A computational phase imaging method and system

Abstract

The present invention provides a computational phase imaging method and system. This method collects the noise-containing far-field diffracted light intensity after an illumination beam impinges on a sample. It then uses coherent diffraction imaging to iteratively reconstruct the complex amplitude information of the sample and illumination beam. A dynamic activation function is applied to the iterative process to suppress noise perturbations on computational convergence, enabling the system to escape local optimal traps and ultimately achieve a globally optimal solution. This method addresses technical issues with existing phase computational imaging methods, such as severe loss of imaging resolution and algorithmic non-convergence in the presence of noise.

Description

Technical Field

[0001] The present invention relates to the field of computational imaging technology, and more particularly to a computational phase imaging method and system. Background Art

[0002] Computational microscopy is a crucial advanced imaging method in fields such as advanced manufacturing, precision measurement, and biomedicine. Computational imaging fully exploits the multidimensional optical information of light interacting with matter, including spectrum, phase, polarization, coherence, and diffraction. Leveraging numerical calculation methods and ultrafast computer computing capabilities, it enables rapid and precise computational imaging of illumination and samples. Computational imaging methods surpass the diffraction limit of conventional light-field microscopes at numerical aperture, enabling high-precision, high-speed computational microscopy.

[0003] In practical applications, the raw sampled data used for computational imaging is contaminated by a variety of disturbance noises, including sampling noise from optical detectors, positional perturbations of the sample stage, and errors in the stable pointing of the illumination source. This severely compromises the resolution and efficiency of computational imaging. In recent years, many advanced computational imaging methods have been proposed and garnered widespread scientific attention, such as stacked diffraction imaging algorithms and X-ray tomography algorithms. These computational imaging methods exhibit a certain degree of robustness against weak sampling noise, but in complex noise scenarios, they often struggle to converge to a global optimal solution. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a computational phase imaging method and system. By applying a dynamic activation function to the iterative update process of computational imaging, the convergence robustness of the algorithm in complex noise scenarios is improved, the ability of the algorithm to escape the local optimal trap is enhanced, and convergence to the global optimum is accelerated, thereby solving the technical problems of low noise robustness and poor convergence accuracy of existing computational imaging algorithms.

[0005] According to a first aspect of the present invention, there is provided a computational phase imaging method, comprising:

[0006] Step 1, based on the illumination beam and the current position of the sample to be measured at which the illumination beam is incident, obtaining the near-field outgoing wave distribution and the far-field diffraction light intensity distribution of the sample;

[0007] Step 2, based on the far-field diffraction light intensity distribution, iteratively solve the near-field outgoing wave distribution, and obtain the sample function and illumination function of the current position to be measured after each iterative update;

[0008] Step 3: Calculate the incremental gradient residual of the outgoing wave after each iterative update based on the sample function and illumination function of the current position to be measured after each iterative update. ,when If it is less than the set threshold, go to step 4; otherwise, go to step 2;

[0009] Step 4: Optimize the sample function and illumination function of the current position to be measured after iterative update obtained in step 2 based on the dynamic regularization function. Repeat steps 2 to 4 based on the optimized sample function and illumination function until the incremental gradient residual of the outgoing wave is less than 0. If the mean square error (MSE) between the iteratively updated far-field diffraction intensity distribution and the far-field diffraction intensity distribution in step 1 is less than a set threshold, the iteration is terminated, and the sample function and illumination function of the current position to be measured are obtained.

