Wavefront aberration calculation method and device, electronic equipment and storage medium

Abstract

The application provides a wavefront aberration calculation method and device, electronic equipment and storage medium, and relates to the technical field of computers. The method comprises the following steps: determining a point source target, obtaining a corresponding image set based on the point source target, wherein the image set comprises a focal plane image and an out-of-focus image; calculating an equivalent real coefficient vector and an equivalent imaginary coefficient vector based on the image set; calculating a corresponding final solution Zernike coefficient vector based on the equivalent real coefficient vector and the equivalent imaginary coefficient vector, determining a Zernike polynomial, and calculating a wavefront aberration from the final solution Zernike coefficient vector and the Zernike polynomial. The application solves the problem of low efficiency of wavefront aberration calculation in the related art.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a wavefront aberration calculation method, apparatus, electronic device, and storage medium. Background Technology

[0002] Optical wavefront aberration detection plays a crucial role in various fields, such as wavefront detection and correction in astronomy, wavefront detection and correction in microscopy, wavefront aberration measurement in optical systems, beam wavefront shaping and detection, and wavefront aberration detection in human eye imaging. Among these, image-based optical wavefront aberration detection is an important method, possessing advantages such as simple system structure, ease of implementation, and strong anti-interference capabilities.

[0003] However, quickly and accurately solving for wavefront aberrations from images has always been a challenging problem. Traditional methods typically construct an objective evaluation function using imaging models and parameters, and then use gradient descent-based nonlinear optimization algorithms to search for the optimal solution. However, when the objective function is non-convex in the solution space, these gradient-based optimization algorithms are prone to getting trapped in local minima or saddle points, causing the solution to deviate significantly from the true value. Furthermore, the iterative solution process of nonlinear optimization algorithms consumes considerable computational time and resources. In recent years, deep learning methods have flourished, injecting strong momentum into image-based wavefront detection. Through deep learning network models, an effective mapping between images and wavefront aberrations can be directly established, avoiding the problems of getting trapped in local minima and excessively long solution times, thus improving both accuracy and speed. However, deep learning methods require a large amount of accurately labeled training data, which is often a daunting task for wavefront detection research. In addition, the interpretability of deep learning methods is often weak.

[0004] Existing technologies can combine traditional nonlinear optimization algorithms with neural networks, using the low-frequency Fourier spectrum coefficients of the image as the input of the neural network, and the wavefront aberration Zernike coefficients output by the neural network as the initial values ​​of the nonlinear optimization algorithm. Although this method can greatly reduce the risk of getting trapped in local extrema and improve the iteration convergence speed, it still requires the use of a nonlinear iterative optimization algorithm, resulting in limited processing speed and thus affecting computational efficiency. This situation needs further improvement. Summary of the Invention

[0005] This application provides a wavefront aberration calculation method, apparatus, electronic device, and storage medium, which can solve the problem of low efficiency in wavefront aberration calculation in related technologies. The technical solutions are as follows: According to one aspect of this application, a wavefront aberration calculation method includes: determining a point source target; obtaining a corresponding image set based on the point source target, wherein the image set includes a focal plane image and an out-of-focus image; calculating an equivalent real part coefficient vector and an equivalent imaginary part coefficient vector based on the image set; calculating a corresponding final solution Zernike coefficient vector based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, and determining a Zernike polynomial; and calculating the wavefront aberration by combining the final solution Zernike coefficient vector and the Zernike polynomial.

[0006] According to one aspect of this application, a wavefront aberration calculation apparatus includes: an image set acquisition module for determining a point source target and acquiring a corresponding image set based on the point source target, wherein the image set includes a focal plane image and an out-of-focus image; a coefficient vector calculation module for calculating an equivalent real part coefficient vector and an equivalent imaginary part coefficient vector based on the image set; and a wavefront aberration calculation module for calculating a corresponding final solution Zernike coefficient vector based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, determining a Zernike polynomial, and using the final solution Zernike coefficient vector and the Zernike polynomial to calculate the wavefront aberration.

