respectively correspond to the images of 25 degrees, 0 degrees and-25 degrees.
Step S203: and calculating a loss function, and carrying out iterative updating on the target parameters. Embodiments of the present application may calculate a loss functionAnd a gradient of the target parameter, and iteratively updating the target parameter using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Is the learning rate of each parameter.
Step S204: and deleting and screening the local track function. Further, in the embodiment of the present application, the local track function with the intensity H smaller than the intensity Hmax of the strongest local track function 0.01 may be deleted, and the local track function may be reset with a probability of 0.02, so that the intensity H of the local track function is reduced to 0.1 times of the original intensity H, and a protection period t=50 is set.
And circularly calculating a loss function, updating each target parameter, deleting and screening the operation of the local track function until the number of the local track functions is converged, and realizing three-dimensional coordinate reconstruction.
Wherein, the line graph of the value of the loss function along with the iterative process is shown in fig. 6; a schematic diagram of the atomic model obtained at the final convergence is shown in fig. 7; the difference value reference diagram of the calculated image and the experimental image is shown in fig. 8, wherein the 1 st column is the calculated diagram, the 2 nd column is the experimental diagram, the 3 rd column is the difference value of the calculated image and the experimental image, the 1 st row is the data under the condition that the inclination angle is 25 degrees, and the 2 nd row is the data under the condition that the inclination angle is 20 degrees; the calculated atomic coordinate and real coordinate distance histogram is shown in fig. 9.
According to the three-dimensional reconstruction method of the local orbit function, which is provided by the embodiment of the application, based on the collected image data of the sample under the plurality of inclination angles, a calculation image under the plurality of inclination angles is obtained, then a loss function of the calculation image is calculated, and the gradient of the optimization parameter is obtained, so that the optimization parameter is obtained, after repeated screening and calculation, until the loss function meets the convergence condition, a three-dimensional reconstruction result is obtained, the reconstruction process of the three-dimensional coordinate can be simplified, the requirement on hardware is reduced, complicated human intervention can be reduced, the labor cost is saved, meanwhile, in the iteration process, the sample drift and the mechanical inclination error of a sample table can be corrected, and the accuracy of the three-dimensional coordinate reconstruction is improved. Therefore, the technical problems that in the related art, only three-dimensional coordinates of atoms can be obtained from the reconstructed three-dimensional density matrix, errors cannot be corrected, the reconstruction process has high requirements on hardware, and the accuracy of the reconstructed three-dimensional coordinates is poor are solved.
Next, a three-dimensional reconstruction device of a local orbit function according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 10 is a block schematic diagram of a three-dimensional reconstruction device of a local orbit function according to an embodiment of the present application.
As shown in fig. 10, the three-dimensional reconstruction device 10 of a local orbit function includes: the system comprises an acquisition module 100, an accumulation module 200 and a reconstruction module 300.
Specifically, the acquisition module 100 is configured to acquire image data of a sample at a plurality of tilt angles.
The accumulation module 200 is configured to perform sprinkling at equal intervals in real space based on the image data and perform linear accumulation to obtain a calculated image at each of a plurality of tilt angles.
The reconstruction module 300 is configured to calculate a loss function according to the calculated image at each tilt angle, obtain a gradient of the loss function with respect to the parameter to be optimized, optimize the parameter to be optimized according to the gradient, screen out atoms meeting the preset condition, recalculate a new loss function until meeting the convergence condition, and reconstruct the three-dimensional space coordinates of the center of the local orbit function and the shape of the local orbit function in real space to obtain a three-dimensional reconstruction result.
Optionally, in one embodiment of the present application, the acquisition module 100 includes: an acquisition unit and a noise reduction unit.
Wherein, the acquisition unit is used for acquiring initial image data of the sample under a plurality of tilting angles.
And the noise reduction unit is used for carrying out centering shaft and noise reduction processing on the initial image data and normalizing the processed image to obtain image data.
Optionally, in one embodiment of the present application, the calculation formula of the loss function is:
wherein W represents a loss function, M represents the total number of tilting angles, j represents the serial number of the tilting angle, i represents the serial number of the local orbit function, P represents the side length of the image, u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, f j (u, v) represents the image calculated at the j-th angle, b j (u, v) represents an experimentally obtained image at the j-th angle.
Optionally, in one embodiment of the present application, the reconstruction module 300 includes: a detection unit and a protection unit.