[0010] According to a second aspect of the present invention, there is provided a computational phase imaging system, comprising:

[0011] An acquisition module, configured to acquire a near-field outgoing wave distribution and a far-field diffraction light intensity distribution of the sample based on the illumination beam and the current position to be measured at which the illumination beam is incident on the sample;

[0012] An iterative solution module, configured to iteratively solve the near-field outgoing wave distribution based on the far-field diffraction light intensity distribution, and obtain a sample function and an illumination function at a current position to be measured after each iterative update;

[0013] A calculation module is used to calculate the incremental gradient residual of the outgoing wave after each iterative update based on the sample function and illumination function of the current position to be measured after each iterative update. ;

[0014] Judgment module, used when When the value is less than the set threshold, the optimization module is executed; otherwise, the iterative solution module is executed;

[0015] The optimization module optimizes the sample function and the illumination function of the current position to be measured after the iterative update obtained by the iterative update module based on the dynamic regularization function, and repeatedly executes the iterative solution module, the calculation module and the judgment module based on the optimized sample function and illumination function until the incremental gradient residual of the outgoing wave is less than 0. If the mean square error (MSE) of the iteratively updated far-field diffraction intensity distribution and the far-field diffraction intensity distribution in step 1 is less than the set threshold, the iteration is terminated, and the sample function and illumination function of the current position to be measured are obtained.

[0016] According to a third aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the processor is configured to implement the steps of the computational phase imaging method when executing a computer management program stored in the memory.

[0017] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer management program is stored. When the computer management program is executed by a processor, the steps of the computational phase imaging method are implemented.

[0018] The present invention provides a computational phase imaging method and system that collects the noise-containing far-field diffracted light intensity after an illumination beam impinges on a sample. It then uses coherent diffraction imaging to iteratively reconstruct the complex amplitude information of the sample and illumination beam. A dynamic activation function is applied to the iterative process to suppress noise perturbations on computational convergence, enabling the system to escape local optimality traps and ultimately achieve a globally optimal solution. This method addresses technical issues with existing phase computational imaging methods, such as severe loss of imaging resolution and algorithmic non-convergence in the presence of noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a computational phase imaging method provided by the present invention;

[0020] Figure 2 Schematic diagram of the illumination beam incident collection device;

[0021] Figure 3 Schematic diagram of the solution results of sample function and illumination function;

[0022] Figure 4 A schematic structural diagram of a computational phase imaging system provided by the present invention;

[0023] Figure 5 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0024] Figure 6 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention.

[0025] In the accompanying drawings, the names represented by the reference numerals are as follows:

[0026] 201. Illumination beam, 202. Sample, 203. Near-field outgoing wave distribution, 204. Fraunhofer far-field diffraction, 205. Far-field diffraction light intensity distribution, 301. Amplitude of sample function, 302. Phase of sample function, 303. Amplitude of illumination function, 304. Phase of illumination function. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention can be arbitrarily combined with each other to form a feasible technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0028] Based on the technical problems of low noise robustness and poor convergence accuracy of computational imaging algorithms in the prior art, the present invention proposes a computational phase imaging method, see Figure 1 , the computational phase imaging method mainly includes the following steps:

[0029] Step 1: The illumination beam is incident on the sample to be measured j , obtain the near-field outgoing wave distribution of the sample and far-field diffraction intensity distribution , j represents the serial number of the position to be measured, j = 1, 2, ..., J, J is a natural number.

[0030] As an example, in step 1, the illumination beam is incident on the current position of the sample to be measured. j , obtain the near-field outgoing wave distribution of the sample And collect the far-field diffraction intensity distribution of the sample , including: based on the current position of the sample to be tested j Sample function of and lighting functions , obtain the near-field outgoing wave distribution of the sample at the current position to be measured , ;Collect far-field diffraction light intensity distribution based on Fraunhofer diffraction method .

[0031] See Figure 2 , is a schematic diagram of the illumination beam incident and light intensity collection device, wherein, when the illumination beam 201 is incident on the sample 202, the current position to be measured of the sample 202 is illuminated jThe far-field diffraction light intensity distribution 205 of the sample is collected, specifically, based on Fraunhofer diffraction 204. The near-field outgoing wave distribution 203 of the sample is calculated based on the sample function and the illumination function of the sample at the current position to be measured.