[0007] In one exemplary embodiment, the apparatus includes, but is not limited to: The Fourier spectrum acquisition module performs two-dimensional Fourier transforms on both the focal plane image and the defocus image to obtain the corresponding two-dimensional Fourier spectrum of the focal plane. and defocused two-dimensional Fourier spectrum The focal plane two-dimensional Fourier spectrum and the defocused two-dimensional Fourier spectrum Composition of Fourier spectrum , ; The coefficient vector combination module selects the Fourier spectrum. The real and imaginary coefficients in the vector are used to combine them into a real coefficient vector. and the vector of imaginary coefficients , ; The real part coefficient vector calculation module is used to calculate the equivalent real part coefficient vector. The imaginary part coefficient vector calculation module is used to calculate the equivalent imaginary part coefficient vector. .

[0008] In one exemplary embodiment, the apparatus includes, but is not limited to: The Fourier spectrum determination module selects the two-dimensional Fourier spectrum of the focal plane. and the defocused two-dimensional Fourier spectrum The low-frequency portion is used to determine the Fourier spectrum. .

[0009] In one exemplary embodiment, the apparatus includes, but is not limited to: The real solution Zernike coefficient vector determination module obtains the real part solution matrix and multiplies the real part solution matrix by the equivalent real part coefficient vector to determine the real solution Zernike coefficient vector. The virtual solution Zernike coefficient vector determination module obtains the virtual part solution matrix and multiplies the virtual part solution matrix by the equivalent virtual part coefficient vector to determine the virtual solution Zernike coefficient vector; The final solution Zernike coefficient vector determination module determines the final solution Zernike coefficient vector based on the actual solution Zernike coefficient vector and the virtual solution Zernike coefficient vector.

[0010] In one exemplary embodiment, the apparatus includes, but is not limited to: The point spread function image acquisition module is used to acquire... Grouped spread function images, wherein each group of images includes a focal plane point spread function image. and the off-focus spread function image Furthermore, the wavefront aberration of the system during the capture of each set of images... different, ; The wavefront Zernike coefficient vector acquisition module obtains the spread function image for each group of points. and Constructing information about wavefront aberrations Objective evaluation function This is used to obtain the corresponding wavefront Zernike coefficient vector. ; The coefficient vector acquisition module is based on the point spread function image. and the point spread function image Used to obtain the corresponding equivalent real part coefficient vector And equivalent imaginary part coefficient vector , ; Zernike coefficient matrix construction module, based on Zernike coefficient vector obtained from grouped point spread function image , Based on the Zernike coefficient vector Used to construct the Zernike coefficient matrix; The equivalent real part coefficient matrix construction module is based on The equivalent real part coefficient vector of the point spread function image of the group Used to construct the equivalent real part coefficient matrix, where... The number of elements in the equivalent real part coefficient vector. ; Solving the real part of the matrix The determination module uses the equivalent real part coefficient matrix and the Zernike coefficient matrix to determine the real part solution matrix. .

[0011] In one exemplary embodiment, the apparatus includes, but is not limited to: The generalized inverse matrix calculation module is used to calculate the equivalent real part coefficient matrix. generalized inverse matrix ; The real part solution matrix calculation module is used to calculate the real part solution matrix. .

[0012] In one exemplary embodiment, the apparatus includes, but is not limited to: The wavefront aberration Zernike coefficient vector determination module calculates the minimum value of the target evaluation function based on a nonlinear optimization algorithm, and uses the Zernike coefficient vector corresponding to the minimum value of the target evaluation function to determine the wavefront aberration Zernike coefficient vector. .

[0013] According to one aspect of this application, an electronic device includes at least one processor and at least one memory, wherein computer-readable instructions are stored on the memory; the computer-readable instructions are executed by one or more of the processors to cause the electronic device to implement the wavefront aberration calculation method as described above.

[0014] According to one aspect of this application, a storage medium stores computer-readable instructions thereon, which are executed by one or more processors to implement the wavefront aberration calculation method as described above.

[0015] According to one aspect of this application, a computer program product includes computer-readable instructions stored in a storage medium, wherein one or more processors of an electronic device read the computer-readable instructions from the storage medium, load and execute the computer-readable instructions, causing the electronic device to implement the wavefront aberration calculation method as described above.