The system comprises a detection unit, a parameter deletion unit, a local track function pair index detection unit and a local track function pair index detection unit, wherein the detection unit is used for detecting all local track functions after each step of iteration update of parameters, and the parameter deletion unit is used for deleting any local track function when detecting that the parameter of any local track function is smaller than a threshold value obtained by all local track function parameters, and simultaneously obtaining a local track function pair index with the center distance of the local track function smaller than a preset pixel by establishing a binary tree, and deleting any local track function in the local track function pair.
And the protection unit is used for reducing the parameters of each local track function to a preset multiple by the preset probability after the parameters are iteratively updated and the local track functions are deleted in each step, and setting the protection time so that the screening operation and the deleting operation are not allowed to be executed in the protection time.
Optionally, in an embodiment of the present application, the parameters to be optimized include at least one of three-dimensional space coordinates of a center of each local orbit function, parameters describing a shape thereof, three euler angles corresponding to each corner, drift of the sample at each angle, and mechanical tilting deviation of the sample stage.
It should be noted that the foregoing explanation of the embodiment of the three-dimensional reconstruction method of the local track function is also applicable to the three-dimensional reconstruction device of the local track function of the embodiment, and will not be repeated herein.
According to the three-dimensional reconstruction device of the local orbit function, based on the collected image data of the samples at the plurality of inclination angles, the calculated images at the plurality of inclination angles are obtained, the loss function of the calculated images is calculated, the gradient of the optimized parameters is obtained, and therefore the optimized parameters are obtained. Therefore, the technical problems that in the related art, only three-dimensional coordinates of atoms can be obtained from the reconstructed three-dimensional density matrix, errors cannot be corrected, the reconstruction process has high requirements on hardware, and the accuracy of the reconstructed three-dimensional coordinates is poor are solved.
Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
memory 1101, processor 1102, and a computer program stored on memory 1101 and executable on processor 1102.
The processor 1102 implements the three-dimensional reconstruction method of the local orbit function provided in the above-described embodiment when executing the program.
Further, the electronic device further includes:
a communication interface 1103 for communication between the memory 1101 and the processor 1102.
Memory 1101 for storing a computer program executable on processor 1102.
The memory 1101 may include a high-speed RAM memory or may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
If the memory 1101, the processor 1102, and the communication interface 1103 are implemented independently, the communication interface 1103, the memory 1101, and the processor 1102 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in FIG. 11, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 1101, the processor 1102, and the communication interface 1103 are integrated on a chip, the memory 1101, the processor 1102, and the communication interface 1103 may perform communication with each other through internal interfaces.
The processor 1102 may be a central processing unit (Central Processing Unit, abbreviated as CPU) or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC) or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the three-dimensional reconstruction method of a local orbit function as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (8)
1. A method for three-dimensional reconstruction of a local orbit function, comprising the steps of:
collecting image data of a sample at a plurality of inclination angles;
based on the image data, scattering points are carried out at equal intervals in real space, and linear accumulation is utilized to obtain a calculated image under each tilting angle of the plurality of tilting angles; and
calculating a loss function according to the calculated image under each inclination angle, acquiring the gradient of the loss function relative to a parameter to be optimized, optimizing the parameter to be optimized according to the gradient, screening out atoms meeting preset conditions, recalculating a new loss function until meeting convergence conditions, and obtaining a three-dimensional reconstruction result at the three-dimensional space coordinates of the center of the real space reconstruction local orbit function and the shape of the local orbit function, wherein the parameter to be optimized comprises at least one of the three-dimensional space coordinates of the center of each local orbit function, the parameter describing the shape, three Euler angles corresponding to each rotation angle, the drift of a sample under each angle and the mechanical inclination deviation of a sample table,
Wherein obtaining the gradient of the loss function with respect to the parameter to be optimized comprises: after the loss function is obtained, the three-dimensional coordinates (x i ,y i ,z i ) Strength H i Width B i Amount of sample drift at each angle (u j ,v j ) Angle deviation psi of sample stage j 、θ j 、And updating the target parameter with the gradient,
wherein, screening out atoms meeting preset conditions includes: after each step of iteration updating, detecting parameters of all local orbit functions;
when the parameters of any local track function are detected to be smaller than the threshold value obtained by the parameters of all the local track functions, deleting any local track function, and simultaneously, obtaining a local track function pair index with the center distance of the local track function smaller than a preset pixel by establishing a binary tree, and deleting any local track function in the local track function pair index;
after each step of iteration is completed to update parameters and delete the local track functions, reducing the parameters of each local track function to a preset multiple with a preset probability, and setting a protection time so that screening operation and deleting operation are not allowed to be executed within the protection time.