[0032] Among them, the far-field diffraction light intensity distribution of the sample is collected based on the Fraunhofer diffraction method. The far-field diffraction light intensity distribution of the sample can be expressed as follows:

[0033] (1);

[0034] in, is the wavelength of the illumination beam, is the distance from the sample to the diffraction light intensity detector in the Fraunhofer diffraction light path, j is the current position j of the illumination beam incident on the sample to be measured, and They are the position coordinate matrices in the spatial domain and frequency domain respectively, and k is the corresponding illumination wavelength The wave vector below.

[0035] It should be noted that the far-field diffraction intensity distribution of a sample is a diffraction intensity distribution that includes noise. In the embodiments of the present invention, there are two main ways to obtain this noisy diffraction intensity: the first is when the illumination beam transmits through the sample, with the diffraction light path and the diffraction light intensity detector located on the side of the sample facing away from the illumination beam; the second is when the illumination beam reflects off the sample, with the diffraction light path and the diffraction light intensity detector located on the reflected light path of the incident illumination. Noise in the diffraction light intensity includes one or more of detector noise, sample jitter noise, and light source decoherence noise.

[0036] The Fraunhofer diffraction light field model must satisfy the Fraunhofer diffraction approximation conditions, generally using the Fresnel number Characterization:

[0037] (2);

[0038] Where a is the size of the sample microstructure aperture, such as the radius, and L is the distance between the microstructure and the diffraction light intensity detector.

[0039] The near-field outgoing wave distribution of the sample is calculated based on the sample function and the illumination function. For the initial near-field outgoing wave distribution, the sample function and the illumination function can be initialized, and then the initial near-field outgoing wave distribution of the sample can be calculated based on the initialized sample function and the illumination function. The initialization steps of the sample function and the illumination function generally take two forms: one is to assign the sample function and the illumination function using a random number matrix; the other is to assign the sample function and the illumination function to a given prior information of the sample function or the illumination function.

[0040] Step 2: Based on the far-field diffraction light intensity distribution of the sample, the near-field outgoing wave distribution is iteratively solved to obtain the sample function and illumination function of the current position to be measured after each iterative update.

[0041] As an embodiment, the step 2, based on the far-field diffraction light intensity distribution of the sample, iteratively solves the near-field exit wave distribution to obtain the sample function and illumination function of the current position to be measured after each iterative update, including: according to the far-field diffraction light intensity distribution, based on the alternating projection algorithm between time space and Fourier space, iteratively updating the current near-field exit wave distribution to obtain the updated near-field exit wave distribution; according to the near-field exit wave distribution before the current update and the updated near-field exit wave distribution, based on the coherent diffraction imaging algorithm, updating the sample function and illumination function before the current iteration, to obtain the sample function and illumination function of the current position to be measured after the current iterative update.

[0042] It can be understood that the above steps can obtain the initial near-field outgoing wave distribution and far-field diffraction intensity distribution of the illumination beam incident on the current position of the sample to be measured based on the initialized sample function and illumination function, and then combine the far-field diffraction intensity distribution of the sample to iteratively solve the near-field outgoing wave distribution to update the near-field outgoing wave distribution. Specifically, according to the far-field diffraction intensity distribution, based on the Fourier projection transform, the current near-field outgoing wave distribution is updated. Update to obtain the updated near-field outgoing wave distribution , and its specific update formula is as follows:

[0043] (3);

[0044] in, and For the sample function and lighting function before the current update, is the near-field outgoing wave distribution before updating, FFT stands for fast Fourier transform, represents the inverse fast Fourier transform; j represents the illumination beam The overlapping position of the incident sample at position j (the current position to be measured), Represents the outgoing wave The frequency domain representation of , which can also be called the iteratively updated far-field diffraction intensity distribution, is the near-field outgoing wave distribution after the current iteration update, is the far-field diffraction light intensity distribution at the jth point.