[0016] The beneficial effects of the technical solution provided in this application are as follows: By linearizing the model relationship between the low-frequency Fourier coefficients of the approximate point spread function image and the Zernike coefficients of the wavefront aberration, the Zernike coefficients of the wavefront aberration are directly calculated from the low-frequency Fourier coefficients of the point spread function image. Only two images, one of the focal plane and one of the off-focal point spread function, are required, making the operation easy to implement. During processing, only a two-dimensional Fourier transform and simple matrix operations are needed, with a computation time of only a few milliseconds, allowing for almost real-time problem solving. The processing speed is extremely fast, and when dealing with wavefront aberrations with small-scale variations, it achieves accuracy comparable to current mainstream methods, thus without degradation in accuracy. Furthermore, while maintaining solution accuracy, it accelerates processing speed, thereby improving computational efficiency.

[0017] In the above technical solution, by determining the point source target, obtaining the corresponding image set based on the point source target, calculating the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector based on the image set, and finally calculating the corresponding final solution Zernike coefficient vector based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, and determining the Zernike polynomial, the wavefront aberration is calculated using the final solution Zernike coefficient vector and the Zernike polynomial. This method can directly and accurately calculate and determine optical wavefront aberrations, and while ensuring solution accuracy, it can also accelerate processing speed, thereby improving computational efficiency. Therefore, it effectively solves the problem of low efficiency in wavefront aberration calculation in related technologies. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram based on the implementation environment involved in this application; Figure 2 This is a flowchart illustrating a wavefront aberration calculation method according to an exemplary embodiment; Figure 3 This is a flowchart of steps S111 to S113 in a wavefront aberration calculation method according to an exemplary embodiment; Figure 4 This is a flowchart of steps S121 to S123 in a wavefront aberration calculation method according to an exemplary embodiment; Figure 5 This is a flowchart of steps S1211 to S1216 in a wavefront aberration calculation method according to an exemplary embodiment; Figure 6 This is a structural block diagram of a wavefront aberration calculation device according to an exemplary embodiment; Figure 7 This is a structural block diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0021] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this disclosure means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0022] As mentioned earlier, combining traditional nonlinear optimization algorithms with neural networks, using the low-frequency Fourier spectrum coefficients of the image as the input of the neural network, and the wavefront aberration Zernike coefficients output by the neural network as the initial values ​​of the nonlinear optimization algorithm, can greatly reduce the risk of getting trapped in local extrema and improve the iteration convergence speed. However, it still requires the use of a nonlinear iterative optimization algorithm, resulting in limited processing speed and thus affecting computational efficiency. As can be seen from the above, the related technologies still have the defect of low accuracy in wavefront aberration calculation.

[0023] Therefore, the wavefront aberration calculation method provided in this application can effectively improve the accuracy of wavefront aberration calculation. Accordingly, this wavefront aberration calculation method is applicable to wavefront aberration calculation devices, which can be deployed in electronic devices. To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0024] Figure 1This is a schematic diagram of the implementation environment involved in a wavefront aberration calculation method. The implementation environment includes terminals, servers, and a service system configured with a member association database.

[0025] Specifically, the terminal can be run by a client that provides data computing, and can be an electronic device such as a desktop computer, laptop computer, tablet computer, or smartphone, without any limitation.

[0026] A server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. This server is an electronic device used to provide backend services; for example, in this implementation environment, the server provides cloud storage services for data computation to the terminal.

[0027] The server establishes a communication connection with the service system in advance via wired or wireless means, and achieves interaction with the service system through this communication connection. The service system can be a single server or a server cluster consisting of multiple servers.

[0028] Through the interaction between the terminal and the server, the client running on the terminal will send a resource usage invitation to the server, requesting the server to determine the location of the resource and the time of resource usage through resource allocation, and then issue the invitation accordingly.

[0029] For the server, the resource usage invitation linkage service system performs the resource allocation process for the client of the inviter and returns the invitation result, which indicates the location of the resource and the time of resource usage, to the client of the inviter so that the client of the inviter can further confirm whether to send the resource usage invitation to the client of the invitee.

[0030] Of course, depending on actual operational needs, servers and service systems can also be integrated into the same server cluster so that resource allocation is handled by that same server cluster.

[0031] Please see Figure 2 This application provides a wavefront aberration calculation method, which is applicable to electronic devices, such as… Figure 1 The server in the implementation environment is shown.

[0032] In the following method embodiments, for ease of description, the execution subject of each step of the method is an electronic device, but this does not constitute a specific limitation.

