Coherent diffraction imaging method and device

Abstract

The disclosure provides a coherent diffraction imaging method, comprising: obtaining N frames of diffraction patterns of a target object, where coherent waves are diffracted to form outgoing waves when passing through the target object, the outgoing waves respectively form N frames of first distributions at the target object at N occurrence moments, N being a positive integer; on the basis of the inter-frame constraint of the N frames of first distribution and the N frames of diffraction patterns, target complex amplitude distribution of the target object is obtained by executing target iteration until the target complex amplitude distribution meets a preset convergence condition, the inter-frame constraint comprises updating similar distribution among the first distributions of the N frames in each target iteration so as to obtain first estimation of the first distributions of the N frames; and outputting the target complex amplitude distribution. The imaging method provided by the invention has good robustness and convergence.

Description

Technical Field

[0001] The present disclosure relates to the field of optical diffraction imaging, and in particular to a coherent diffraction imaging method and device. Background Art

[0002] Coherent Diffractive Imaging (CDI) is a lensless imaging technology that uses the coherence of electromagnetic waves such as light waves or X-rays to reconstruct the structural information of an object. This technology breaks through the diffraction limit of traditional optical microscopes and can analyze the structure of matter at the atomic scale, which is of great significance to the fields of materials science, biomedicine, physics, etc.

[0003] The basic principle of coherent diffraction imaging is based on Fourier transform and wave optics theory. In actual operation, diffraction information is usually received by the detector in the form of a diffraction pattern. The diffraction pattern can only record amplitude information but loses phase information, so it is necessary to use an algorithm to perform phase recovery to obtain the structural information of the sample.

[0004] The present invention provides a coherent diffraction imaging method, which improves the iterative convergence speed and obtains better sample imaging results.

[0005] The content of the background technology section is only the information known to the applicant personally, and does not mean that the above information has entered the public domain before the application date of this disclosure, nor does it mean that it can become the prior art of the present disclosure. Summary of the invention

[0006] The present invention provides a coherent diffraction imaging method and device, which can improve the iterative convergence speed of phase recovery.

[0007] In a first aspect, the present disclosure provides a coherent diffraction imaging method, comprising: obtaining N frames of diffraction patterns of a target object ({I} N ), wherein the coherent wave diffracts when passing through the target object to form an outgoing wave, and at N occurrence moments, the outgoing wave forms N frames of first distributions ({ψ} N ), when the outgoing wave propagates along the outgoing direction to the detector, the first distribution of the N frames ({ψ} N ) are transformed into the second distribution of N frames ({Ψ} N ), the N-frame diffraction pattern ({I} N ) is the second distribution of the N frames obtained by the detector ({Ψ} N ), wherein N is a positive integer; based on the N-frame first distribution ({ψ} N ) and the N-frame diffraction pattern ({I} N), obtaining the target complex amplitude distribution (O) of the target object by performing target iteration until the target complex amplitude distribution (O) satisfies a preset convergence condition, wherein the inter-frame constraint includes in each target iteration, N ) or the similarity distribution (ψ s ) is updated to obtain the first estimate ({ψ′} of the first distribution of the N frames N ); and outputting the target complex amplitude distribution (O).

[0008] In some implementations, for the mth iteration, m is a positive integer, and the N frames are first distributed ({ψ} N ) between similar distributions (ψ s,m ) is updated to obtain the first estimate of the first distribution of the N frames ({ψ′ m} N ) includes: determining an initial estimate ({ψ 0m} N ) similarity (S m ); Based on the similarity (S m ), determine the initial estimate of the first distribution of the N frames in the current iteration ({ψ 0m} N ) between the similar distributions (ψ s,m ) and the difference distribution ({ψ e,m} N ); Based on the similarity distribution (ψ s,m ) and the difference distribution ({ψ e,m} N ), determine the first estimate ({ψ′) of the first distribution of the N frames of the current iteration m} N ).

[0009] In some implementations, the first estimate ({ψ′ m} N )satisfy:

[0010] ψ′ m (r; n) = ψ e,m (r;n)+ρψ s,m (r)

[0011]

[0012] ψ e,m (r; n) = [1-S m (r)]ψ 0m (r; n)

[0013] S m (r) = C m (σ m (r))

[0014]

[0015] Wherein, r is the coordinate vector of the plane where the target object is located, n is a positive integer less than or equal to N, and ρ is used to keep the energy constant in the current iteration. is the initial estimate of the first distribution of the N frames ({ψ 0m} N ), σ m (r) is the initial estimate of the first distribution of the N frames ({ψ 0m} N )’s standard deviation distribution, C m is a conversion function used to convert the σ m (r) is converted into the similarity S m (r), α, β m and k m Used to adjust the shape of the transfer function.

[0016] In some implementations, for the mth iteration, m is a positive integer, the target iteration includes: obtaining a second estimate ({ψ″) of the first distribution of the N frames of the previous iteration m-1} N ); A second estimate ({ψ″) of the first distribution of the N frames based on the previous iteration m-1} N ) obtains the initial estimate of the first distribution of the current iteration N frames ({ψ 0m} N ); an initial estimate of the first distribution of the N frames ({ψ 0m} N ) applies the inter-frame constraint to obtain the first estimate ({ψ′ m} N ); A first estimate ({ψ′) of the first distribution of the N frames of the current iteration m} N ), the second estimate of the first distribution of the N frames of the previous iteration ({ψ″ m-1} N ), and the N-frame diffraction pattern ({I} N ), and obtain the second estimate ({ψ″) of the first distribution of the N frames of the current iteration m} N ).

[0017] In some implementations, the second estimate ({ψ″) of the first distribution of the N frames based on the previous iteration m-1} N ) obtains the initial estimate of the first distribution of the N frames of the current iteration ({ψ 0m} N ) includes: when the second estimate ({ψ″) of the first distribution of the N frames m-1} N ) is located in the support region, the second estimate ({ψ″) of the first distribution of the N frames m-1} N ) as the initial estimate of the first distribution of N frames ({ψ 0m} N ); or when the second estimate of the first distribution of the N frames ({ψ″ m-1} N ) is outside the support region, the second estimate ({ψ″ m-1} N ) has a value of zero in this region.

[0018] In some implementations, the obtaining of the second estimate ({ψ″) of the first distribution of the N frames of the current iteration m} N ) includes: based on the N-frame diffraction pattern ({I} N ), and obtain the second distribution estimate ({Ψ′ m} N ), the N-frame second distribution estimate of the current iteration ({Ψ′ m} N ) is the N-frame diffraction pattern ({I} N ) corresponding to the complex amplitude distribution of the outgoing wave at the detector; and a first estimate of the first distribution of the N frames of the current iteration ({ψ′ m} N ) and the second estimate of the first distribution of the N frames in the previous iteration ({ψ″ m-1} N ), determine the second distribution estimate ({Ψ′) of the N frames of the current iteration m} N ) is back-propagated to the target object at the current iteration of the second estimate ({ψ″) of the first distribution of the N frames m} N ).

[0019] In some implementations, the second estimate ({ψ″) of the N-frame first distribution of the current iteration m} N )satisfy: in, is an inverse Fourier transform function, k is a coordinate vector of the plane where the detector is located, n is a positive integer less than or equal to N, and δ is related to the first estimate of the first distribution of the Nth frame ({ψ′ m} N ) and the second estimate ({ψ″) of the first distribution of the N frames of the previous iteration m-1} N ) are linearly related.