[0045] As an embodiment, the method of updating the sample function and illumination function before the current update based on the coherent diffraction imaging algorithm according to the near-field exit wave distribution before the current update and the first near-field exit wave distribution after the update, and obtaining the sample function and illumination function of the current position to be measured after the current iterative update, includes:

[0046] (4);

[0047] in:

[0048] (5);

[0049] in, and are the sample function and lighting function updated after each iteration, and They are and The update weight factor is an interval constant ranging from (0,1). and For the sample function and lighting function before the update, is the near-field outgoing wave distribution before updating, is the updated near-field outgoing wave distribution; ξ and η is a function The update step size is n, the current iteration number, and N is the incremental gradient residual of the outgoing wave. The number of iterations when it is less than the set threshold, is the activation function, flag is the function The selection switch has two states, 0 and 1. Its initial value is set to 1. As the iteration n progresses, when When it approaches zero, the flag value is inverted and the cycle continues.

[0050] It is understandable that the embodiment of the present invention uses a coherent diffraction imaging algorithm to analyze the far-field diffraction intensity distribution of noise. Perform iterative solution and output the sample function after iterative update and lighting functions .

[0051] Specifically, the core principle of the coherent diffraction imaging algorithm is based on the iterative optimization theory of alternating projections between time space and Fourier space. The iterative process can be seen in formulas (4) and (5), and the sample function and illumination function of the current position to be measured after the current iterative update are obtained.

[0052] Step 3: Based on the sample function and illumination function of the current position to be measured after each iterative update, calculate the incremental gradient residual of the outgoing wave after each iterative update. ,when If the value is less than the set threshold, go to step 4; otherwise, go to step 2.

[0053] As an example, the incremental gradient residual of the outgoing wave after each iterative update is calculated in step 3. , including: obtaining the sample function and illumination function of each position to be tested before the current position to be tested after each iterative update; based on the sample function and illumination function of all the positions to be tested that have completed iterative updates after each iterative update, as well as the near-field outgoing wave distribution of all the positions to be tested that have completed iterative updates before the update, calculating the incremental gradient residual of the outgoing wave after each iterative update .

[0054] It can be understood that when the sample function and illumination function of the current position to be measured are obtained after the current iteration update, the current output wave incremental gradient residual is calculated. . Among them, for each iteration, it is necessary to calculate the iteratively updated sample function and illumination function of each position before the current position to be measured. Based on the iteratively updated sample function and illumination function of all previous positions including the current position to be measured, as well as the near-field outgoing wave distribution before all positions are updated, the current outgoing wave incremental gradient residual is calculated. .

[0055] Among them, the incremental gradient residual of the outgoing wave is The calculation formula can be expressed as follows:

[0056] (6);

[0057] Where ∑ is the summation function.

[0058] According to formula (6), the incremental gradient residual of the outgoing wave after each iterative update is calculated as , determine the residual of the incremental gradient of the outgoing wave Is it less than the set threshold? If so, execute step 4. If not, execute step 2 again until the incremental gradient residual of the outgoing wave is updated iteratively. If it is less than the set threshold, execute step 4 and record the incremental gradient residual of each outgoing wave. The number of iterations N when it is less than the set threshold.

[0059] Step 4: Optimize the sample function and illumination function of the current position to be measured after iterative update obtained in step 2 based on the dynamic regularization function. Repeat steps 2 to 4 based on the optimized sample function and illumination function until the incremental gradient residual of the outgoing wave is less than 0. If the mean square error (MSE) between the iteratively updated far-field diffraction intensity distribution and the far-field diffraction intensity distribution in step 1 is less than a set threshold, the iteration is terminated, and the sample function and illumination function of the current position to be measured are obtained.

[0060] As an embodiment, in the step 4, the sample function and the illumination function of the current position to be measured obtained in step 2 are updated based on the dynamic regularization function, including: according to the obtained sample function and the illumination function of the current position to be measured, the near-field outgoing wave distribution after the current update and the near-field outgoing wave distribution before the update, the sample function and the illumination function are updated based on the dynamic regularization function.

[0061] It can be understood that when the incremental gradient residual of the outgoing wave When it is less than the set threshold, record the number of iterations N at this time, apply the dynamic regularization function to the sample function and illumination function of the current position to be measured obtained after the iteration number N, optimize the sample function and illumination function corresponding to the iteration number N, and then continue to step 2 based on the optimized sample function and illumination function.