[0033] like Figure 2As shown, the method may include the following steps: S100, determine the point source target, and obtain the corresponding image set based on the point source target; The image set includes focal plane images. and out-of-focus images And focal plane image This refers to the situation where the target surface of the imaging detector is located at the focal plane of the imaging system, resulting in an out-of-focus image. This means that the target surface of the imaging detector is not at the focal plane of the imaging system at this time, but is offset by a certain distance.

[0034] It is important to emphasize that the point source target here does not necessarily require the target itself to be very small, but rather that the image formed by the imaging system is very small. For example, when observing stars with an astronomical telescope, the actual size of the star may be very large, but it appears as only a small dot in the image formed by the telescope.

[0035] Focal plane image and out-of-focus images It can be generated in various ways, including but not limited to the following: by moving the target surface of the imaging detector to positions both at and outside the focal plane, thereby acquiring a focal plane image. and out-of-focus images Two independent imaging detectors are used, one with the target surface located at the focal plane and the other with the target surface located outside the focal plane, to obtain a focal plane image. and out-of-focus images Different levels of light beams can be generated using optical elements such as gratings, each used to acquire a focal plane image. and out-of-focus images .

[0036] Furthermore, the defocus distance can be optimized in the following ways: The peak-to-peak value (PV) of wavelet aberration caused by defocus is , For imaging light wavelength, In theory, The smaller the value, the smaller the approximation error of the linear model, and the higher the accuracy of wavefront aberration solving; however, in practice, due to the influence of image acquisition errors... If it's too small, it will cause the image to go out of focus. and focal plane image The differences between them are not significant, thus reducing the solution accuracy. Therefore, The values ​​should be balanced.

[0037] S110, calculates the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector based on the image set; Among them, such as Figure 3As shown, the specific calculation process is as follows: S111, focal plane image and out-of-focus images All samples undergo a two-dimensional Fourier transform to obtain the corresponding two-dimensional Fourier spectrum of the focal plane. and defocused two-dimensional Fourier spectrum Among them, the two-dimensional Fourier spectrum of the focal plane and defocused two-dimensional Fourier spectrum Composition of Fourier spectrum , .

[0038] It should be noted here that the two-dimensional Fourier spectrum of the focal plane is selected. and the defocused two-dimensional Fourier spectrum The low-frequency portion of the spectrum is used to determine the Fourier spectrum. Performing a two-dimensional Fourier transform on an image generates its two-dimensional Fourier spectrum. The zero-frequency Fourier spectrum represents the image's average gray level. The low-frequency Fourier spectrum, close to the zero-frequency spectrum, contains the image's basic outline and overall structural information, while the high-frequency Fourier spectrum, far from the zero-frequency spectrum, contains the image's details and texture information. "Taking the low-frequency portion of the two-dimensional Fourier spectrum" means selecting a small region containing the zero-frequency spectrum and centered on it. This region can be circular, square, rectangular, etc. Further optimization can be used to select the low-frequency Fourier spectrum: assuming the highest frequency of the image's Fourier spectrum is 1, then the highest frequency of the selected region's Fourier spectrum is... , .

[0039] The Fourier spectrum is a complex number, consisting of a real part and an imaginary part. The coefficients of the real and imaginary parts of the low-frequency Fourier spectrum are called the low-frequency Fourier coefficients. Combining the low-frequency Fourier spectrum into a vector of real and imaginary coefficients means arranging the low-frequency Fourier coefficients into a column vector in a specific order. Below is an example. The low-frequency Fourier spectrum is... A matrix, whose elements are complex numbers, where Take the coefficients of the real and imaginary parts of each element and rearrange them into two... The column vectors are the real part coefficient vector and the imaginary part coefficient vector, respectively.

[0040]

[0041] S112, Select the Fourier spectrum The real and imaginary coefficients in the equation are combined to form a vector of real coefficients. and the vector of imaginary coefficients , ; S113, Calculate the equivalent real part coefficient vector Calculate the equivalent imaginary part coefficient vector .

[0042] By executing S111 to S113, the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector can be calculated.

[0043] S120: Calculate the corresponding final solution Zernike coefficient vector based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, determine the Zernike polynomial, and calculate the wavefront aberration using the final solution Zernike coefficient vector and the Zernike polynomial.