[0020] In some implementations, the target iteration of the mth round further includes: when m=K, K is an integer and K≥0, a second estimate ({ψ″) of the first distribution of N frames based on the current iteration m} N ), and obtain an initial estimate of the target complex amplitude distribution (O), wherein the initial estimate of the target complex amplitude distribution (O) satisfies:

[0021]

[0022] Wherein, r is the coordinate vector of the plane where the target object is located, n is a positive integer less than or equal to N, K (r; n) is the initial estimate of the target complex amplitude distribution (O), P K (r) is the initial estimate of the probe complex amplitude distribution, and ∈ is a preset constant.

[0023] In some implementations, the target iteration of the mth round further includes: when m≥K, K is an integer and K≥0, a second estimate ({ψ″) based on the first distribution of N frames of the current iteration m-1} N ), update the target complex amplitude distribution (O).

[0024] In some implementations, the estimate of the target complex amplitude distribution (O) after the current iterative update satisfies:

[0025]

[0026] Wherein, r is the coordinate vector of the plane where the target object is located, n is a positive integer less than or equal to N, m-1 (r; n) is the estimate of the target complex amplitude distribution after the last round of iterative update, P m-1 (r) is the estimate of the probe complex amplitude distribution after the last round of iterative update, P m (r) is the estimate of the probe complex amplitude distribution after the current round of iterative update, * represents the conjugate complex number, |.| max represents a non-negative constraint, α 1 , α 2 , α 3 and α 4 A preset constant between 0 and 1.

[0027] In a second aspect, the present disclosure provides an imaging device, comprising: at least one storage medium storing at least one set of instruction sets for realizing coherent diffraction imaging; and at least one processor communicatively connected to the at least one storage medium, wherein when the imaging device is running, the at least one processor reads the at least one instruction set and implements the method described in the first aspect.

[0028] In some implementations, the device also includes: a coherent light source configured to emit a beam of coherent waves. When the device is in operation, the coherent waves are constrained to be irradiated by the probe onto the target object and diffracted; a detector configured to receive the outgoing waves generated by the diffraction and generate N frames of diffraction patterns.

[0029] In some implementations, the coherent waves may be at least one of electromagnetic radiation, photons, X-rays, electrons, neutrons, and protons.

[0030] It can be seen from the above technical solutions that the imaging method P100 provided by the present disclosure performs the target iteration process on the first distribution ({ψ} N ) between similar distributions (ψ s ) is estimated, and then based on the similarity distribution (ψ s ) The first estimate of the first distribution of N frames ({ψ′} N ) is updated. Due to the similar distribution (ψ s ) is adjusted in each iteration as the estimate of the first distribution of N frames is continuously updated. The imaging method P100 can adaptively utilize the similar information between frames in the iteration. Using the similar distribution (ψ s ) as the first estimate of the first distribution of N frames ({ψ′} N ) can improve the estimate of the first distribution of N frames ({ψ′} N ) gradually approaches the true distribution ({ψ} N ), thereby improving the iterative convergence speed of the target complex amplitude distribution (O).

[0031] Other functions of the coherent diffraction imaging method and apparatus provided by the present disclosure will be partially listed in the following description. Based on the description, the content introduced by the following figures and examples will be obvious to those of ordinary skill in the art. The creative aspects of the coherent diffraction imaging method and apparatus provided by the present disclosure can be fully explained by practicing or using the methods, apparatuses and combinations provided in the following detailed examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A schematic diagram of an imaging device provided according to some embodiments of the present disclosure is shown;

[0034] Figure 2 A schematic diagram of the hardware structure of a computing device provided according to some embodiments of the present disclosure is shown;

[0035] Figure 3 A flow chart of a coherent diffraction imaging method provided according to some embodiments of the present disclosure is shown;

[0036] Figure 4 A flowchart of applying inter-frame constraints according to some embodiments of the present disclosure is shown;

[0037] Figure 5 A flowchart of target iteration provided according to some embodiments of the present disclosure is shown;

[0038] Figure 6 A flowchart showing target iteration provided according to some embodiments of the present disclosure; and

[0039] Figure 7 A graph showing experimental comparison results provided according to some embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0040] The following description provides specific application scenarios and requirements of the present disclosure, with the purpose of enabling those skilled in the art to make and use the contents of the present disclosure. Various local modifications to the disclosed embodiments are apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments shown, but to the widest scope consistent with the claims.

[0041] The terms used herein are only used for the purpose of describing specific example embodiments and are not restrictive. For example, unless the context clearly indicates otherwise, as used herein, the singular forms "a", "an" and "the" may also include plural forms. When used in the present disclosure, the terms "include", "comprise" and / or "contain" mean to refer to the associated integers, steps, operations, elements and / or components, but do not exclude the existence of one or more other features, integers, steps, operations, elements, components and / or groups or other features, integers, steps, operations, elements, components and / or groups may be added in the system / method.

[0042] In view of the following description, these and other features of the present disclosure, as well as the operation and function of the related elements of the structure, and the economy of the combination and manufacture of the parts can be significantly improved. Reference is made to the accompanying drawings, all of which form a part of the present disclosure. However, it should be clearly understood that the drawings are for illustration and description purposes only and are not intended to limit the scope of the present disclosure. It should also be understood that the drawings are not drawn to scale.

[0043] The flow chart used in this disclosure illustrates the operation of the system implementation according to some embodiments in this disclosure. It should be clearly understood that the operation of the flow chart can be implemented out of order. On the contrary, the operation can be implemented in reverse order or simultaneously. In addition, one or more other operations can be added to the flow chart. One or more operations can be removed from the flow chart.

[0044] In the present disclosure, "X includes at least one of A, B or C" means that X includes at least A, or X includes at least B, or X includes at least C. That is, X may include only any one of A, B, and C, or may include any combination of A, B, and C and other possible contents / elements at the same time. Any combination of A, B, and C may be A, B, C, AB, AC, BC, or ABC.

[0045] In the present disclosure, unless explicitly stated otherwise, the association relationship generated between structures can be a direct association relationship or an indirect association relationship. For example, when describing "A is connected to B", unless it is explicitly stated that A is directly connected to B, it should be understood that A can be directly connected to B or indirectly connected to B; for another example, when describing "A is above B", unless it is explicitly stated that A is directly above B (AB is adjacent and A is above B), it should be understood that A can be directly above B or indirectly above B (AB is separated by other elements and A is above B). And so on.

[0046] Optical imaging technology has a profound impact on scientific research and industrial applications. Short-wavelength radiation, such as X-rays and high-energy electrons, is the main means to achieve nanometer or atomic resolution imaging. However, due to the manufacturing bottleneck of optical lenses with high imaging quality and large numerical aperture and the lack of suitable manufacturing materials, traditional optical microscopes have been unable to meet the imaging characterization needs of emerging fields of materials science and biology.

[0047] Coherent diffraction imaging technology provides a powerful imaging method for those bands where it is difficult to manufacture high-quality lenses. Coherent diffraction imaging technology forms a diffraction field through the interaction of a coherent light source and the sample to be measured, and reconstructs the amplitude and phase information of the sample through iterative calculation. Since it does not require high-performance imaging optical elements, it can be widely used on coherent radiation sources such as deep extreme ultraviolet, X-rays and electron beams.

[0048] The outgoing wave formed by the coherent wave passing through the sample to be tested is received by the detector in the form of a diffraction pattern. The diffraction pattern can only record the amplitude information but loses the phase information. Therefore, it is necessary to obtain the structural information of the sample through phase recovery. In the process of phase recovery, the iterative update method can be used to gradually approximate the true phase distribution of the sample to be tested. Due to the non-uniqueness of the solution to the phase recovery problem, certain constraints need to be imposed during the iteration to update the phase. Usually, the phase estimation can be gradually converged by alternating the application of support constraints and amplitude constraints.