[0062] Among them, the dynamic regularization function can be expressed as:

[0063] (7);

[0064] in:

[0065] (8);

[0066] in, and are the updated first sample function and first illumination function respectively, is the exponential moving average coefficient, is the reshape function, Is a function that generates a random number matrix.

[0067] It is understandable that as the calculation iteration proceeds, the incremental gradient residual of the outgoing wave Start to converge, when When it is less than the set threshold, the number of iterations is assigned to Activation function N, flag negation, reshaping function The random matrix is ​​taken as the value, and the dynamic regularization function applied in step 4 can be understood as follows: when the number of iterations is N, formulas (7) and (8) can be used to replace formulas (4) and (5) to obtain the sample function and illumination function when the number of iterations is N. At other numbers of iterations, formulas (4) and (5) are used to obtain the sample function and illumination function.

[0068] For the sample function and illumination function of the current position of the sample to be measured obtained by iterative update in step 2, if the incremental gradient residual of the outgoing wave at the current iteration number is When it is less than the set threshold, the current sample function and illumination function are regularized and optimized based on the dynamic regularization function in step 4.

[0069] Based on the sample function and illumination function after regularization optimization, steps 2 to 4 are executed in a loop to iterate and obtain the sample function and illumination function of the current position of the final sample to be measured.

[0070] It should be noted that the reshaping function is introduced into the dynamic regularization function in step 4 If and only if the residual When it is less than the set threshold, it means that the iterative process falls into a local optimum. At this time, the sample function or the illumination function introduces the random matrix of the reshaping function. , the sample function or lighting function is assigned a random matrix, forcibly breaking the local optimal trap and making it jump out of the local optimality until the sample function is iteratively updated to find the global optimal process.

[0071] Wherein, as an embodiment, the step 2 to step 4 is repeated to finally obtain the sample function and the illumination function of the current position to be measured, including: based on the optimized sample function and the illumination function, the step 2 to step 4 is repeated until the incremental gradient residual of the near-field outgoing wave distribution is The mean square error (MSE) of the far-field diffraction light field and the far-field diffraction light intensity distribution in step 1 is less than the set threshold, the iteration is terminated, and the sample function and illumination function of the current position to be measured are obtained, including: based on the optimized sample function and illumination function, repeating steps 2 to 4, and when the iteration termination condition is met, obtaining the sample function and illumination function of the current position to be measured; wherein, when the incremental gradient residual of the outgoing wave When the mean square error (MSE) between the far-field diffraction intensity distribution and the iteratively updated far-field diffraction intensity distribution is less than the set threshold, the iteration termination condition is determined to be met, where:

[0072] (9);

[0073] Where J represents the number of all positions to be tested;

[0074] Alternatively, if the current number of iterations reaches the set maximum number of iterations, it is determined that the current iteration meets the iteration termination condition.

[0075] It can be understood that after step 4 regularizes and optimizes the sample function and the illumination function based on the dynamic regularization function, based on the optimized sample function and the illumination function, steps 2, 3 and 4 are then executed cyclically until the iteration termination condition is met and the iteration is stopped. There are two main ways to determine the iteration termination condition of the present invention: the first is the far-field light intensity distribution calculated by the Fraunhofer diffraction propagation of the illumination beam according to the reconstructed near-field output wave distribution. The diffracted light intensity input in step 1 Calculate the mean square error (MSE) between them and use the mean square error (MSE) combined with the current outgoing wave incremental gradient residual Comprehensive judgment is made, for example, when the mean square error MSE and the residual of the incremental gradient of the outgoing wave When both are less than the corresponding set threshold, the iteration termination condition is met; the second is to set the maximum number of iteration cycles based on prior knowledge for judgment, and stop the iteration when the number of iterations reaches the maximum number of iteration cycles. When the iteration termination condition is met, the sample function and illumination function of the current position of the sample to be tested are output, where, Figure 3 Figure 3 is a schematic diagram of the final solution results for the sample function and illumination function, specifically the output amplitude 301 and phase 302 of the sample function, and the output amplitude 303 and phase 304 of the illumination function. The sample function is a complex amplitude matrix obtained through computational phase imaging, an advanced computational imaging method that differs methodologically from traditional light field imaging methods based on geometric ray tracing.