[0044] First, in the process of calculating the final solution Zernike coefficient vector, as follows: Figure 4 As shown, the method also includes the following process: S121, obtain the real part solution matrix, and multiply the real part solution matrix by the equivalent real part coefficient vector to determine the real solution Zernike coefficient vector; Among them, such as Figure 5 As shown, the calculation process for solving the real part matrix is ​​as follows: S1211, obtain Grouped spread function images, wherein each group of images includes a focal plane point spread function image. and the off-focus spread function image Furthermore, the wavefront aberrations during the capture of each set of images different, .

[0045] S1212, for each group of point spread function images and Constructing information about wavefront aberrations Objective evaluation function Obtain the corresponding wavefront Zernike coefficient vector. ; It should be noted that in determining the wavefront Zernike coefficient vector, a nonlinear optimization algorithm is used to calculate the minimum value of the objective evaluation function. The Zernike coefficient vector corresponding to the minimum value of the objective evaluation function is then determined as the wavefront aberration Zernike coefficient vector. Specifically, for each random Zernike coefficient vector, the nonlinear optimization algorithm iteratively calculates the value of the objective evaluation function and the corresponding Zernike coefficient vector after the calculation is completed. Among all random Zernike coefficient vectors, the Zernike coefficient vector corresponding to the minimum value of the objective evaluation function is considered the final solution. Preferably, Nonlinear optimization algorithms include gradient descent (GD), conjugate gradient (CG), Newton's method, and quasi-Newton methods. The Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm is a quasi-Newton method, possessing advantages such as low memory requirements, fast convergence speed, and good stability. Furthermore, as a second-order gradient algorithm, its accuracy is often higher than that of first-order gradient algorithms such as gradient descent and conjugate gradient.

[0046] S1213, Based on point spread function image and the point spread function image Obtain the corresponding equivalent real part coefficient vector And equivalent imaginary part coefficient vector , ; The equivalent real coefficient vector has been proposed earlier. And equivalent imaginary part coefficient vector The calculation method will not be elaborated here.

[0047] S1214, based on Zernike coefficient vector obtained from grouped point spread function image , And based on the Zernike coefficient vector Constructing the Zernike coefficient matrix .

[0048] S1215, based on Equivalent real part coefficient vector of the grouped spread function graph Construct the equivalent real coefficient matrix ,in, The number of elements in the equivalent real part coefficient vector. .

[0049] S1216, Determining the real part solution matrix based on the equivalent real part coefficient matrix and the Zernike coefficient matrix. ; Among them, the matrix is ​​solved by calculating the real part. When doing so, it is necessary to first calculate the equivalent real part coefficient matrix. generalized inverse matrix Then the real part solution matrix can be calculated. .

[0050] Solving the matrix in the real part Then, the equivalent real part coefficient vector can be retrieved. By solving the matrix by finding the real part With the equivalent real part coefficient vector Multiplying them together yields the Zernike coefficient vector of the real solution: .

[0051] S122, Obtain the imaginary part solution matrix The virtual solution Zernike coefficient vector is determined by multiplying the virtual part solution matrix by the equivalent virtual part coefficient vector. Among them, the imaginary part is solved by the matrix. The solution process and the real part coefficient matrix The solution process is consistent, utilizing Construct an equivalent imaginary coefficient matrix from the equivalent imaginary coefficient vector of the grouped point spread function image. , The number of elements in the equivalent imaginary coefficient vector, and the equivalent imaginary coefficient matrix. Similarly, in calculating the imaginary part of the matrix... When doing so, it is necessary to first calculate the equivalent imaginary part coefficient matrix. generalized inverse matrix Then the imaginary part solution matrix can be calculated. By retrieving the equivalent imaginary part coefficient vector Solve the imaginary part of the matrix With the equivalent imaginary part coefficient vector Multiplication yields the virtual solution Zernike coefficient vector: .

[0052] S123, determine the final solution Zernike coefficient vector based on the real solution Zernike coefficient vector and the virtual solution Zernike coefficient vector.

[0053] From the real solution Zernike coefficient vector respectively and the virtual solution Zernike coefficient vector Select reliable coefficient elements and combine them into the final solution Zernike coefficient vector. .

[0054] set up , , The number of elements in the Zernike coefficient vector. Represents the transpose of a vector; constructs a selection vector. The Zernike coefficient vector has a final solution. The symbol '.' represents the dot product operation, and vectors are selected. The element is defined as follows:

[0055] The above It refers to Zernike polynomials.