[0049] In the related art, the convergence speed of phase recovery is improved by increasing the prior knowledge of the static area of ​​the sample or increasing the boundary information of the sample. The present disclosure provides a new coherent diffraction imaging method, which adds adaptive constraints in the iteration to make full use of the similarity information of the changing sample in multi-frame sampling, improves the iterative convergence speed and obtains better phase recovery results.

[0050] Figure 1 FIG. 1 is a schematic diagram of an imaging device 001 provided according to some embodiments of the present disclosure. The imaging device 001 includes a coherent light source 100, a detector 200, a storage medium, and a processor.

[0051] The coherent light source 100 is used to emit a coherent wave 110 that illuminates the target 002. The target 002 is a sample to be tested. In coherent diffraction imaging, the size of the sample to be tested matches the wavelength of the coherent wave 110 to ensure that a sufficiently strong diffraction signal that can be used for analysis can be generated while meeting the required imaging resolution. The target 002 can be a sample that changes with time. For example, the target 002 can be a growing crystal, a moving cell, a microorganism, a virus, etc. The imaging device 001 can perform coherent diffraction imaging on the target 002 that changes with time to obtain structural information of the target 002 at multiple times.

[0052] In order to adapt to targets 002 of different sizes and properties or different application requirements, the imaging device 001 can use different types of coherent waves 110. The imaging device 001 provided by the present disclosure can be applied to a variety of applications, and the specific coherent light source 100 used to generate the required coherent waves 110 depends on factors such as the properties of the target 002, the required resolution, and the research objectives.

[0053] In some embodiments, the coherent wave 110 may be electromagnetic radiation. For example, high-frequency parts such as visible light, near infrared, ultraviolet, vacuum ultraviolet and even X-rays. Electromagnetic radiation as a coherent wave 110 can be widely used in materials science, biomedicine, semiconductor devices, nanostructure research and other fields. For example, micro-nanostructure surface morphology analysis, crystal structure analysis, protein molecular structure, etc. In some embodiments, the coherent wave 110 may be a photon. Photons are quantum units of electromagnetic radiation. Photon coherent diffraction imaging can be applied to emerging fields such as quantum information processing and quantum imaging. For example, quantum optics, quantum information science, quantum state preparation and measurement in quantum computing, and exploration of imaging technology beyond classical limits. In some embodiments, the coherent wave 110 may be an X-ray. X-rays have the characteristics of short wavelength and high penetration, and can penetrate deep into the target. X-ray coherence can be guaranteed by advanced light sources such as synchrotron radiation sources and free electron lasers. X-rays as coherent waves 110 can be applied to material structure analysis (such as crystal structure, amorphous materials, complex macromolecules), nanoscale imaging (such as nanoparticles, biomacromolecules, internal cell structure), materials science (defect detection, stress analysis), cultural heritage protection (non-destructive testing), etc. In some embodiments, the coherent wave 110 can be an electron. The wave behavior of electrons needs to be significant at higher energy to form electron waves. Electron diffraction mainly occurs in electron microscopes, where the high-speed electron beam emitted by an electron gun interacts with the sample to produce a coherent diffraction pattern. Electron waves as coherent waves 110 can be applied to electron microscopy (including transmission electron microscope TEM and scanning electron microscope SEM), and the microstructure, chemical composition, crystallographic information, etc. of the sample can be directly observed. In some embodiments, the coherent wave 110 can be a neutron. Because neutrons have no charge and have specific interactions with atomic nuclei, they can penetrate many materials that are opaque to electromagnetic radiation and are selectively sensitive to certain elements and isotopes. Neutron beams generated by neutron sources (such as reactors or spallation neutron sources) can achieve coherent diffraction. The coherent beam as a coherent wave 110 can be used to study the magnetic structure, hydrogen / deuterium position, stress distribution, and distribution of complex alloy components of materials, and has important applications in the fields of condensed matter physics, earth science, energy materials, and biomedicine. In some embodiments, the coherent wave 110 can be a proton. As a heavy particle with a positive charge, the volatility of protons is significant at extremely high energies. Proton coherent diffraction can play a role in the fields of material structure research under extreme conditions, nuclear physics, and cosmic ray detection.

[0054] It should be noted that the above-mentioned scenarios in which different types of coherent waves 110 can be applied are exemplary descriptions of some scenarios in which the imaging device 001 provided by the present disclosure can be applied. Other application scenarios in which the imaging device 001 provided by the present disclosure can be used are within the scope of protection of the present disclosure.

[0055] When the imaging device 001 is in operation, the coherent wave 110 is constrained to be irradiated by the probe 120 onto the target 002 and diffracted. For ease of description, the plane where the target 002 is located and is perpendicular to the axial direction of the probe 120 can be agreed to be the target plane A. The target plane A is the plane where the probe 120 penetrates the target 002 and diffracts. After the probe 120 penetrates the target 002, an outgoing wave is formed, which reaches the detector 200 after propagating for a certain distance.

[0056] The probe 120 may be a coherent wave constrained to a certain size or shape (e.g., focused into a beam or narrow pulse). On the one hand, the imaging device 001 constrains the coherent wave 110 to the probe 120 to obtain a higher energy density, which helps to obtain a sufficiently strong diffraction signal for effective imaging. On the other hand, the imaging device 001 constrains the coherent wave 110 to the probe 120 so that the wavefront incident on the target 002 has a high degree of spatial coherence, thereby producing a clear, resolvable, high-resolution diffraction pattern during the diffraction process. The imaging device 001 can provide a higher spatial resolution by using a probe 120 of a smaller size.

[0057] Continue to refer to Figure 1 , the detector 200 is used to receive the outgoing wave generated by diffraction on the target object 002 and generate N frames of diffraction patterns. For the convenience of description, it can be agreed that the plane on which the detector 200 receives the outgoing wave is the detection plane B. After the probe 120 passes through the target object 002, it will carry the microstructure information of the target object 002, that is, after the diffraction occurs, the outgoing wave will carry the information of the target object 002 to the detection plane B. The detector 200 can record the intensity information of the outgoing wave at different positions on the detection plane B when it propagates to the detection plane B, and form a diffraction pattern. Different positions of the diffraction pattern correspond to different frequencies. The distance between the detector 200 and the target object 002 meets the far-field diffraction condition. The intensity of each point in the diffraction pattern is determined by the Fourier transform relationship, that is, after the complex amplitude distribution (first distribution) of the outgoing wave on the target plane A is Fourier transformed, a corresponding complex amplitude distribution (second distribution) is formed on the detection plane B in the far field. The imaging device 001 can collect the diffraction pattern of the target object 002 at a specific sampling frequency.

[0058] For the changing target 002, the detector 200 can obtain multiple frames of diffraction patterns at multiple sampling moments. The multiple sampling moments for collecting the diffraction patterns can be equally spaced or unequally spaced. The target 002 has spatial local consistency at the corresponding multiple diffraction moments (i.e., the time when diffraction occurs on the target plane A corresponding to the sampling moment of the diffraction pattern of the detection plane B). Specifically, for example, the target 002 is a continuously growing crystal. At adjacent diffraction moments, some areas of the target 002 have changed, while some areas remain relatively still. For another example, the target 002 is an object moving in the target plane A. At adjacent diffraction moments, the target 002 can undergo an overall displacement, but the interior of the partial area where the target 002 is located remains relatively still. Therefore, from the perspective of the target plane A, the target 002 shows spatial similarity at multiple diffraction moments. Reflected in the detection plane B, there is also inter-frame similarity between the multiple frame diffraction patterns.

[0059] As mentioned above, the imaging device 001 provided by the present disclosure can be adapted to application scenarios in different fields. The selection of the detector 200 needs to consider the type of coherent wave 110 used, as well as the imaging requirements (such as spatial resolution, energy resolution, dynamic range, time resolution, etc.), experimental conditions (such as sample type, experimental environment, data acquisition speed, etc.) and economic factors.