2. The method of claim 1, wherein acquiring image data of the sample at the plurality of tilt angles comprises:
acquiring initial image data of the sample at the plurality of tilt angles;
and performing centering shaft and noise reduction processing on the initial image data, and normalizing the processed image to obtain the image data.
3. The method of claim 1, wherein the loss function is calculated as:
wherein W represents a loss function, M represents the total number of tilting angles, j represents the serial number of the tilting angle, i represents the serial number of the local orbit function, P represents the side length of the image, u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, f j (u, v) represents the image calculated at the j-th angle, b j (u, v) represents an experimentally obtained image at the j-th angle.
4. A three-dimensional reconstruction device for a local orbit function, comprising:
the acquisition module is used for acquiring image data of the sample at a plurality of inclination angles;
the accumulation module is used for scattering points at equal intervals in real space based on the image data and utilizing linear accumulation to obtain a calculated image under each tilting angle of the plurality of tilting angles; and
A reconstruction module, configured to calculate a loss function according to the calculated image at each inclination angle, obtain a gradient of the loss function with respect to a parameter to be optimized, optimize the parameter to be optimized according to the gradient, screen out atoms meeting a preset condition, recalculate a new loss function until meeting a convergence condition, and obtain a three-dimensional reconstruction result at a three-dimensional space coordinate of a center of the real space reconstruction local orbit function and a shape of the local orbit function, where the parameter to be optimized includes at least one of a three-dimensional space coordinate of a center of each local orbit function, a parameter describing a shape of the three euler angles corresponding to each rotation angle, a drift of a sample at each angle, and a mechanical tilting deviation of a sample stage,
wherein obtaining the gradient of the loss function with respect to the parameter to be optimized comprises: after the loss function is obtained, the three-dimensional coordinates (x i ,y i ,z i ) Strength H i Width B i Amount of sample drift at each angle (u j ,v j ) Angle deviation psi of sample stage j 、θ j 、And updating the target parameter with the gradient,
wherein, screening out atoms meeting preset conditions includes: after each step of iteration updating, detecting parameters of all local orbit functions;
When the parameters of any local track function are detected to be smaller than the threshold value obtained by the parameters of all the local track functions, deleting any local track function, and simultaneously, obtaining a local track function pair index with the center distance of the local track function smaller than a preset pixel by establishing a binary tree, and deleting any local track function in the local track function pair index;
after each step of iteration is completed to update parameters and delete the local track functions, reducing the parameters of each local track function to a preset multiple with a preset probability, and setting a protection time so that screening operation and deleting operation are not allowed to be executed within the protection time.
5. The apparatus of claim 4, wherein the acquisition module comprises:
an acquisition unit configured to acquire initial image data of the sample at the plurality of tilt angles;
and the noise reduction unit is used for carrying out centering and noise reduction processing on the initial image data and normalizing the processed image to obtain the image data.
6. The apparatus of claim 4, wherein the loss function is calculated by the formula:
wherein W represents a loss function, M represents the total number of tilting angles, j represents the serial number of the tilting angle, i represents the serial number of the local orbit function, P represents the side length of the image, u represents the abscissa of each pixel in the image, v represents the ordinate of each pixel in the image, f j (u, v) represents the image calculated at the j-th angle, b j (u, v) represents an experimentally obtained image at the j-th angle.
7. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of three-dimensional reconstruction of a local track function as claimed in any one of claims 1 to 3.