[0056] Wavefront aberrations can be represented by a linear combination of a series of orthogonal polynomials. Zernike polynomials are a set of functions defined in the unit circle, which are orthogonal to each other in the circular domain and have simple invariance properties. They are widely used in wavefront aberration analysis. The coefficients of the linear combination of Zernike polynomials are called Zernike coefficients.

[0057] Generally, a finite number of Zernike polynomials are used to fit the wavefront aberrations, for example, the first 1 to 15 terms, and the fitting coefficients form a Zernike coefficient vector. Wavefront aberration can be expressed as , For Zernike polynomials, when Symmetric about the origin (center of the unit circle), i.e. Define the elements of the selection vector. Otherwise, define .

[0058] Multiplying and summing the final solution Zernike coefficients with the corresponding Zernike polynomials yields the reconstructed wavefront aberrations. Let the final solution Zernike coefficient vector be... There is wavefront aberration. .

[0059] The following are embodiments of the apparatus described in this application, which can be used to execute the wavefront aberration calculation method involved in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of the wavefront aberration calculation method involved in this application.

[0060] Please see Figure 6 This application provides a wavefront aberration calculation device, including but not limited to: The image set acquisition module 200 determines the point source target and acquires the corresponding image set based on the point source target, wherein the image set includes focal plane images and defocus images; The coefficient vector calculation module 210 is used to calculate the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector based on the image set; The wavefront aberration calculation module 220 calculates the corresponding final solution Zernike coefficient vector based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, and determines the Zernike polynomial. The final solution Zernike coefficient vector and the Zernike polynomial are then used to calculate the wavefront aberration.

[0061] In one exemplary embodiment, the apparatus includes, but is not limited to: The Fourier spectrum acquisition module 300 performs two-dimensional Fourier transform on both the focal plane image and the defocus image to obtain the corresponding two-dimensional Fourier spectrum of the focal plane. and defocused two-dimensional Fourier spectrum The focal plane two-dimensional Fourier spectrum and the defocused two-dimensional Fourier spectrum Composition of Fourier spectrum , ; The coefficient vector combination module 310 selects the Fourier spectrum. The real and imaginary coefficients in the vector are used to combine them into a real coefficient vector. and the vector of imaginary coefficients , ; The real part coefficient vector calculation module 320 is used to calculate the equivalent real part coefficient vector. The imaginary part coefficient vector calculation module is used to calculate the equivalent imaginary part coefficient vector. .

[0062] In one exemplary embodiment, the apparatus includes, but is not limited to: Fourier spectrum determination module 400 selects the two-dimensional Fourier spectrum of the focal plane. and the defocused two-dimensional Fourier spectrum The low-frequency portion is used to determine the Fourier spectrum. .

[0063] In one exemplary embodiment, the apparatus includes, but is not limited to: The real solution Zernike coefficient vector determination module 500 obtains the real part solution matrix and multiplies the real part solution matrix by the equivalent real part coefficient vector to determine the real solution Zernike coefficient vector. The virtual solution Zernike coefficient vector determination module 510 obtains the virtual part solution matrix and multiplies the virtual part solution matrix by the equivalent virtual part coefficient vector to determine the virtual solution Zernike coefficient vector. The final solution Zernike coefficient vector determination module 520 is used to determine the final solution Zernike coefficient vector based on the actual solution Zernike coefficient vector and the virtual solution Zernike coefficient vector.

[0064] In one exemplary embodiment, the apparatus includes, but is not limited to: Point spread function image acquisition module 600, used to acquire Grouped spread function images, wherein each group of images includes a focal plane point spread function image. and the off-focus spread function image Furthermore, the wavefront aberration of the system during the capture of each set of images... different, ; The wavefront Zernike coefficient vector acquisition module 610 obtains the spread function image for each group of points. and Constructing information about wavefront aberrations Objective evaluation function This is used to obtain the corresponding wavefront Zernike coefficient vector. ; The coefficient vector acquisition module 620, based on the point spread function image, and the point spread function image Used to obtain the corresponding equivalent real part coefficient vector And equivalent imaginary part coefficient vector , ; Zernike coefficient matrix construction module 630, based on Zernike coefficient vector obtained from grouped point spread function image , Based on the Zernike coefficient vector Used to construct the Zernike coefficient matrix; Equivalent real part coefficient matrix construction module 640, based on The equivalent real part coefficient vector of the point spread function image of the group Used to construct the equivalent real part coefficient matrix, where... The number of elements in the equivalent real part coefficient vector. ; Solving the real part of the matrix The determination module 650 determines the real part solution matrix based on the equivalent real part coefficient matrix and the Zernike coefficient matrix. .