[0060] In some embodiments, the detector 200 may be a CCD (Charge-Coupled Device) detector. CCD detectors can provide higher quantum efficiency and lower readout noise, and are suitable for capturing weak diffraction signals. In some embodiments, the detector 200 may be a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor. CMOS image sensors have lower cost and higher readout speed. In some embodiments, the detector 200 may be a photodiode array detector. In some embodiments, the detector 200 may be a photon counting detector. For example, the detector 200 may be a silicon photomultiplier (SiPM) or a single photon avalanche diode (SPAD) detector. In some embodiments, the detector 200 may be an X-ray detector. For example, the detector 200 may be a scintillator coupled detector, a silicon based direct electron detector (DEDS) or a hybrid pixel detector (HEPDs). In some embodiments, the detector 200 may be an electronic detector. For example, the detector 200 may be a scintillation detector, a direct electron counting detector, or a fluorescent screen-CCD system detector. In some embodiments, the detector 200 may be a neutron detector. For example, a scintillator detector, in which neutrons interact with scintillator materials to produce visible light, which is then recorded by a photoelectric conversion device. For example, a 3He tube detector. For another example, a solid-state neutron detector can directly convert charged particles generated by a neutron capture reaction into electrical signals.

[0061] It should be noted that the above-mentioned scenarios in which different types of detectors 200 can be applied are exemplary descriptions of some scenarios in which the imaging device 001 provided by the present disclosure can be applied. Other application scenarios in which the imaging device 001 provided by the present disclosure can be used are within the scope of protection of the present disclosure.

[0062] Continue to refer to Figure 1The imaging device 001 includes at least one storage medium and at least one processor. The at least one processor is in communication connection with the at least one storage medium. The at least one storage medium stores at least one set of instruction sets for implementing the coherent diffraction imaging method provided in the present disclosure. The at least one processor can be in communication connection with the detector 200 to apply the collected N frames of diffraction patterns to the implementation of the coherent diffraction imaging method provided in the present disclosure. When the imaging device 001 is running, the at least one processor reads the at least one instruction set and implements the coherent diffraction imaging method provided in the present disclosure. The coherent diffraction method provided in the present disclosure will be described in detail later. In some embodiments, the imaging device 001 can integrate the at least one storage medium and the at least one processor into a computing device 300. The imaging device 001 may include one or more computing devices 300.

[0063] Figure 2 FIG. 1 is a schematic diagram showing a hardware structure of a computing device 300 provided according to some embodiments of the present disclosure. Figure 2 As shown, computing device 300 includes at least one storage medium 330 and at least one processor 320. In some embodiments, computing device 300 may also include an internal communication bus 310. In some embodiments, computing device 300 may also include a communication port 350. In some embodiments, computing device 300 may also include an I / O component 360.

[0064] Internal communication bus 310 may connect various system components, including storage media 330 and processor 320. I / O component 360 supports input / output between computing device 300 and other components.

[0065] The storage medium 330 may include a data storage device. The data storage device may be a non-temporary storage medium or a temporary storage medium. For example, the data storage device may include one or more of a disk 332, a read-only storage medium (ROM) 334, or a random access storage medium (RAM) 336. The storage medium 330 also includes at least one instruction set stored in the data storage device. The instruction set is a computer program code, and the computer program code may include a program, a routine, an object, a component, a data structure, a process, a module, etc. for executing the diffraction imaging method provided by the present disclosure.

[0066] The communication port 350 is used for data communication between the computing device 300 and the outside world. For example, the computing device 300 can be connected to the network 330 through the communication port 350. At least one processor 320 is connected to the at least one storage medium 330 through the internal communication bus 310. At least one processor 320 is used to execute the at least one instruction set. When the correlation diffraction imaging system 001 is running, the at least one processor 320 reads the at least one instruction set and executes the correlation diffraction imaging method provided by the present disclosure according to the instructions of the at least one instruction set.

[0067] The processor 320 can execute all the steps included in the related diffraction imaging method. The processor 320 can be in the form of one or more processors. The processor 320 can issue execution instructions. The processor 320 may include one or more hardware processors, such as a microcontroller, a microprocessor, a reduced instruction set computer (RISC), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a central processing unit (CPU), a graphics processing unit (GPU), a physical processing unit (PPU), a microcontroller unit, a digital signal processor (DSP), a field programmable gate array (FPGA), an advanced RISC machine (ARM), a programmable logic device (PLD), any circuit or processor capable of performing one or more functions, etc., or any combination thereof.

[0068] For illustrative purposes only, only one processor 320 is described in the computing device 300 in the present disclosure. However, it should be noted that the computing device 300 in the present disclosure may also include multiple processors, and therefore, the operations and / or method steps disclosed in the present disclosure may be performed by one processor as described in the present disclosure, or may be performed jointly by multiple processors. For example, if the processor 320 of the computing device 300 in the present disclosure performs step A and step B, it should be understood that step A and step B may also be performed jointly or separately by two different processors 320 (e.g., the first processor performs step A, the second processor performs step B, or the first and second processors perform steps A and B together).

[0069] In summary, the imaging device 001 provided in the present disclosure can emit a coherent wave 110 through a coherent light source 100, and constrain the coherent wave 110 to form a precise probe 120. The probe 120 irradiates the target object 002 and forms an outgoing wave after diffraction. The outgoing waves at multiple moments propagate to the detection plane B in the far field and are received by the detector 200 to form a multi-frame diffraction pattern. The computing device 300 restores the complex amplitude distribution of the target object 002 based on the received multi-frame diffraction pattern. The imaging device 001 can be applied to coherent wave diffraction imaging of various bands, can adapt to various types of detectors 200, has a simple structure, wide adaptability, and has a wide range of application scenarios.

[0070] Next, the coherent diffraction imaging method provided by the present disclosure (hereinafter referred to as the imaging method) is described in detail.

[0071] Figure 3 The flowchart of the imaging method P100 provided according to some embodiments of the present disclosure is shown. As mentioned above, when the imaging device 001 is running, the processor can read the instruction set stored in its local storage medium and / or database, and then execute the imaging method P100 according to the provisions of the instruction set.

[0072] The imaging method P100 provided in the present disclosure includes:

[0073] S110: Obtain N frames of diffraction patterns of target object 002 ({I} N ).

[0074] As mentioned above, the coherent wave 110 generated by the coherent light source 100 is diffracted to form an outgoing wave when passing through the target 002. At N occurrence moments, i.e., N diffraction occurrence moments, the outgoing wave forms N frames of first distributions ({ψ} N ). Wherein, N is a positive integer, and the symbol {·} N represents a set of N frames. The first distribution of the N frames ({ψ} N ) is the real spatial distribution of the outgoing wave at the target plane A. ψ(r;n) represents the distribution of the nth frame in the N-frame first distribution at the target plane A, where r is the coordinate vector on the target plane A. ψ(r;n) is abbreviated as ψ in the following text. The N-frame first distribution ({ψ} N ) is the complex amplitude distribution of the outgoing wave, including amplitude and phase information. When the outgoing wave propagates along the outgoing direction to the detector 200, the first distribution ({ψ} N ) are transformed into the corresponding N-frame second distribution ({Ψ} N ). N frames second distribution ({Ψ} N ) is the complex amplitude distribution of the outgoing wave when it propagates to the detection plane B. Ψ(k;n) represents the distribution of the nth frame in the N-frame second distribution at the detection plane B, where k is the coordinate vector on the detection plane B. Ψ(k;n) is abbreviated as Ψ in the following text. N ) and its corresponding N-frame second distribution ({Ψ} N ) satisfies the Fourier transform relationship.

