Multi-wavelength coherent diffraction imaging phase recovery algorithm and system
By combining the multi-wavelength coherent diffraction imaging algorithm with total variation denoising and the alternating direction algorithm, the problem of low phase recovery accuracy of traditional coherent diffraction imaging in high-noise environments is solved, and efficient phase recovery and high-resolution imaging are achieved.
Patent Information
- Application Number
- CN202510878526.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-27
- Publication Date
- 2025-10-17
AI Technical Summary
Traditional coherent diffraction imaging methods have low phase recovery accuracy and slow convergence speed in high-noise environments or complex object structures, which limits their effectiveness in practical applications.
A multi-wavelength coherent diffraction imaging algorithm is adopted, combined with total variation denoising technology and alternating direction algorithm. The diffraction pattern is obtained by using multiple incident lights of different wavelengths and the phase information is restored based on the alternating direction method and total variation algorithm.
The phase recovery accuracy and algorithm convergence speed are improved, the imaging resolution and clarity are enhanced, the noise interference is reduced, and the image detail information is retained.
Smart Images
Figure CN120800247A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of coherent diffraction imaging, and particularly to a multi-wavelength coherent diffraction imaging phase recovery algorithm and system. BACKGROUND
[0002] With the development of biomedical and nanotechnology, the demand for high-resolution imaging technology is increasing. Photoelectric detectors are effective in measuring light field intensity, but cannot record the phase information of the light field, which limits many imaging and sensing applications. Light field intensity measurement can only provide the modulus of the complex amplitude, without capturing the phase component, so the direct recovery of the complex amplitude is limited. To solve the above problems, two commonly used methods are holography and coherent diffraction imaging (CDI). Holography recovers the complex amplitude through the interference pattern of the reference wave and the object wave, but increases the complexity of the light source and environmental interference. In contrast, CDI, as a high-resolution imaging technology without traditional lens imaging, has been widely concerned because it can directly obtain the three-dimensional structure information of the object without physical contact.
[0003] CDI theoretically provides imaging capability close to the maximum resolution by recording diffraction patterns and using iterative algorithms to reconstruct object images. However, phase recovery is a non-convex optimization problem, and the convergence of the solution of the traditional phase recovery algorithm cannot be completely guaranteed. By using iterative algorithms such as G-S (Gauss-Seidel) to recover the phase information, certain sample prior information is required, and it is easy to fall into a local optimal solution. This makes the traditional CDI method often have problems of low phase recovery accuracy and slow convergence speed when facing high noise environment or complex object structure, which limits its effect in practical applications. SUMMARY
[0004] The purpose of the present application is to provide a multi-wavelength coherent diffraction imaging phase recovery algorithm and system, which combines total variation denoising technology with alternating direction algorithm, effectively suppresses noise in the image, and improves phase recovery accuracy and convergence speed.
[0005] To achieve the above purpose, the present application provides the following solutions:
[0006] In a first aspect, the present application provides a multi-wavelength coherent diffraction imaging phase recovery algorithm, comprising:
[0007] Irradiating a sample to be measured with incident light of a plurality of different wavelengths to obtain a diffraction pattern corresponding to each incident light;
[0008] According to the diffraction patterns corresponding to all incident lights, based on the alternating direction method and the total variation algorithm, a phase recovery result of the sample to be measured is obtained.
[0009] In a second aspect, the present application provides a multi-wavelength coherent diffraction imaging phase recovery system, comprising:
[0010] an image acquisition module, configured to irradiate a sample to be measured with incident light of a plurality of different wavelengths, to obtain a diffraction pattern corresponding to each incident light;
[0011] a phase recovery module, configured to obtain a phase recovery result of the sample to be measured based on an alternating direction method and a total variation algorithm according to the diffraction patterns corresponding to all the incident light.
[0012] According to the specific embodiments provided in the present application, the following technical effects are disclosed:
[0013] The present application provides a multi-wavelength coherent diffraction imaging phase recovery algorithm and system, by irradiating a sample to be measured with incident light of a plurality of different wavelengths, obtaining diffraction patterns corresponding to a plurality of wavelengths, obtaining greater phase or depth changes than a single wavelength, enhancing the constraint on the phase information of the object, so that more prior knowledge can be obtained in the phase recovery process, reducing the generation of phase ambiguity and artifacts, and improving the algorithm convergence speed and imaging resolution and clarity; by combining the total variation denoising technology with the alternating direction algorithm, using the smoothing characteristics of the total variation denoising technology in the spatial domain, the noise in the image is effectively suppressed, while the detail information of the image is retained. BRIEF DESCRIPTION OF DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0015] Figure 1 It is an application environment diagram of a multi-wavelength coherent diffraction imaging phase recovery algorithm in an embodiment of the present application;
[0016] Figure 2 It is a flowchart of a multi-wavelength coherent diffraction imaging phase recovery algorithm provided in an embodiment of the present application;
[0017] Figure 3 It is a detailed flowchart of step 202 in the embodiment; Figure 2
[0018] Figure 4 It is a flowchart of using an alternating direction method for phase recovery provided in an embodiment of the present application;
[0019] Figure 5 A functional module schematic diagram of a multi-wavelength coherent diffraction imaging phase recovery system provided by an embodiment of the present application is shown in the figure;
[0020] Figure 6 A working principle schematic diagram of an image acquisition module provided by an embodiment of the present application is shown in the figure;
[0021] Figure 7 An optical path schematic diagram of an image acquisition module provided by an embodiment of the present application is shown in the figure;
[0022] Figure 8 A structural schematic diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION
[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.