8. A computer readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for implementing a method of three-dimensional reconstruction of a local track function as claimed in any one of claims 1-3.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111179339A (en) * | 2019-12-13 | 2020-05-19 | 深圳市瑞立视多媒体科技有限公司 | Coordinate positioning method, device and equipment based on triangulation and storage medium |
| WO2020098686A1 (en) * | 2018-11-16 | 2020-05-22 | 广州市百果园信息技术有限公司 | Face detection model training method and apparatus, and face key point detection method and apparatus |
| WO2021077720A1 (en) * | 2019-10-25 | 2021-04-29 | 深圳奥比中光科技有限公司 | Method, apparatus, and system for acquiring three-dimensional model of object, and electronic device |
| EP3866112A2 (en) * | 2020-11-30 | 2021-08-18 | Beijing Baidu Netcom Science And Technology Co. Ltd. | Method, apparatus, device, storage medium and program for three-dimensional reconstruction |
| CN114186191A (en) * | 2021-11-19 | 2022-03-15 | 合肥联宝信息技术有限公司 | Method, device and equipment for calculating coordinate transformation matrix and readable storage medium |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9613285B2 (en) * | 2012-03-22 | 2017-04-04 | The Charles Stark Draper Laboratory, Inc. | Compressive sensing with local geometric features |
| EP2951528B1 (en) * | 2013-01-29 | 2018-07-25 | Andrew Robert Korb | Methods for analyzing and compressing multiple images |
| JP6989387B2 (en) * | 2015-05-05 | 2022-01-05 | キンダイ、インコーポレイテッドKyndi, Inc. | Quanton representation for emulating quantum similarity computations in classical processors |
| US10049446B2 (en) * | 2015-12-18 | 2018-08-14 | Carestream Health, Inc. | Accelerated statistical iterative reconstruction |
| WO2019246397A1 (en) * | 2018-06-21 | 2019-12-26 | The University Of Chicago | A fully fourier space spherical convolutional neural network based on clebsch-gordan transforms |
| US10607164B1 (en) * | 2019-07-28 | 2020-03-31 | Zichu Wang | Architects space programming process that determines new classroom sizes and count for educational institutions |
| CN111208513B (en) * | 2020-01-15 | 2023-03-31 | 西安电子科技大学 | Space target ISAR image sequence energy back projection and three-dimensional reconstruction method |
| CN111507908B (en) * | 2020-03-11 | 2023-10-20 | 平安科技（深圳）有限公司 | Image correction processing method, device, storage medium and computer equipment |
| EP4136611A4 (en) * | 2020-05-18 | 2023-06-07 | Shanghai United Imaging Healthcare Co., Ltd. | Systems and methods for image reconstruction |
| FR3111067A1 (en) * | 2020-06-06 | 2021-12-10 | Olivier Querbes | Optical impression of a patient's dental arch |
| JP2021196568A (en) * | 2020-06-18 | 2021-12-27 | コニカミノルタ株式会社 | Image forming method and image forming system |
| CN111862316B (en) * | 2020-07-28 | 2024-01-05 | 杭州深瞳科技有限公司 | Three-dimensional reconstruction method of dense direct RGBD (Red Green blue-white) of tight coupling of IMU (inertial measurement Unit) based on optimization |
| CN113034681B (en) * | 2021-04-07 | 2022-05-03 | 清华大学 | Three-dimensional reconstruction method and device for spatial plane relation constraint |
| CN113720865B (en) * | 2021-08-06 | 2022-09-02 | 清华大学 | Electronic lamination imaging method and device for automatically correcting tape axis deviation of sample |
| CN114648611B (en) * | 2022-04-12 | 2023-07-18 | 清华大学 | Three-dimensional reconstruction method and device for local orbit function |
-
2022
- 2022-04-12 CN CN202210381325.3A patent/CN114648611B/en active Active
-
2023
- 2023-03-08 WO PCT/CN2023/080253 patent/WO2023197785A1/en unknown
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020098686A1 (en) * | 2018-11-16 | 2020-05-22 | 广州市百果园信息技术有限公司 | Face detection model training method and apparatus, and face key point detection method and apparatus |
| WO2021077720A1 (en) * | 2019-10-25 | 2021-04-29 | 深圳奥比中光科技有限公司 | Method, apparatus, and system for acquiring three-dimensional model of object, and electronic device |
| CN111179339A (en) * | 2019-12-13 | 2020-05-19 | 深圳市瑞立视多媒体科技有限公司 | Coordinate positioning method, device and equipment based on triangulation and storage medium |
| EP3866112A2 (en) * | 2020-11-30 | 2021-08-18 | Beijing Baidu Netcom Science And Technology Co. Ltd. | Method, apparatus, device, storage medium and program for three-dimensional reconstruction |
| CN114186191A (en) * | 2021-11-19 | 2022-03-15 | 合肥联宝信息技术有限公司 | Method, device and equipment for calculating coordinate transformation matrix and readable storage medium |
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