[0075] The N-frame diffraction pattern ({I} N ) is the second distribution ({Ψ} of the N frames obtained by the detector 200 N ) intensity information. The detector 200 can record the N frames of the second distribution ({Ψ} N) at different positions on the detection plane B, and form N frames of diffraction patterns. The intensity information and the N frames of the second distribution ({Ψ} N ) is positively correlated with the amplitude of the diffraction pattern ({I} N ) corresponds to different frequencies.

[0076] S130: Based on the N-frame first distribution ({ψ} N ) and the N-frame diffraction pattern ({I} N ), and obtain the target complex amplitude distribution (O) of the target object by performing target iteration until the target complex amplitude distribution (O) satisfies a preset convergence condition.

[0077] The target complex amplitude distribution (O) is an estimate of the complex amplitude distribution of the target object 002 on the target plane A, including amplitude and phase information. The target complex amplitude distribution (O) can be obtained from the first distribution ({ψ} N ) is separated from the image. The imaging method P100 performs multiple target iterations to continuously update the first distribution ({ψ} N ) is estimated, and then the target complex amplitude distribution (O) is obtained.

[0078] The target object 002 has local consistency in space at the N diffraction occurrence moments. From the target plane A, the target object 002 shows spatial similarity at the N diffraction occurrence moments, that is, the first distribution of the N frames ({ψ} N ) have similar distributions (ψ s ). Reflected in the detection plane B, N-frame diffraction pattern ({I} N ) also exists between frames. Imaging method P100 takes similarity as the first distribution of N frames ({ψ} N ) is applied to the first distribution ({ψ} N ) in the estimation process.

[0079] The inter-frame constraint includes the following steps: N ) between similar distributions (ψ s ) is updated to obtain the first estimate ({ψ′} of the first distribution of the N frames N ). The first estimate ({ψ′} of the first distribution of the N frames N ) is the distribution based on similarity (ψ s ) for the first distribution ({ψ} N ). The imaging method P100 estimates the first distribution ({ψ} N ) can be estimated by one or more methods, using similar distribution (ψ s) is one of the ways to estimate and update it.

[0080] S150: Output the target complex amplitude distribution (O).

[0081] The target complex amplitude distribution (O) is an amplitude and phase estimation of the target object 002. The presentation form of the structure of the target object 002 can be an image restored according to the target complex amplitude distribution (O), or the amplitude information and the phase information can be visualized separately.

[0082] The imaging method P100 provided in the present disclosure performs target iteration on the first distribution ({ψ} N ) between similar distributions (ψ s ) is estimated, and then based on the similarity distribution (ψ s ) The first estimate of the first distribution of N frames ({ψ′} N ) is updated. Due to the similar distribution (ψ s ) is adjusted in each iteration as the estimate of the first distribution of N frames is continuously updated. The imaging method P100 can adaptively utilize the similar information between frames in the iteration. Using the similar distribution (ψ s ) as the first estimate of the first distribution of N frames ({ψ′} N ) can improve the estimate of the first distribution of N frames ({ψ′} N ) gradually approaches the true distribution ({ψ} N ), thereby improving the iterative convergence speed of the target complex amplitude distribution (O).

[0083] In the aforementioned step S130, the imaging method P100 performs similarity distribution (ψ s ) is updated to obtain the first estimate ({ψ′} of the first distribution of the N frames. N ). In each round of target iteration, similar distribution (ψ s ) With the first distribution ({ψ′} for N frames N )’s estimates are constantly changing and are also being updated.

[0084] Figure 4 A flowchart of applying inter-frame constraints according to some embodiments of the present disclosure is shown. For the mth iteration, m is a positive integer, and the first distribution ({ψ} N ) between similar distributions (ψ s,m ) is updated to obtain the first estimate of the first distribution of the N frames ({ψ′ m} N ) may include:

[0085] S135-1: Determine the initial estimate ({ψ 0m} N ) similarity (S m ).

[0086] The initial estimate of the first distribution of N frames ({ψ 0m} N ) is the first distribution of N frames at the beginning of the current round of iteration ({ψ} N ). For the first round of iterations, an initial estimate of the first distribution of N frames ({ψ 0m} N ) to perform an initialization operation. For example, the imaging device 001 can perform an initial estimation ({ψ 0m} N ) performs random initialization or zero-phase initialization.

[0087] In some embodiments, the determining of the initial estimate ({ψ 0m} N ) similarity (S m ) may include: calculating an initial estimate ({ψ 0m} N ) and obtaining the similarity (S m The discrete index can be used to reflect the first distribution of N frames ({ψ} N ), or the degree of deviation from the mean. For example, the dispersion indicator can be the standard deviation or the variance. It can be considered that the larger the dispersion indicator, the higher the initial estimate of the first distribution of N frames ({ψ 0m} N ) between m ) is lower.

[0088] S135-3: Based on the similarity (S m ), determine the initial estimate of the first distribution of the N frames in the current iteration ({ψ 0m} N ) between the similar distributions (ψ s,m ) and the difference distribution ({ψ e,m} N ).

[0089] Similar distribution (ψ s,m ) is the first distribution ({ψ} N ) is an estimate of the similar part in . For the first distribution ({ψ} N ), each frame can be represented by a similar distribution (ψ s ) and the difference distribution (ψe ). In the current iteration, we can use the similarity (S m ) for similar distribution (ψ s,m ) and N-frame difference distribution ({ψ e,m} N ) to update the valuation.

[0090] S135-5: Based on the similarity distribution (ψ s,m ) and the difference distribution ({ψ e,m} N ), determine the first estimate ({ψ′) of the first distribution of the N frames of the current iteration m} N ).

[0091] As mentioned above, the first estimate of the first distribution of N frames ({ψ′} N ) is the distribution based on similarity (ψ s ) for the first distribution ({ψ} T ). The imaging method P100 estimates the first distribution ({ψ} N ) can be estimated by one or more methods, using similar distribution (ψ s ) is one of the ways to estimate and update it. In other words, in the mth iteration, the first estimate of the first distribution of N frames ({ψ′ m} N ) is the initial estimate of the first distribution of frame N (ψ 0m )The result after applying inter-frame constraints.

[0092] The first estimate of the first distribution of N frames ({ψ′ m} N ) in each frame (ψ′ m ) can be represented by a similar distribution (ψ s,m ) and the difference distribution (ψ e,m ). For example, the first estimate of the first distribution of frame N ({ψ′ m} N ) can be expressed as a similar distribution (ψ s,m ) and N-frame difference distribution ({ψ e,m} N ) or the weighted sum.

[0093] In some embodiments, a similar distribution (ψ s For example, the transform domain may be a SVD (Singular Value Decomposition) transform domain. N), the size of each frame ψ is defined as x·y, then the first distribution of N frames ({ψ} N ) has a dimension of x·y·N. The first distribution ({ψ} N ) are rearranged into a two-dimensional matrix X of size N·xy. The first row of χ corresponds to the first distribution of frame N ({ψ} N ), and so on for the other rows. Perform SVD transformation on the two-dimensional matrix χ and decompose it into three matrices U, S, and V. Among them, U is the left singular vector matrix, S is the singular value matrix, and V is the right singular vector matrix. S is a diagonal matrix. For the continuously changing outgoing wave, that is, the first distribution of the N frames of continuous change, the first item in the S matrix will have a larger value. The first row V(1,:) of V is the normalized main component, and the corresponding first singular value S(1,1) is the largest, which means that V(1,:) has the largest energy component in all rows of the two-dimensional matrix χ. Therefore, take Its size is 1·xy. Rearranging it into a matrix of x·y can obtain a similar distribution (ψ s ).

