A stacked diffraction position correction computed imaging method, apparatus, and system

Abstract

The application discloses a kind of laminated diffraction position correction calculation imaging method, device and system, wherein the method comprises: in the process that motion platform drives to be measured sample according to preset curve does two-dimensional plane motion, using illumination probe to irradiate to be measured sample, using camera to gather the intensity information of the diffraction light field of to be measured sample;With the center of each scanning position coordinate of motion platform Randomly generate a series of position coordinates, a series of position coordinates are divided into multiple initial populations, to use the intensity information of the diffraction light field calculated using the position coordinates in population with the intensity information of the diffraction light field collected by camera Between difference minimum is the goal, iteratively update population, iterate to maximum iteration number, obtain the update position coordinate of motion platform;The update position coordinate of motion platform is used as the coordinate of illumination probe and to be measured sample, the complex amplitude function of illumination probe and to be measured sample is calculated, and thus reconstruct amplitude phase.The laminated diffraction position correction of the application is high in precision, fast.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of coherent diffraction imaging, and more particularly, to a stacked diffraction position correction computational imaging method, device and system. BACKGROUND

[0002] Due to the increasing demand for imaging quality in academia and industry in recent years, traditional optical microscopes have been difficult to meet the imaging needs, and coherent diffraction imaging (CDI) as a lensless imaging technology has become a new research direction. Since the camera can only record intensity information during the acquisition process, CDI can be used to recover and reconstruct the image of the object by measuring the intensity. Ptychography is a commonly used computational method in CDI, which uses multiple diffraction field information corresponding to the image part.

[0003] Ptychography partially illuminates the object by placing an aperture in front of the object or using a local illumination probe on the object, and the probe scans the object and measures the corresponding intensity information in the far field. The above series of diffraction fields are used to reconstruct the object, solving the problem of intensity loss during the camera acquisition process. One of the advantages of ptychography reconstruction image is that it can reconstruct a larger field of view, and the condition for generating a large field of view is that the probe and the object move relatively. The accuracy of each step position movement is directly related to the quality of the image reconstruction, so the position disturbance error has a profound impact on the image reconstruction. Therefore, the significance of the probe position correction is highlighted.

[0004] In recent years, some achievements have been made in the field of position correction algorithm in stacked diffraction imaging at home and abroad. In 2008, Manuel Guizar-Sicairos et al. proposed and tested a nonlinear optimization algorithm, which was first used to solve the phase recovery problem with lateral translation diversity. In 2012, A.M. Maiden et al. proposed a simulated annealing algorithm for position correction in ptychography, which searched for the optimal position information by making a large number of guesses about the probe position. In 2013, Zhang Fucai proposed a position cross-correlation algorithm to search and correct the position error in the image reconstruction iteration process, which searched for the optimal position information by cross-correlating the information of the sample before and after updating. The method has high calculation accuracy, but the calculation speed is slow.

[0005] Therefore, the existing position correction technology in stacked diffraction imaging has the technical problem of being unable to simultaneously improve the accuracy and speed. SUMMARY

[0006] In view of the above defects or improvement needs of the prior art, the present application provides a stacked diffraction position correction computational imaging method, device and system, thereby solving the technical problem that the position correction technology in the prior stacked diffraction imaging cannot simultaneously improve the precision and speed.

[0007] To achieve the above object, according to one aspect of the present application, a stacked diffraction position correction computational imaging method is provided, which is applied to a stacked diffraction system including a laser, a beam expander, an adjustable diaphragm, a focusing mirror, a motion platform and a camera arranged in sequence along the optical axis, the laser sequentially passes through the beam expander, the adjustable diaphragm and the focusing mirror to generate an illumination probe to irradiate a sample to be measured on the motion platform, and the method comprises:

[0008] In the process that the motion platform drives the sample to be measured to move in a two-dimensional plane according to a preset curve, the sample to be measured is irradiated by the illumination probe, and the camera is used to collect the intensity information of the diffraction light field of the sample to be measured;