[0076] The present invention collects the far-field diffraction light intensity containing noise after the illumination beam enters the sample, adopts the coherent diffraction imaging method to iteratively reconstruct the complex amplitude information of the sample and the illumination beam, and applies a dynamic regularization function to the iterative process to suppress the disturbance of noise on the calculation convergence. It has the ability to escape the local optimal trap and finally obtain the global optimal solution, which solves the technical problems of the existing phase calculation imaging method such as serious loss of imaging resolution and non-convergence of the algorithm under noise disturbance.

[0077] Figure 4 A structural diagram of a computational phase imaging system provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, a computational phase imaging system includes an acquisition module 401, an iterative solution module 402, a calculation module 403, a judgment module 404 and an optimization module 405, wherein:

[0078] An acquisition module 401 is configured to acquire a near-field outgoing wave distribution and a far-field diffraction light intensity distribution of the sample based on the illumination beam and the current position of the sample at which the illumination beam is incident.

[0079] An iterative solution module 402 is configured to iteratively solve the near-field outgoing wave distribution based on the far-field diffraction light intensity distribution, and obtain a sample function and an illumination function at the current position to be measured after each iterative update;

[0080] The calculation module 403 is used to calculate the incremental gradient residual of the outgoing wave after each iterative update based on the sample function and the illumination function of the current position to be measured after each iterative update. ;

[0081] The judging module 404 is used to If the value is less than the set threshold, the optimization module 405 is executed; otherwise, the iterative solution module 402 is executed;

[0082] The optimization module 405 optimizes the sample function and the illumination function of the current position to be measured after the iterative update obtained by the iterative update module based on the dynamic regularization function, and repeatedly executes the iterative solution module 402, the calculation module 403 and the judgment module 404 based on the optimized sample function and illumination function until the incremental gradient residual of the outgoing wave is less than 0. If the mean square error (MSE) between the iteratively updated far-field diffraction intensity distribution and the far-field diffraction intensity distribution in step 1 is less than a set threshold, the iteration is terminated, and the sample function and illumination function of the current position to be measured are obtained.

[0083] It can be understood that the computational phase imaging system provided by the present invention corresponds to the computational phase imaging method provided in the aforementioned embodiments. The relevant technical features of the computational phase imaging system can refer to the relevant technical features of the computational phase imaging method, which will not be repeated here.

[0084] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 511, the steps of the computational phase imaging method are implemented.

[0085] See also Figure 6 , Figure 6 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Figure 6 As shown, this embodiment provides a computer-readable storage medium 600 on which a computer program 611 is stored. When the computer program 611 is executed by a processor, the steps of the computational phase imaging method are implemented.

[0086] The present invention provides a method and system for computational phase imaging, which has the following beneficial effects:

[0087] 1. The present invention applies a dynamic regularization function to the iterative update process of computational imaging to improve the convergence robustness of the algorithm in complex noise scenarios, enhance the algorithm's ability to escape the local optimal trap, and accelerate convergence to the global optimal.

[0088] 2. The computational phase imaging method provided by the present invention has the advantages of a streamlined system, robust algorithm, fast solution, and high imaging resolution. It can achieve fast and precise computational imaging solutions for samples in multiple scenarios and under complex noise interference.