[0065] In one exemplary embodiment, the apparatus includes, but is not limited to: The generalized inverse matrix calculation module 700 is used to calculate the equivalent real part coefficient matrix. generalized inverse matrix ; The real part solution matrix calculation module 710 is used to calculate the real part solution matrix. .

[0066] In one exemplary embodiment, the apparatus includes, but is not limited to: The wavefront aberration Zernike coefficient vector determination module 800 calculates the minimum value of the target evaluation function based on a nonlinear optimization algorithm, and determines the Zernike coefficient vector corresponding to the minimum value of the target evaluation function as the wavefront aberration Zernike coefficient vector. .

[0067] It should be noted that the wavefront aberration calculation device provided in the above embodiments is only illustrated by the division of the above functional modules when performing wavefront aberration calculations. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the wavefront aberration calculation device will be divided into different functional modules to complete all or part of the functions described above.

[0068] Furthermore, the wavefront aberration calculation device and wavefront aberration calculation method provided in the above embodiments belong to the same concept, and the specific way in which each module performs its operation has been described in detail in the method embodiments, and will not be repeated here.

[0069] Please see Figure 7 This application provides an electronic device 4000, which may include: a desktop computer, a laptop computer, a server, etc.

[0070] exist Figure 7 In this context, the electronic device 4000 includes at least one processor 4001 and at least one memory 4003.

[0071] The data interaction between the processor 4001 and the memory 4003 can be achieved through at least one communication bus 4002. This communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0072] Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0073] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0074] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program instructions or code in the form of instructions or data structures and accessible by the electronic device 400, but not limited thereto.

[0075] The memory 4003 stores computer-readable instructions, and the processor 4001 can read the computer-readable instructions stored in the memory 4003 through the communication bus 4002.

[0076] The computer-readable instructions are executed by one or more processors 4001 to implement the wavefront aberration calculation methods in the above embodiments.

[0077] Furthermore, this application provides a storage medium storing computer-readable instructions that are executed by one or more processors to implement the wavefront aberration calculation method described above.

[0078] This application provides a computer program product including computer-readable instructions stored in a storage medium. One or more processors of an electronic device read the computer-readable instructions from the storage medium, load and execute the computer-readable instructions, so that the electronic device implements the wavefront aberration calculation method as described above.

[0079] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0080] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for calculating wavefront aberrations, characterized in that, include: Determine the point source target, and obtain the corresponding image set based on the point source target, wherein the image set includes focal plane images and defocus images; Calculate the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector based on the image set; The Zernike coefficient vector of the final solution is calculated based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, and the Zernike polynomial is determined. The wavefront aberration is calculated based on the Zernike coefficient vector of the final solution and the Zernike polynomial. Both the focal plane image and the defocused image are subjected to two-dimensional Fourier transform to obtain the corresponding two-dimensional Fourier spectrum of the focal plane. and defocused two-dimensional Fourier spectrum The focal plane two-dimensional Fourier spectrum and the defocused two-dimensional Fourier spectrum Composition of Fourier spectrum , ; Select the Fourier spectrum The real and imaginary coefficients in the equation are combined to form a vector of real coefficients. and the vector of imaginary coefficients , ; Calculate the equivalent real part coefficient vector Calculate the equivalent imaginary part coefficient vector ; In the process of calculating the corresponding final solution Zernike coefficient vector based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, the method further includes: Retrieve the real part solution matrix, and multiply the real part solution matrix by the equivalent real part coefficient vector to determine the real solution Zernike coefficient vector; Retrieve the imaginary part solution matrix, and multiply the imaginary part solution matrix by the equivalent imaginary part coefficient vector to determine the imaginary solution Zernike coefficient vector; The final solution Zernike coefficient vector is determined based on the real solution Zernike coefficient vector and the virtual solution Zernike coefficient vector.