[0009] A series of position coordinates are randomly generated with each scanning position coordinate of the motion platform as the center, the series of position coordinates are divided into a plurality of initial populations, the difference between the intensity information of the diffraction light field calculated using the position coordinates in the population and the intensity information of the diffraction light field collected by the camera is minimized as the target, the population is iteratively updated, and the updated position coordinates of the motion platform are obtained after iteration to a preset maximum iteration number;

[0010] The updated position coordinates of the motion platform are used as the coordinates of the illumination probe and the sample to be measured to calculate the complex amplitude function of the illumination probe and the sample to be measured, thereby reconstructing the amplitude and phase of the sample to be measured and the illumination probe.

[0011] Further, the updated position coordinates of the motion platform are iteratively obtained as follows:

[0012] A series of position coordinates are randomly generated with the initial position coordinates of the motion platform as the center, the series of position coordinates are divided into a plurality of initial populations,

[0013] For the i-th initial population, the complex amplitude function of the illumination probe and the sample to be measured at each position coordinate in the initial population is calculated, the complex amplitude function of the illumination probe is multiplied by the complex amplitude function of the sample to be measured to obtain the exit wave complex amplitude function of the illumination probe; the exit wave complex amplitude function is propagated to the Fourier space to calculate the intensity information of the diffraction light field; and the position coordinate with the minimum difference between the calculated intensity information of the diffraction light field and the intensity information of the diffraction light field collected by the camera is used as the individual historical best position of the i-th initial population;

[0014] The individual historical best positions with the minimum difference in the plurality of initial populations are used as the group historical best position;

[0015] The weighted sum of the distance of the position update at the last iteration, the difference between the individual historical optimal position and the randomly generated individual position, and the difference between the group historical optimal position and the randomly generated individual position is taken as the distance of the position update at the next iteration, and the population is iteratively updated to minimize the difference between the intensity information of the diffracted light field and the intensity information of the diffracted light field collected by the camera, and the iteration is stopped when the preset maximum number of iterations is reached. The distance of the position update corresponding to the individual historical optimal position and the group historical optimal position at the final iteration is added to the position coordinates of the motion platform at the last iteration to obtain the updated position coordinates of the motion platform.

[0016] Further, the distance of the position update at the next iteration v is calculated as follows:

[0017] v = v0 * w + c1 * (x m -x) + c2 * (y m -x)

[0018] wherein w represents the position inertia weight at the last iteration, and is in the range of [0, 1]; c1 represents the self-update weight, and is in the range of [0, 1]; c2 represents the group update weight, and is in the range of [0, 1]; x m represents the individual historical optimal position, y m represents the group historical optimal position, v0 is the distance of the position update at the last iteration, and x is the updated position coordinates of the motion platform obtained at the last iteration.

[0019] Further, the position inertia weight is adjusted as follows:

[0020]

[0021] wherein w max represents the maximum value of the position inertia weight, and is in the range of [0, 1]; w min represents the minimum value of the position inertia weight, and is in the range of [0, 1]; w max > w min ; ger represents the current number of iterations, and ger max represents the preset maximum number of iterations.

[0022] Further, the position inertia weight is adjusted as follows:

[0023]

[0024] wherein w max represents the maximum value of the position inertia weight, and is in the range of [0, 1]; w min represents the minimum value of the position inertia weight, and is in the range of [0, 1]; w max > wmin , E n is the MSE error value of the particle coordinate at the current iteration, E min is the MSE error value of the historical best position of the population at the current iteration, E avg is the average error of all search particles at the current iteration.