[0089] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0090] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0094] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0095] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A computational phase imaging method, characterized in that: include: Step 1, based on the illumination beam and the current position of the sample to be measured at which the illumination beam is incident, obtaining the near-field outgoing wave distribution and the far-field diffraction light intensity distribution of the sample; Step 2, based on the far-field diffraction light intensity distribution, iteratively solve the near-field outgoing wave distribution, and obtain the sample function and illumination function of the current position to be measured after each iterative update; Step 3: Calculate the incremental gradient residual of the outgoing wave after each iterative update based on the sample function and illumination function of the current position to be measured after each iterative update. ,when When it is less than the set threshold, go to step 4; Otherwise, go to step 2; Step 4: Optimize the sample function and illumination function of the current position to be measured after iterative update obtained in step 2 based on the dynamic regularization function. Repeat steps 2 to 4 based on the optimized sample function and illumination function until the incremental gradient residual of the outgoing wave is less than 0. If the mean square error (MSE) between the iteratively updated far-field diffraction light intensity distribution and the far-field diffraction light intensity distribution in step 1 is less than a set threshold, the iteration is terminated, and the sample function and illumination function of the current position to be measured are obtained; In step 4, the sample function and the lighting function of the current position to be measured after iterative update obtained in step 2 are optimized based on the dynamic regularization function, including: According to the obtained sample function and illumination function of the current position to be measured after iterative update, the near-field outgoing wave distribution after iterative update, and the near-field outgoing wave distribution before update, the sample function and illumination function of the current position to be measured after iterative update are optimized based on the dynamic regularization function: in: in, and For the sample function and lighting function before iterative update, and are the optimized sample function and lighting function of the current position to be measured, is the exponential moving average coefficient, ξ and η is a function The update step size, is the activation function, n is the current number of iterations, and They are and The update weight factor is an interval constant ranging from (0,1). is the reshape function, Is a function that generates a random number matrix.

2. The computational phase imaging method according to claim 1, wherein: The step 1, based on the illumination beam and the illumination beam incident on the current position of the sample to be measured, obtains the near-field outgoing wave distribution and the far-field diffraction light intensity distribution of the sample, including: Based on the sample function and the illumination function of the current position to be measured, obtaining the near-field outgoing wave distribution of the sample at the current position to be measured; The far-field diffraction light intensity distribution of the sample at the current test position is collected based on the Fraunhofer diffraction method.

3. The computational phase imaging method according to claim 1, wherein: The far-field diffraction light intensity distribution is a diffraction light intensity containing noise, and the noise includes one or more of detector noise, sample jitter noise and light source decoherence noise.

4. The computational phase imaging method according to claim 2, wherein: The step 2, based on the far-field diffraction light intensity distribution of the sample, iteratively solves the near-field outgoing wave distribution, and obtains the sample function and illumination function of the current position to be measured after each iterative update, including: Iteratively updating the near-field outgoing wave distribution based on the Fourier projection transform according to the far-field diffraction light intensity distribution to obtain an updated near-field outgoing wave distribution; According to the near-field outgoing wave distribution before the update and the near-field outgoing wave distribution after the update, the sample function and illumination function before the current iterative update are updated based on the coherent diffraction imaging algorithm to obtain the sample function and illumination function of the current position to be measured after the current iterative update.

5. The computational phase imaging method according to claim 4, wherein: The iterative updating of the near-field outgoing wave distribution based on the far-field diffraction light intensity distribution and the Fourier projection transform to obtain the updated near-field outgoing wave distribution includes: in, and is the sample function and lighting function before iterative update, FFT stands for Fast Fourier Transform, represents the inverse fast Fourier transform; j represents the illumination beam The overlap position of the incident sample at point j, is the near-field outgoing wave distribution The frequency domain representation of , that is, the far-field diffraction intensity distribution after iterative update, is the near-field outgoing wave distribution after iterative update, is the far-field diffraction light intensity distribution at point j; The updating of the sample function and the illumination function before the current iterative update based on the coherent diffraction imaging algorithm according to the near-field outgoing wave distribution before the update and the near-field outgoing wave distribution after the update, and obtaining the sample function and the illumination function of the current position to be measured after the current iterative update, includes: in: in, and are the sample function and lighting function updated after each iteration, and They are and The update weight factor is an interval constant ranging from (0,1). and For the sample function and lighting function before the update, is the near-field outgoing wave distribution before updating, is the updated near-field outgoing wave distribution; ξ and η is a function The update step size is n, the current iteration number, and N is the incremental gradient residual of the outgoing wave. The number of iterations approaches zero, is the activation function, flag is the function The selection switch has two states, 0 and 1. Its initial value is set to 1. As the iteration n progresses, when When it approaches zero, the flag value is inverted and the cycle continues.