[0094] The imaging method P100 provided by the present disclosure can be based on the N-frame first distribution ({ψ} N ) or the similarity between the corresponding N-frame distributions of the first distribution of N frames in the transform domain (S) to the first distribution of N frames ({ψ} N ) in the similar distribution (ψ s ) is used to characterize, and then based on the similarity distribution (ψ s ) to obtain the first estimate of the first distribution of N frames ({ψ′} N ). In each round of iteration (taking the mth round as an example), the similarity (S m ), similar distribution (ψ s,m ), N frame difference distribution ({ψ e,m} N ) and the first estimate of the first distribution of N frames ({ψ′ m} N ) are updated. The imaging method P100 makes full use of the first distribution of N frames ({ψ} N ), for the first distribution ({ψ} N ) can converge at a faster speed.

[0095] In some embodiments, the first estimate ({ψ′) of the first distribution of the N frames in the mth iteration m} N ) can satisfy the following relationship:

[0096] ψ′ m (r; n) = ψ e,m (r;n)+ρψ s,m (r)

[0097]

[0098] ψ e,m (r; n) = [1-S m (r)]ψ 0m (r; n)

[0099] S m (r) = C m (σ m (r))

[0100]

[0101] Among them, the first estimate of the first distribution of N frames ({ψ′ m} N ) can be represented by a similar distribution (ψ s,m ) and the difference distribution (ψ e,m ) is obtained by weighted summation. Difference distribution (ψ e,m ) is used to keep the energy constant in the current iteration. In some embodiments, the weighting coefficient p can be The summation symbol acts on the first distribution of N frames. is the initial estimate of the first distribution of the N frames ({ψ 0m} N ) is the average value of m It can be the initial estimate of the first distribution of the N frames ({ψ 0m} N ) is distributed with standard deviation.

[0102] C m is a conversion function used to convert the σ m Converted into the similarity S m α、β m and k m Used to adjust the shape of the conversion function. m It has an inverse S shape, and the magnitude of the function value is proportional to σ m α is a preset parameter value used to make the conversion function C m In σ m When it approaches zero, it reaches the preset α. As the iteration proceeds, β m and κ m The value of will be continuously updated. In order to have better robustness in the iterative process, in some embodiments, β m The calculation method can be β m =0.9·s1+0.2·s2. Among them, s1 is the σ in the mth iteration. m The lowest 10% average value, s2 is σ mThe imaging method P100 uses the average of the highest 10% and the lowest 10% instead of the highest and lowest values, which can achieve a more stable convergence effect and make the iterative process more robust. m With σ m In some embodiments, κ m Can be set to m multiples of, for example, κ m Can be set to m 50 times the standard deviation.

[0103] As mentioned above, the imaging method P100 provided by the present disclosure is for the first distribution ({ψ} N ) can be estimated by one or more methods, using similar distribution (ψ s ) is one of the ways to estimate and update it. In other words, in the mth iteration, the first estimate of the first distribution of N frames ({ψ′ m} N ) is the initial estimate of the first distribution of frame N (ψ 0m ) is the result after applying the inter-frame constraint. In addition to the estimation method of applying the inter-frame constraint, the imaging method P100 can also apply other constraints to the N-frame first distribution to obtain the initial estimation of the N-frame first distribution ({ψ 0m} N ) and the second estimate of the first distribution of N frames ({ψ″ m} N ).

[0104] Figure 5 A flowchart of target iteration according to some embodiments of the present disclosure is shown. For the mth round of iteration, the target iteration may include:

[0105] S131: Obtain the second estimate ({ψ″) of the first distribution of the N frames in the previous iteration m-1} N ).

[0106] The second estimate of the first distribution of the N frames ({ψ″ m-1} N ) is the first distribution ({ψ} for N frames obtained after the previous iteration N )’s estimation results.

[0107] S133: A second estimate ({ψ″) of the first distribution of the N frames in the previous iteration m-1} N ) obtains the initial estimate of the first distribution of the current iteration N frames ({ψ 0m} N ).

[0108] In some embodiments, the imaging method P100 performs a second estimation ({ψ″) of the first distribution of the N frames. m-1} N ) applies support constraints to transform the N-frame first distribution ({ψ} N The second estimate ({ψ″) of the first distribution of the N frames based on the previous iteration is limited within the constraint range. m-1} N ) obtains the initial estimate of the first distribution of the N frames of the current iteration ({ψ 0m} N ) includes: when the second estimate ({ψ″) of the first distribution of the N frames m-1} N ) is located in the support region, the second estimate ({ψ″) of the first distribution of the N frames m-1} N ) as the initial estimate of the first distribution of N frames ({ψ 0m} N ); or when the second estimate of the first distribution of the N frames ({ψ″ m-1} N ) is outside the support region, the second estimate ({ψ″ m-1} N ) has a value of zero in this region.

[0109] S135: Initial estimation of the first distribution of the N frames ({ψ 0m} N ) applies the inter-frame constraint to obtain the first estimate ({ψ′ m} N ).

[0110] The initial estimate of the first distribution of the N frames ({ψ 0m} N ) The method for applying the inter-frame constraint is as described in S135-1 to S135-5 above and will not be repeated here.

[0111] S137: A first estimate ({ψ′) of the first distribution of the N frames based on the current iteration m} N ), the second estimate of the first distribution of the N frames of the previous iteration ({ψ″ m-1} N ), and the N-frame diffraction pattern ({I} N ), and obtain the second estimate ({ψ″) of the first distribution of the N frames of the current iteration m} N ).

[0112] In some embodiments, the imaging method P100 performs the first estimation ({ψ′) of the first distribution of the N frames of the current iteration. m} N ) and the second estimate ({ψ″) of the first distribution of the N frames in the previous iteration m-1} N ) propagates to the detection plane B, and applies a modulus constraint to ensure energy conservation during the propagation process. Specifically, the imaging method P100 can be based on the N-frame diffraction pattern ({I} N ), and obtain the second distribution estimate ({Ψ′ m} N ). The N-frame second distribution estimate ({Ψ′ m} N ) is the N-frame diffraction pattern ({I} N ) is the estimated value of the complex amplitude distribution of the outgoing wave at the detector. N ) and the second distribution of N frames at the detection plane B ({Ψ} N ) satisfies the Fourier transform relationship. Therefore, the initial estimate Ψ of the second distribution of N frames in the current iteration on the detection plane B can be obtained based on the Fourier transform: 0m In some embodiments, in, represents Fourier transform. By increasing the increment of δ, this method can effectively avoid the iteration from being stagnant. In some embodiments, δ = ψ′ m (r; n)-ψ″ m-1 (r; n). Of course, ψ′ m and ψ″ m-1 The coefficients of can be adjusted according to actual needs. Apply a modulus constraint to the initial estimate of the second distribution of N frames to obtain the second distribution estimate of N frames ({Ψ′ m} N ). The way to impose the modulus constraint on each frame in the initial estimate of the second distribution of N frames can be: Where I corresponds to N frames of diffraction patterns ({I} N ) in each frame.

[0113] In some embodiments, the imaging method P100 calculates the second distribution estimate ({Ψ′) of the N frames on the detection plane B. m} N ) is back-propagated to the target plane A to obtain the second estimate ({ψ″) of the first distribution of the N frames m} N Specifically, the imaging method P100 may be based on a first estimate ({ψ′) of the first distribution of the N frames of the current iteration. m}N ) and the second estimate of the first distribution of the N frames in the previous iteration ({ψ″ m-1} N ), determine the second distribution estimate ({Ψ′) of the N frames of the current iteration m} N ) is back-propagated to the target object at the current iteration of the second estimate ({ψ″) of the first distribution of the N frames m} N In some embodiments, the second estimate ({ψ″) of the N-frame first distribution of the current iteration m} N ) satisfies: in represents the inverse Fourier transform. The first estimate of the first distribution of the N frames ({ψ′ m} N ) and the second estimate ({ψ″) of the first distribution of the N frames of the previous iteration m-1} N ) is linearly related. In some embodiments, δ = ψ' m (r; n)-ψ″ m-1 (r; n). Of course, ψ′ m and ψ″ m-1 The coefficient of can be adjusted according to actual needs. This method can effectively avoid the iteration from stagnating by increasing the increment of δ.