[0025] Further, the complex amplitude function of the to-be-tested sample is:

[0026]

[0027] Wherein, α represents the first iterative search step length, and the value is between [0, 1], O(r), O'(r') respectively represent the complex amplitude function of the to-be-tested sample before and after the position coordinate is updated, P m (r, λ), P m '(r', λ) respectively represent the complex amplitude function of the illumination probe before and after the position coordinate is updated at the mth scanning position, respectively represent the intensity information of the diffraction light field before and after the illumination probe amplitude is replaced at the mth scanning position, r represents the position coordinate of the motion platform, r' represents the updated position coordinate of the motion platform, λ represents the wavelength of the illumination probe, * represents the conjugate operation, and || max represents the maximum value of the amplitude of each element in the complex amplitude function corresponding matrix.

[0028] Further, the complex amplitude function of the illumination probe after the position coordinate is updated at the mth scanning position is:

[0029]

[0030] Wherein, β represents the second iterative search step length, and the value is between [0, 1].

[0031] According to another aspect of the present application, there is provided a stacked diffraction position correction computational imaging device, comprising a processor and a memory;

[0032] The memory is used for storing computer programs or instructions;

[0033] The processor is used for executing the computer programs or instructions in the memory, so that a stacked diffraction position correction computational imaging method is executed.

[0034] According to another aspect of the present application, there is provided a stacked diffraction position correction computational imaging system, comprising a laser, a beam expander, an adjustable diaphragm, a focusing mirror, a motion platform and a camera arranged in sequence along the optical axis; and a stacked diffraction position correction computational imaging device, the imaging device is used for correcting the coordinates of the illumination probe and the to-be-tested sample.

[0035] Further, the focusing mirror is a focusing lens or a focusing mirror.

[0036] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0037] (1) The motion error of the platform driving the motion of the sample to be measured in the stacked diffraction system will cause the position of the sample to be measured to deviate, and ultimately the resolution of the reconstructed image to be low. In the process of driving the sample to be measured to move in a two-dimensional plane according to a preset curve by the motion platform, the particle swarm algorithm is used to iteratively update the search position, so as to avoid the image reconstruction quality from being reduced due to position disturbance, thereby improving the resolution of the reconstructed image. Compared with other stacked diffraction computed imaging methods, the stacked diffraction position correction imaging method based on the particle swarm algorithm proposes a new direction for updating the position search, avoids the algorithm from falling into a local optimal solution too early, and has a faster search speed, thereby realizing fast position updating and high precision.

[0038] (2) The present application iterates multiple populations, and each iteration will obtain an individual historical best position and a population historical best position. The individual and the population are optimized, which can greatly improve the position search area and further improve the search precision. The present application first proposes a stacked diffraction position correction method based on the particle swarm algorithm. Based on the idea of simulated annealing, the search of each position coordinate is improved in efficiency through a process similar to population evolution.

[0039] (3) In the present application, the position inertia weight, the self-updating weight and the population updating weight are used as the position search step when calculating the position updating distance, so as to improve the convergence speed and convergence precision of the position search. When calculating the complex amplitude function, the iteration search step is set to improve the convergence speed and convergence precision of the iteration algorithm. The situation that the corrected positions interfere with each other when the probe coordinate positions are close is avoided. By increasing the diversity of particle swarm iteration and updating the step distance, the algorithm can be effectively prevented from falling into a local optimal solution too early, and iterated towards the optimal solution continuously.

[0040] (4) The present application provides multiple ways of changing the position inertia weight, wherein the weight linear reduction method is simple to operate and has high efficiency. The adaptive weight updating method can effectively improve the search accuracy while ensuring the efficiency. When E n <E min It is indicated that the particle search direction is correct, and the direction of the previous search should be reserved with a larger value. n >E min It is indicated that the particle search direction deviates from the direction of the optimal solution explored, and the influence of the search direction of this time on the search direction of next time should be reduced. When the average error E avgMuch larger than the group historical best position E min When, it indicates that the current search direction weight should be reduced.