2. The method as described in claim 1, characterized in that, The two-dimensional Fourier spectrum of the focal plane and the defocused two-dimensional Fourier spectrum Composition of Fourier spectrum The method further includes: Select the two-dimensional Fourier spectrum of the focal plane and the defocused two-dimensional Fourier spectrum The low-frequency portion of the spectrum is used to determine the Fourier spectrum. .

3. The method as described in claim 1, characterized in that, The real part of the matrix is ​​retrieved for solution. During the process, the method further includes: Get Grouped spread function images, wherein each group of images includes a focal plane point spread function image. and the off-focus spread function image Furthermore, the wavefront aberration of the system during the capture of each set of images... different, ; For each set of point spread function images and Constructing information about wavefront aberrations Objective evaluation function Obtain the corresponding wavefront Zernike coefficient vector. ; Based on the point spread function image and the point spread function image Obtain the corresponding equivalent real part coefficient vector ; based on Obtaining the Zernike coefficient vector from the grouped point spread function image , Based on the Zernike coefficient vector Construct the Zernike coefficient matrix; based on The equivalent real part coefficient vector of the point spread function image of the group Construct an equivalent real part coefficient matrix, where, The number of elements in the equivalent real part coefficient vector. ; The real part solution matrix is ​​determined based on the equivalent real part coefficient matrix and the Zernike coefficient matrix. .

4. The method as described in claim 3, characterized in that, The real part solution matrix is ​​determined based on the equivalent real part coefficient matrix and the Zernike coefficient matrix. During the process, the method further includes: Calculate the equivalent real part coefficient matrix. generalized inverse matrix ; Calculate the real part solution matrix .

5. The method as described in claim 4, characterized in that, The corresponding wavefront Zernike coefficient vector is obtained. During the process, the method further includes: It calculates the minimum value of the objective evaluation function based on a nonlinear optimization algorithm, and determines the Zernike coefficient vector corresponding to the minimum value of the objective evaluation function as the wavefront aberration Zernike coefficient vector. .

6. A wavefront aberration calculation device, characterized in that, include: The image set acquisition module determines the point source target and acquires the corresponding image set based on the point source target, wherein the image set includes focal plane images and defocus images; The coefficient vector calculation module is used to calculate the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector based on the image set; The wavefront aberration calculation module calculates the corresponding final solution Zernike coefficient vector based on the equivalent real part coefficient vector and the equivalent imaginary part coefficient vector, and determines the Zernike polynomial. The wavefront aberration is then calculated based on the final solution Zernike coefficient vector and the Zernike polynomial. The Fourier spectrum acquisition module performs two-dimensional Fourier transforms on both the focal plane image and the defocus image to obtain the corresponding two-dimensional Fourier spectrum of the focal plane. and defocused two-dimensional Fourier spectrum The focal plane two-dimensional Fourier spectrum and the defocused two-dimensional Fourier spectrum Composition of Fourier spectrum , ; The coefficient vector combination module selects the Fourier spectrum. The real and imaginary coefficients in the vector are used to combine them into a real coefficient vector. and the vector of imaginary coefficients , ; The real part coefficient vector calculation module is used to calculate the equivalent real part coefficient vector. The imaginary part coefficient vector calculation module is used to calculate the equivalent imaginary part coefficient vector. ; The real solution Zernike coefficient vector determination module obtains the real part solution matrix and multiplies the real part solution matrix by the equivalent real part coefficient vector to determine the real solution Zernike coefficient vector. The virtual solution Zernike coefficient vector determination module obtains the virtual part solution matrix and multiplies the virtual part solution matrix by the equivalent virtual part coefficient vector to determine the virtual solution Zernike coefficient vector. The final solution Zernike coefficient vector determination module determines the final solution Zernike coefficient vector based on the actual solution Zernike coefficient vector and the virtual solution Zernike coefficient vector.

7. An electronic device, characterized in that, include: At least one processor and at least one memory, wherein, The memory stores computer-readable instructions; The computer-readable instructions are executed by one or more of the processors, causing the electronic device to implement the wavefront aberration calculation method as described in any one of claims 1 to 5.

8. A storage medium having computer-readable instructions stored thereon, characterized in that, The computer-readable instructions are executed by one or more processors to implement the wavefront aberration calculation method as described in any one of claims 1 to 5.

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