[0114] In summary, the imaging method P100 provided by the present disclosure is to divide the N frames into the first distribution ({ψ} N ) propagates back and forth between the target plane A and the detection plane B to perform N frames of the first distribution ({ψ} N ) is updated. In each iteration (taking the mth round as an example), the initial estimate of the first distribution of the N frames ({ψ 0m} N ) applies the inter-frame constraint to obtain the first estimate ({ψ′ m} N ), we can make full use of the initial estimate of the first distribution of N frames ({ψ 0m} N ) is conducive to improving the convergence speed.

[0115] The previous article describes in detail how to obtain the first distribution of N frames ({ψ} N Next, how to obtain the target complex amplitude distribution (O) is described in detail. Figure 6 A flowchart of target iteration provided according to some embodiments of the present disclosure is shown.

[0116] In some embodiments, the target iteration of the mth round further includes: when m=K, K is an integer and K≥0, a second estimate ({ψ″) of the first distribution of N frames of the current iteration is m} N ), and obtain the initial estimate of the target complex amplitude distribution (O). Wherein, K may be a preset number of iteration rounds. After completing K rounds of iteration, the target complex amplitude distribution (O) is initialized. Wherein, the initial estimate of the target complex amplitude distribution (O) satisfies:

[0117]

[0118] Among them, O K (r; n) is the initial estimate of the nth frame of the target complex amplitude distribution (O), P K (r) is the initial estimate of the probe complex amplitude distribution, and ∈ is a preset constant used to prevent the denominator from having a zero value.

[0119] When m ≥ K, the second estimate ({ψ m} N ), update the target complex amplitude distribution (O). Wherein, when m=K, the imaging method P100 can obtain the initial estimate of the target complex amplitude distribution (O) and the probe complex amplitude distribution (P) according to the above initialization method. After obtaining the initial estimate of the probe complex amplitude distribution (P), support constraints and modulus constraints can be applied to it for updating.

[0120] When m>K, the estimation of the nth frame of the target complex amplitude distribution (O) after the current iterative update satisfies:

[0121]

[0122]

[0123] Among them, O m-1 (r; n) is the estimate of the target complex amplitude distribution after the last round of iterative update. m-1 (r) is the estimate of the probe complex amplitude distribution after the last round of iterative update. m (r) is the estimate of the probe complex amplitude distribution after the current round of iterative update. * represents a conjugate complex number. |.| max represents a non-negative constraint. 1 , α 2 , α 3 and α 4 A preset constant between 0 and 1.

[0124] When the estimation of the target complex amplitude distribution (O) satisfies a preset convergence condition, the iteration is stopped.

[0125] Figure 7 A graph showing experimental comparison results provided according to some embodiments of the present disclosure is shown. Figure 7 The figure shows the numerical simulation imaging results of the growth process of polystyrene sulfonate (3,4-ethylenedioxythiophene, PEDOT:PSS) crystal samples. Figure 7 (a) It can be seen that the convergence speed of the imaging method P100 provided by the present disclosure is significantly faster than that of the traditional CDI method, and convergence is achieved within a few hundred iterations. Figure 7 (b)-(d) show the amplitude and phase reconstructed using imaging method P100 for selected frames at t1=0s, t2=2.5s, and t3=5s, respectively. The results of conventional CDI at t3=5s are shown in Figure 7 (f) The quality of the reconstruction results of the imaging method P100 is significantly better than that of the traditional CDI method. The reconstruction results of the traditional CDI method show obvious artifacts that hinder the clarity of the information inside the crystal. Figure 7 (g) and (h) show similar distributions (ψ s ) and similarity S. Figure 7 (b)-(d) shows that the similarity S of regions with large sample changes is lower, e.g. Figure 7 As shown in the black area in (h). Similarly, the similarity parts obtained at the corresponding positions are also small. The simulation experiment shows that the imaging method P100 can effectively extract the similarity parts between the first distributions of N frames and accelerate the convergence of the algorithm through inter-frame constraints.

[0126] In summary, the imaging method P100 provided in the present disclosure performs target iteration on the first distribution ({ψ} N ) between similar distributions (ψ s ) is estimated, and then based on the similarity distribution (ψ s ) The first estimate of the first distribution of N frames ({ψ′} N ) is updated. Due to the similar distribution (ψ s ) is adjusted in each iteration as the estimate of the first distribution of N frames is continuously updated. The imaging method P100 can adaptively utilize the similar information between frames in the iteration. Using the similar distribution (ψ s ) as the first estimate of the first distribution of N frames ({ψ′} N ) can improve the estimate of the first distribution of N frames ({ψ′} N ) gradually approaches the true distribution ({ψ} N ), thereby improving the iterative convergence speed of the target complex amplitude distribution (O).

[0127] The above describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require a specific order or a continuous order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] In summary, after reading this detailed disclosure, those skilled in the art will appreciate that the foregoing detailed disclosure may be presented only by way of example and may not be limiting. Although not explicitly stated herein, those skilled in the art will appreciate that the disclosure requires encompassing various reasonable changes, improvements, and modifications to the embodiments. These changes, improvements, and modifications are intended to be proposed by the disclosure and are within the spirit and scope of the exemplary embodiments of the disclosure.

[0129] In addition, certain terms in the present disclosure have been used to describe embodiments of the present disclosure. For example, "one embodiment", "embodiment" and / or "some embodiments" mean that specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present disclosure. Therefore, it can be emphasized and should be understood that two or more references to "embodiment" or "one embodiment" or "alternative embodiment" in various parts of the present disclosure do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics may be appropriately combined in one or more embodiments of the present disclosure.

[0130] It should be understood that in the foregoing description of the embodiments of the present disclosure, in order to help understand a feature and for the purpose of simplifying the present disclosure, the present disclosure combines various features in a single embodiment, drawing or its description. However, this does not mean that the combination of these features is necessary. When reading the present disclosure, it is entirely possible for those skilled in the art to mark out some of the devices as separate embodiments. In other words, the embodiments in the present disclosure can also be understood as the integration of multiple secondary embodiments. This is also true when the content of each secondary embodiment is less than all the features of a single aforementioned disclosed embodiment.

[0131] Each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, documents, articles, etc., cited herein, except any historical prosecution documents to which it is relevant, any equivalent that may be inconsistent or conflicting with this document, or any equivalent historical prosecution documents that may have a limiting effect on the broadest scope of the claims, may be incorporated herein by reference and used for all purposes now or hereafter associated with this document. In addition, in the event of any inconsistency or conflict between the descriptions, definitions, and / or use of terms associated with any of the incorporated materials and the terminology, descriptions, definitions, and / or use associated with this document, the terminology in this document shall control.

[0132] Finally, it should be understood that the embodiments of the application disclosed herein are explanations of the principles of the embodiments of the present disclosure. Other modified embodiments are also within the scope of the present disclosure. Therefore, the embodiments disclosed in the present disclosure are merely examples and not limitations. Those skilled in the art can adopt alternative configurations according to the embodiments in the present disclosure to realize the applications in the present disclosure. Therefore, the embodiments of the present disclosure are not limited to the embodiments accurately described in the application.