[0041] (5) The present application can be used in both the transmission imaging system and the reflection imaging system to correct the position of the layered diffraction, so as to overcome the problem of the image reconstruction quality decline caused by the position disturbance, the problem of the clarity degradation of the sample to be measured in the layered diffraction calculation imaging, and the problem of the resolution improvement of the reconstructed image. The application in different systems shows that the present application can be compatible with different types of imaging systems. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a flow chart of a layered diffraction position correction calculation imaging method provided by an embodiment of the present application;

[0043] Figure 2 (a) in the figure is a light path principle diagram of a layered diffraction position correction calculation imaging system provided by an embodiment 1 of the present application;

[0044] Figure 2 (b) in the figure is a light path principle diagram of a layered diffraction position correction calculation imaging system provided by an embodiment 2 of the present application;

[0045] Figure 3 is a flow chart of a layered diffraction position correction calculation imaging method based on the particle swarm algorithm provided by an embodiment of the present application;

[0046] Figure 4 (a) in the figure is an amplitude pattern of a sample to be measured used in a simulation process provided by an embodiment of the present application;

[0047] Figure 4 (b) in the figure is a phase pattern of a sample to be measured used in a simulation process provided by an embodiment of the present application;

[0048] Figure 5 is a two-dimensional motion plane trajectory of a sample to be measured provided by an embodiment of the present application;

[0049] Figure 6 is an amplitude reconstructed image of a sample without position correction provided by an embodiment of the present application;

[0050] Figure 7 is an amplitude reconstructed image of a sample with layered diffraction position correction by the particle swarm algorithm provided by an embodiment of the present application;

[0051] In all the figures, the same reference signs are used to represent the same elements or structures, wherein:

[0052] 1 is a laser, 2 is a beam expander, 3 is an adjustable diaphragm, 4 is a focusing mirror, 5 is a motion platform, and 6 is a camera. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0054] As shown in Figure 1 A stacked diffraction position correction computational imaging method, the method is applied to a stacked diffraction system, the stacked diffraction system includes a laser, a beam expander, an adjustable diaphragm, a focusing lens, a motion platform and a camera arranged in sequence along the optical axis direction, the laser sequentially passes through the beam expander, the adjustable diaphragm and the focusing lens to generate an illumination probe to irradiate a sample to be measured on the motion platform, and the method comprises:

[0055] In the process of driving the sample to be measured by the motion platform to move in a two-dimensional plane according to a preset curve, the sample to be measured is irradiated using the irradiation probe, and the camera is used to collect intensity information of a diffraction light field of the sample to be measured;

[0056] A series of position coordinates are randomly generated with each scanning position coordinate of the motion platform as the center, the series of position coordinates are divided into a plurality of initial populations, and the difference between the intensity information of the diffraction light field calculated using the position coordinates in the population and the intensity information of the diffraction light field collected by the camera is minimized as the target, the population is iteratively updated, and the updated position coordinates of the motion platform are obtained after iteration to a preset maximum iteration number;

[0057] The updated position coordinates of the motion platform are used as the coordinates of the irradiation probe and the sample to be measured, the complex amplitude function of the irradiation probe and the sample to be measured is calculated, and the amplitude and phase of the sample to be measured and the irradiation probe are reconstructed therefrom.

[0058] Embodiment 1

[0059] A stacked diffraction position correction computational imaging system, as shown in Figure 2 (a), the system is a transmission imaging system, which includes a laser, a beam expander, an adjustable diaphragm, a focusing lens, a motion platform and a camera (CCD or CMOS) arranged in sequence along the optical axis direction; and a stacked diffraction position correction computational imaging device.

[0060] The imaging device comprises a processor and a memory.

[0061] The memory is used to store computer programs or instructions.

[0062] The processor is configured to execute the computer program or instructions in the memory, so that a stacked diffraction position correction computational imaging method is executed. The platform driving the motion of the sample to be measured has motion errors, and the imaging device is configured to correct the coordinates of the illumination probe and the sample to be measured on the motion platform.