6. The computational phase imaging method according to claim 2, wherein: In step 3, the incremental gradient residual of the outgoing wave after each iterative update is calculated. ,include: Obtain the sample function and lighting function of each position to be measured before the current position to be measured after each iteration update; Based on the sample function and illumination function of all the test positions that have completed the iterative update after each iterative update, and the near-field outgoing wave distribution of all the test positions that have completed the iterative update before the update, the incremental gradient residual of the outgoing wave after each iterative update is calculated. .

7. The computational phase imaging method according to claim 4, wherein: Based on the optimized sample function and illumination function, steps 2 to 4 are repeated until the incremental gradient residual of the near-field outgoing wave distribution is The mean square error (MSE) of the far-field diffracted light field and the far-field diffracted light intensity distribution in step 1 is less than the set threshold, and the iteration is terminated. The sample function and illumination function of the current position to be measured are obtained, including: Based on the optimized sample function and lighting function, steps 2 to 4 are repeated. When the iteration termination condition is met, the sample function and lighting function of the current position to be measured are obtained. Among them, when the incremental gradient residual of the outgoing wave When the mean square error (MSE) between the far-field diffraction intensity distribution and the iteratively updated far-field diffraction intensity distribution is less than the set threshold, the iteration termination condition is determined to be met, where: Where J represents the number of all positions to be tested; Alternatively, if the current number of iterations reaches the set maximum number of iterations, it is determined that the current iteration meets the iteration termination condition.

8. The computational phase imaging method according to claim 7, wherein: The threshold value is set to , the maximum number of iterations is between 300 and 600.

9. A computational phase imaging system, characterized in that: include: an acquisition module, configured to acquire a near-field outgoing wave distribution and a far-field diffraction light intensity distribution of the sample based on the illumination beam and the current position to be measured at which the illumination beam is incident on the sample; An iterative solution module, configured to iteratively solve the near-field outgoing wave distribution based on the far-field diffraction light intensity distribution, and obtain a sample function and an illumination function at a current position to be measured after each iterative update; A calculation module is used to calculate the incremental gradient residual of the outgoing wave after each iterative update based on the sample function and illumination function of the current position to be measured after each iterative update. ; Judgment module, used when When it is less than the set threshold, the optimization module is executed; Otherwise, executing the iterative solution module; The optimization module is used to optimize the sample function and the illumination function of the current position to be measured after the iterative update obtained by the iterative update module based on the dynamic regularization function, and repeatedly execute the iterative solution module, the calculation module and the judgment module based on the optimized sample function and illumination function until the incremental gradient residual of the outgoing wave is less than 0. If the mean square error (MSE) between the iteratively updated far-field diffraction light intensity distribution and the far-field diffraction light intensity distribution in step 1 is less than a set threshold, the iteration is terminated, and the sample function and illumination function of the current position to be measured are obtained; The optimization module is configured to optimize the sample function and the lighting function of the current position to be measured after iterative updating obtained by the iterative updating module based on the dynamic regularization function, including: According to the obtained sample function and illumination function of the current position to be measured after iterative update, the near-field outgoing wave distribution after iterative update, and the near-field outgoing wave distribution before update, the sample function and illumination function of the current position to be measured after iterative update are optimized based on the dynamic regularization function: in: in, and For the sample function and lighting function before iterative update, and are the optimized sample function and lighting function of the current position to be measured, is the exponential moving average coefficient, ξ and η is a function The update step size, is the activation function, n is the current number of iterations, and They are and The update weight factor is an interval constant ranging from (0,1). is the reshape function, Is a function that generates a random number matrix.

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