Claims

1. A coherent diffraction imaging method, characterized in that: include: Obtain N frames of diffraction patterns of the target object ({I} N ), wherein the coherent wave diffracts when passing through the target object to form an outgoing wave, and at N occurrence moments, the outgoing wave forms N frames of first distributions ({ψ} N ), the first distribution of the N frames when the outgoing wave propagates along the outgoing direction to the detector ({ψ} N ) are transformed into the second distribution of N frames ({Ψ} N ), the N-frame diffraction pattern ({I} N ) is the second distribution of the N frames obtained by the detector ({Ψ} N ), wherein N is a positive integer; Based on the N-frame first distribution ({ψ} N ) and the N-frame diffraction pattern ({I} N ), obtaining the target complex amplitude distribution (O) of the target object by performing target iteration until the target complex amplitude distribution (O) satisfies a preset convergence condition, wherein the inter-frame constraint includes in each target iteration, N ) or the similarity distribution (ψ s ) is updated to obtain the first estimate ({ψ′} of the first distribution of the N frames N );as well as The target complex amplitude distribution (O) is output.

2. The method according to claim 1, characterized in that For the mth iteration, m is a positive integer, and the first distribution ({ψ} N ) between similar distributions (ψ s，m ) is updated to obtain the first estimate of the first distribution of the N frames ({ψ′ m } N )include: Determine the initial estimate ({ψ 0m } N ) similarity (S m ); Based on the similarity (S m ), determine the initial estimate of the first distribution of the N frames in the current iteration ({ψ 0m } N ) between the similar distributions (ψ s，m ) and the difference distribution ({ψ e，m } N );as well as Based on the similarity distribution (ψ s，m ) and the difference distribution ({ψ e，m } N ), determine the first estimate ({ψ′) of the first distribution of the N frames of the current iteration m } N ), The first estimate ({ψ′) of the first distribution of the N frames in the current iteration m } N )satisfy: ψ′ m (r;n)=ψ e，m (r;n)+ρψ s，m (r) ψ e，m (r;n)=[1-S m (r)]ψ 0m (r;n) S m (r)=C m (s m (r)) Wherein, r is the coordinate vector of the plane where the target object is located, n is a positive integer less than or equal to N, and ρ is used to keep the energy constant in the current iteration. is the initial estimate of the first distribution of the N frames ({ψ 0m } N ), σ m (r) is the initial estimate of the first distribution of the N frames ({ψ 0m } N )’s standard deviation distribution, C m is a conversion function used to convert the σ m (r) is converted into the similarity S m (r), α, β m and κ m Used to adjust the shape of the transfer function.

3. The method according to any one of claims 1 to 2, characterized in that: For the mth iteration, m is a positive integer, and the target iteration includes: Get the second estimate ({ψ″) of the first distribution of the N frames in the previous iteration m-1 } N ); Based on the second estimate ({ψ″) of the first distribution of the N frames in the previous iteration m-1 } N ) obtains the initial estimate of the first distribution of the current iteration N frames ({ψ 0m } N ); The initial estimate of the first distribution of the N frames ({ψ 0m } N ) applies the inter-frame constraint to obtain the first estimate ({ψ′ m } N ); Based on the first estimate ({ψ′) of the first distribution of the N frames of the current iteration m } N ), the second estimate of the first distribution of the N frames of the previous iteration ({ψ″ m-1 } N ), and the N-frame diffraction pattern ({I} N ), and obtain the second estimate ({ψ″) of the first distribution of the N frames of the current iteration m } N ).

4. The method according to claim 3, characterized in that The second estimate ({ψ″) of the first distribution of the N frames based on the previous iteration m-1 } N ) obtains the initial estimate of the first distribution of the N frames of the current iteration ({ψ 0m } N )include: When the second estimate of the first distribution of the N frames ({ψ″ m-1 } N ) is located in the support region, the second estimate ({ψ″) of the first distribution of the N frames m-1 } N ) as the initial estimate of the first distribution of N frames ({ψ 0m } N );or When the second estimate of the first distribution of the N frames ({ψ″ m-1 } N ) is outside the support region, the second estimate ({ψ″ m-1 } N ) has a value of zero in this region.

5. The method according to claim 3, characterized in that The second estimate ({ψ″) of the first distribution of the N frames of the current iteration is obtained m } N )include: Based on the N-frame diffraction pattern ({I} N ), and obtain the second distribution estimate ({Ψ′ m } N ), the N-frame second distribution estimate of the current iteration ({Ψ′ m } N ) is the N-frame diffraction pattern ({I} N ) an estimated value of the complex amplitude distribution of the outgoing wave at the detector corresponding to ; and Based on the first estimate ({ψ′) of the first distribution of the N frames of the current iteration m } N ) and the second estimate of the first distribution of the N frames in the previous iteration ({ψ″ m-1 } N ), determine the second distribution estimate ({Ψ′) of the N frames of the current iteration m } N ) is back-propagated to the target object at the current iteration of the second estimate ({ψ″) of the first distribution of the N frames m } N ).

6. The method according to claim 3, characterized in that The second estimate ({ψ″) of the first distribution of the N frames of the current iteration m } N )satisfy: in, is an inverse Fourier transform function, k is a coordinate vector of the plane where the detector is located, n is a positive integer less than or equal to N, and δ is related to the first estimate of the first distribution of the Nth frame ({ψ′ m } N ) and the second estimate ({ψ″) of the first distribution of the N frames of the previous iteration m-1 } N ) are linearly related.

7. The method according to claim 3, characterized in that The target iteration of the mth round further includes: When m=K, K is an integer and K>0, the second estimate ({ψ″) of the first distribution of the N frames of the current iteration is m } N ), and obtain an initial estimate of the target complex amplitude distribution (O), wherein the initial estimate of the target complex amplitude distribution (O) satisfies: Wherein, r is the coordinate vector of the plane where the target object is located, n is a positive integer less than or equal to N, K (r; n) is the initial estimate of the target complex amplitude distribution (O), P K (r) is the initial estimate of the probe complex amplitude distribution, and ∈ is a preset constant.

8. The method according to claim 3, characterized in that The target iteration of the mth round further includes: When m≥K, K is an integer and K≥0, the second estimate ({ψ″) of the first distribution of the N frames of the current iteration is m-1 } N ), update the target complex amplitude distribution (O).

9. The method according to claim 8, characterized in that The estimate of the target complex amplitude distribution (O) after the current iterative update satisfies: Wherein, r is the coordinate vector of the plane where the target object is located, n is a positive integer less than or equal to N, m-1 (r; n) is the estimate of the target complex amplitude distribution after the last round of iterative update, P m-1 (r) is the estimate of the probe complex amplitude distribution after the last round of iterative update, P m (r) is the estimate of the probe complex amplitude distribution after the current round of iterative update, * represents the conjugate complex number, |.| max represents a non-negative constraint, and α1, α2, α3, and α4 are preset constants ranging from 0 to 1.

10. An imaging device comprising: At least one storage medium storing at least one set of instructions for implementing coherent diffraction imaging; as well as at least one processor, in communication with the at least one storage medium, Wherein when the imaging device is running, the at least one processor reads the at least one instruction set and implements the method according to any one of claims 1-9.

11. The apparatus of claim 10, further comprising: A coherent light source is configured to emit a coherent wave, and when the device is in operation, the coherent wave is constrained so that the probe irradiates the target and diffracts; The detector is configured to receive the outgoing wave generated by the diffraction and generate N frames of diffraction patterns.

12. The device according to claim 11, characterized in that The coherent waves may be at least one of electromagnetic radiation, photons, X-rays, electrons, neutrons and protons.

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