[0063] Embodiment 2

[0064] A stacked diffraction position correction computational imaging system, as shown in (b) of the accompanying drawings, the system is a reflection imaging system, comprising a laser, a beam expander, an adjustable diaphragm, a focusing mirror, a motion platform and a camera (CCD or CMOS) arranged in sequence along the optical axis direction; and a stacked diffraction position correction computational imaging device. Figure 2

[0065] The imaging device comprises a processor and a memory;

[0066] The memory is configured to store computer programs or instructions;

[0067] The processor is configured to execute the computer program or instructions in the memory, so that a stacked diffraction position correction computational imaging method is executed. The platform driving the motion of the sample to be measured has motion errors, and the imaging device is configured to correct the coordinates of the illumination probe and the sample to be measured on the motion platform.

[0068] Embodiment 3

[0069] A stacked diffraction position correction computational imaging method, the method is applied to a stacked diffraction system, the stacked diffraction system comprises a helium-neon laser, a beam expander, an adjustable diaphragm, a focusing lens / focusing mirror, a motion platform and a camera arranged in sequence along the optical axis direction, the laser passes through the beam expander, the adjustable diaphragm and the focusing mirror in sequence, generates an illumination probe to irradiate a sample to be measured on the motion platform, the working wavelength of the helium-neon laser 1 is 632.8nm, the beam diameter (1 / e 2 ) of the light output by the helium-neon laser 1 is 0.54mm, which is expanded by 10 times through the beam expander 2, the spot diameter is adjusted to 2mm through the adjustable diaphragm 3, and the collimated parallel light beam passes through the focusing lens / focusing mirror with an effective focal length of 10cm and irradiates the sample to be measured located at about 1.5mm away from the back focal plane of the focusing mirror.

[0070] The method comprises:

[0071] The precision motion platform drives the sample to be measured to move in a two-dimensional plane according to a specific curve (including but not limited to grid curve, etc.), and ensures that the overlapping area of adjacent illumination spots is not less than 60%, as a spatial overlap constraint.

[0072] A camera 10cm away from the sample to be measured records a series of intensity information I of diffraction light field​m (q), m is the scanning position in the motion process, and q is the frequency domain coordinate.

[0073] The measured intensity information I m (q) is brought into the particle swarm superposition diffraction position correction computed imaging algorithm, and the reconstruction of the complex amplitude function O(r) of the to-be-measured sample and the reconstruction of the complex amplitude function P(r, λ) of the illumination probe are performed. Wherein, r is the spatial domain coordinate, and λ is the wavelength of the illumination probe.

[0074] As Figure 3 shown, the specific steps of the superposition diffraction position correction computed imaging method based on the particle swarm algorithm are as follows:

[0075] S1: First, the initial guess is given for the complex amplitude function P(r, λ) of the illumination probe and the complex amplitude function O(r) of the to-be-measured sample. The position coordinate before the offset is set as the position coordinate r of the motion platform, wherein the working wavelength of the illumination probe is 632.8nm.

[0076] S2: According to the preset scanning path, the motion scanning is performed, the exit wave formed by the interaction of the illumination probe P(r, λ) and the to-be-measured sample O(r) is calculated, and the exit wave complex amplitude function at different motion positions is obtained according to formula (1):

[0077]

[0078] Wherein, is the exit wave complex amplitude function of the illumination probe, and O m (r) represents the to-be-measured sample complex amplitude function in the probe at the mth scanning position.

[0079] S3: The exit wave complex amplitude function in the above step S2 is propagated to the Fourier space, so that the intensity information of the diffraction light field at different positions of the camera detection plane is obtained as:

[0080]

[0081] Wherein, Φ m (q, λ) is the intensity information of the diffraction light field of the illumination probe in the Fourier space at the mth scanning position, and prop is the propagation mode of the light field (including but not limited to Fresnel propagation, Fraunhofer propagation, angular spectrum propagation, etc.).

[0082] S4: The diffraction field complex amplitude intensity information is replaced by the pre-measured intensity information I m (q), that is, the amplitude constraint in the Fourier space, and the diffraction light field of the probe after updating at different positions is obtained as:

[0083]

[0084] wherein Φ' = Φ - Φ0 m (q, λ) is the intensity information of the updated diffraction light field after amplitude constraint at the mth scanning position of the illumination probe.

[0085] S5: Place the updated diffraction light field after amplitude constraint at the scanning position obtained in step S4 into the inverse propagation model in step S3, and calculate the complex amplitude update function of the exit wave at different positions according to formula (4):

[0086]

[0087] wherein, is the updated exit wave of the real space diffraction light field of the illumination probe, and iprop is the inverse propagation model of the light field.

[0088] S6: According to the complex amplitude functions of the exit wave before and after amplitude constraint obtained in S2 and S5, update the complex amplitude functions of the illumination probe and the sample to be measured simultaneously by applying the update function; and update the position coordinates of the illumination probe using the particle swarm algorithm:

[0089]

[0090]

[0091] r' = r + v (7)

[0092] v = v0 * w + c1 * (x m -x) + c2 * (y m -x) (8)

[0093] wherein, in formula (5)-(6), a and β belong to the iteration search step, and take values in [0, 1], which determine the convergence speed and convergence accuracy of the iteration algorithm. In formula (7)-(8), w, c1 and c2 belong to the position search step, and take values in [0, 1], which determine the convergence speed and convergence accuracy of the position search.

[0094] wherein, the weight linearly decreases:

[0095]

[0096] w max = 0.9, w min = 0.4; ger represents the current iteration number; and ger max represents the preset maximum iteration number.

[0097] S7: Repeat steps S2-S6 until the scanning motion of all positions of the sample to be measured is completed, and the amplitudes of the diffraction light field are all replaced, i.e. one iteration is completed. Repeat the above steps S2-S7 in a loop until the required number of iterations of the algorithm is completed.

[0098] S8: Finally, output the complex amplitude information of the illumination probe and the sample under test.

[0099] Example 4

[0100] The steps are the same as in Example 3, except that the weight w is updated differently.

[0101] Adaptive weight update formula:

[0102]

[0103] Among them, w max =0.9, w min =0.4; E n E represents the MSE error value for the current particle coordinates. min Φ is the MSE error value of the group's historical best position. m (q n λ) represents the intensity information of the diffracted light field in Fourier space representing the position coordinates of the nth particle at the mth scan position. m (q) represents the intensity information of the diffraction light field collected at the m-th scanning position.

[0104] Examples 3 and 4 use different methods to update positional inertia weights. Example 3 uses a linear weight reduction method, which is highly efficient and simple to operate. Example 4 uses an adaptive weight update formula, which can effectively improve the search accuracy.

[0105] When E n <E min This indicates that the particle search direction is correct, and the previous search direction should be retained with a larger value; E n >E min This indicates that the particle search direction deviates from the direction of the discovered optimal solution, and the influence of the current search direction on the next search direction should be reduced. When the average error E of all searched particles... avg Much larger than the group's historical best position E min When this happens, it indicates that the weight of the current search direction should be reduced. This is achieved through the formula... It can be seen that when the error value of this particle is close to the group's best historical position E... min The difference is greater than the average error E of all searched particles. avg With the group's historical best position E min The weighting of previous search directions should be reduced. When the particle's error value is close to the population's historical best position E... min The difference is less than the average error E of all searched particles. avg With the group's historical best position E minThe difference between the two should also reduce the weight of the previous search direction, but should not be reduced too much compared with the above case.

[0106] Embodiment 5

[0107] Figure 4 In (a) is the amplitude pattern of the sample to be measured used in the simulation process, Figure 4 In (b) is the phase pattern of the sample to be measured used in the simulation process; the illumination probe wavelength is 632.8nm, the sample to be measured size is 256x256pixel, and the beam diameter is a Gaussian beam of 64x64pixel.

[0108] Figure 5 is the two-dimensional motion plane trajectory of the sample to be measured provided by the embodiment of the application, the precision motion table drives the sample to be measured to move in a two-dimensional plane, the motion step length in the XY direction is 12pixel, and the sample to be measured scans an 8x8 region. Therefore, the COMS camera collects a total of 64 coherent diffraction fields. The position error is ±4pixel, the position correction algorithm is uniformly operated for 50 times before the position correction algorithm is operated for 200 times after.

[0109] Figure 6 is the sample amplitude reconstruction graph without position correction, Figure 7 is the sample amplitude reconstruction graph corrected by the particle swarm algorithm provided by the embodiment of the application, and Figure 6 and Figure 7 It can be seen that the sample amplitude reconstruction graph without position correction has low resolution, because the position disturbance causes the image reconstruction quality to decrease, resulting in the degradation of the sample to be measured in the layered diffraction computational imaging; the sample amplitude reconstruction graph corrected by the particle swarm algorithm overcomes the defects that the position disturbance causes the image reconstruction quality to decrease, resulting in the degradation of the sample to be measured in the layered diffraction computational imaging, and effectively improves the resolution of the reconstructed image. The layered diffraction position correction imaging method (PSO-PIE) based on the particle swarm algorithm provided by the application establishes a mathematical model for the Ptychography motion position, and iteratively updates the search position by using the particle swarm algorithm. Compared with other layered diffraction computational imaging methods, the method proposes a new direction for updating the position search, avoids the algorithm from falling into a local optimal solution too early, and has a faster search speed.

[0110] Those skilled in the art will readily understand that the above description is only the preferred embodiment of the application and is not intended to limit the application, and any modifications, equivalent replacements and improvements made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for correcting the imaging of a sample by a stacked diffraction system, the method comprising the steps of: providing a stacked diffraction system comprising a laser, a beam expander, an adjustable diaphragm, a focusing lens, a motion stage, and a camera, the laser being sequentially passed through the beam expander, the adjustable diaphragm, and the focusing lens to produce an illumination probe to irradiate a sample on the motion stage; and adjusting the adjustable diaphragm to correct the imaging of the sample by the stacked diffraction system. The method comprises: During the process that the motion platform drives the sample to be tested to move in a two-dimensional plane according to a preset curve, the sample to be tested is irradiated by using an irradiation probe, and the intensity information of the diffraction light field of the sample to be tested is collected by using a camera; A series of position coordinates are randomly generated with the center of each scanning position coordinate of the motion platform, the series of position coordinates are divided into a plurality of initial populations, the position coordinates in the populations are iteratively updated, the difference between the intensity information of the diffraction light field calculated by using the position coordinates in the populations and the intensity information of the diffraction light field collected by the camera is taken as the target, and the updated position coordinates of the motion platform are obtained after iteration to a preset maximum iteration number; The updated position coordinates of the motion platform are taken as the coordinates of the irradiation probe and the sample to be tested, the complex amplitude function of the irradiation probe and the sample to be tested is calculated, and the amplitude and phase of the sample to be tested and the irradiation probe are reconstructed therefrom; The updated position coordinates of the motion platform are iteratively obtained in the following manner: A series of position coordinates are randomly generated with the center of the initial position coordinate of the motion platform, the series of position coordinates are divided into a plurality of initial populations, For the ith initial population, the complex amplitude function of the irradiation probe and the sample to be tested at each position coordinate in the initial population is calculated, the complex amplitude function of the irradiation probe is multiplied by the complex amplitude function of the sample to be tested to obtain the complex amplitude function of the exit wave of the illumination probe, the exit wave complex amplitude function is propagated to the Fourier space, the intensity information of the diffraction light field is calculated, and the position coordinate with the minimum difference between the calculated intensity information of the diffraction light field and the intensity information of the diffraction light field collected by the camera is taken as the individual historical best position of the ith initial population; The individual historical best positions with the minimum difference in the plurality of initial populations are taken as the group historical best position; The weighted sum of the distance of position update in the last iteration, the difference between the individual historical best position and the randomly generated individual position, and the difference between the group historical best position and the randomly generated individual position is taken as the distance of position update in the next iteration, the populations are iteratively updated, the iteration is performed to a preset maximum iteration number, the distance of position update corresponding to the individual historical best position and the group historical best position in the final iteration is added to the position coordinate of the motion platform in the last iteration to obtain the updated position coordinates of the motion platform.

2. A method of laminated diffractive position correcting computed imaging according to claim 1, wherein, The distance of position update v in the next iteration is calculated in the following manner: v = v0 * w + c1 * (x m - x) + c2 * (y m - y) where w represents the position inertia weight at the last iteration, taking a value between [0, 1]; c1 represents the self-update weight, taking a value between [0, 1]; c2 represents the group update weight, taking a value between [0, 1]; x m represents the individual historical best position, y m represents the group historical best position, v0 is the distance of position update at the last iteration, x is the updated position coordinate of the motion platform obtained at the last iteration, and subscript m represents the mth scanning position.

3. A method of laminated diffractive position correcting computed imaging according to claim 2, wherein, The position inertia weight is adjusted in the following manner: wherein w max represents the maximum value of the position inertia weight, and takes a value between [0, 1], w min represents the minimum value of the position inertia weight, and takes a value between [0, 1], w max > w min ; ger represents the current iteration number, and ger max represents the preset maximum iteration number.

4. A method of laminated diffractive position correcting computed imaging according to claim 2, wherein, The position inertia weight is adjusted in the following manner: where w max represents the maximum value of the position inertia weight, and takes a value in the range of [0, 1], w min represents the minimum value of the position inertia weight, and takes a value in the range of [0, 1], w max > w min , E n is the MSE error value of the particle coordinates at the current iteration number, E min is the MSE error value of the historical best position of the population at the current iteration number, E avg is the average error of all search particles at the current iteration number.

5. A method of laminated diffractive position correcting computed imaging according to claim 2, wherein, The complex amplitude function of the sample to be tested is: wherein, a represents the first iterative search step length, and takes the value between [0, 1], O(r), O'(r') respectively represent the complex amplitude functions of the sample to be measured before and after the position coordinate is updated, P m (r, λ), P m '(r', λ) respectively represent the complex amplitude functions of the illumination probe before and after the position coordinate is updated at the mth scanning position, respectively represent the intensity information of the diffraction light field before and after the illumination probe amplitude is replaced at the mth scanning position, r represents the position coordinate of the motion platform, r' represents the updated position coordinate of the motion platform, λ represents the wavelength of the illumination probe, * represents the conjugate operation, and max represents the maximum value of the amplitude of each element in the complex amplitude function corresponding matrix.

6. A method of laminated diffractive position correcting computed imaging according to claim 5, wherein, The complex amplitude function of the irradiation probe after the position coordinate is updated at the mth scanning position is: Wherein, β represents a second iteration search step, and the value is between 0 and 1.

7. A stacked diffraction position correction computed imaging apparatus, comprising: Comprise: A processor and a memory; The memory is used to store computer programs or instructions; The processor is used to execute the computer programs or instructions in the memory, so that the method in any one of claims 1 to 6 is executed.

8. A stacked, diffractive, position-corrected, computed imaging system, characterized by, The imaging device comprises a laser, a beam expander, an adjustable diaphragm, a focusing mirror, a motion platform and a camera arranged in sequence along the optical axis direction; and the imaging device is used for correcting the coordinates of the irradiation probe and the sample to be measured.

9. A laminated, diffractive, position correcting, computed imaging system as in claim 8, wherein, The focusing mirror is a focusing lens or a focusing mirror.

Images