[0024] The above purposes, features and advantages of the present application can be more obvious and easy to understand. The present application will be described in further detail below with reference to the drawings and specific embodiments.
[0025] The multi-wavelength coherent diffraction imaging phase recovery algorithm provided by the embodiments of the present application can be applied in an application environment as shown in the figure. Figure 1 The terminal 102 communicates with the server 104 through a network. The data storage system can store data required to be processed by the server 104. The data storage system can be separately arranged, can be integrated on the server 104, or can be placed on a cloud or other servers. The terminal 102 can send diffraction patterns corresponding to incident lights of different wavelengths to the server 104. After receiving the diffraction patterns, the server 104 obtains a phase recovery result of the sample to be measured based on the alternating direction method and the total variation algorithm according to the diffraction patterns corresponding to all incident lights. The server 104 can feed back the phase recovery result of the sample to be measured to the terminal 102. In addition, in some embodiments, the multi-wavelength coherent diffraction imaging phase recovery algorithm can also be realized by the server 104 or the terminal 102 alone, for example, the terminal 102 can directly process the multi-wavelength diffraction patterns to obtain the phase recovery result of the sample to be measured, or the server 104 can obtain the multi-wavelength diffraction patterns from the data storage system and process the multi-wavelength diffraction patterns to obtain the phase recovery result of the sample to be measured.
[0026] The terminal 102 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 can be implemented by a single server or a server cluster composed of multiple servers, and can also be a cloud server.
[0027] In one example embodiment, as shown in Figure 2 , a multi-wavelength coherent diffraction imaging phase recovery algorithm is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or both. In the embodiments of the present application, the algorithm is applied to the server 104 in Figure 1 , which includes the following steps 201 and 202. Wherein:
[0028] Step 201, irradiating the sample to be measured with multiple incident lights of different wavelengths to obtain a diffraction pattern corresponding to each incident light.
[0029] Step 202, obtaining a phase recovery result of the sample to be measured based on the alternating direction method and the total variation algorithm according to the diffraction patterns corresponding to all incident lights.
[0030] In one example embodiment, as shown in Figure 3 , the above step 202 can be replaced by the following steps 301 to 304. Wherein:
[0031] Step 301, setting a maximum number of iterations K and initializing an input parameter to obtain an initial input parameter.
[0032] Step 302, obtaining an update formula corresponding to any diffraction pattern based on the alternating direction method and the total variation algorithm according to the diffraction pattern.
[0033] Step 303, for the kth iteration, obtaining an input parameter of the (k+1)th iteration according to the input parameter of the kth iteration and the update formula corresponding to each diffraction pattern. Wherein, k>0, and the input parameter of the first iteration is the initial input parameter.
[0034] Step 304, if k<K, performing the (k+1)th iteration, and if k=K, obtaining the phase recovery result of the sample to be measured according to the input parameter of the Kth iteration.
[0035] In one example embodiment, the above step 302 can be replaced by the following steps 3021 and 3022. Wherein:
[0036] At step 3021, for any diffraction pattern corresponding to an incident light, an amplitude projection operator and a support domain projection operator corresponding to the diffraction pattern are obtained based on an amplitude constraint and a support domain constraint.
[0037] At step 3022, according to the amplitude projection operator and the support domain projection operator corresponding to the diffraction pattern, an update formula corresponding to the diffraction pattern is obtained based on an alternating direction method and a total variation algorithm.
[0038] In an exemplary embodiment, for any diffraction pattern corresponding to an incident light, the amplitude projection operator corresponding to the diffraction pattern is:
[0039]
[0040] wherein P M (·) is the amplitude projection operator corresponding to the diffraction pattern, p is an operation input, D(·) is a diffraction propagation function, I is the diffraction pattern, exp is an exponential function with a natural constant as a base, j is an imaginary unit, and ∠ is a phase symbol.
[0041] The support domain projection operator corresponding to the diffraction pattern is:
[0042]
[0043] wherein P S (·) is the support domain projection operator corresponding to the diffraction pattern, and a is a boundary of a support domain.
[0044] The alternating direction method is used to realize diffraction pattern phase recovery, which is usually expressed as finding a solution at the intersection of different constraints. First, the optimal solution coincides with the obtained diffraction pattern I, and thus the amplitude constraint can be obtained. In an exemplary embodiment, the amplitude constraint can be expressed as:
[0045] M={p|D(p) 2 =I}.
[0046] wherein M is the amplitude constraint.
[0047] Further, by defining a set of support binary masks, the support domain constraint can be obtained as shown in the following formula:
[0048] S={p|p≠0 for p∈a and p=0 for p∈a}.
[0049] wherein S is the support domain constraint.
[0050] The amplitude projection operator and the support domain projection operator can be obtained by alternately projecting the variable p onto M and S.
[0051] In an exemplary embodiment, the step 303 described above can be replaced by steps 3031 to 3033. Among them:
[0052] Step 3031, for the kth iteration, according to the input parameters of the kth iteration and the update formula corresponding to the first diffraction pattern, the update parameters corresponding to the first diffraction pattern are obtained.
[0053] Step 3032, according to the update parameters corresponding to the i-1th diffraction pattern and the update formula corresponding to the ith diffraction pattern, the update parameters corresponding to the ith diffraction pattern are obtained; n≥i≥2, n is the number of diffraction patterns.
[0054] Step 3033, the update parameters corresponding to the nth diffraction pattern are taken as the input parameters of the k+1th iteration.
[0055] In an exemplary embodiment, the update parameters include first update parameters, second update parameters and third update parameters.
[0056] For the kth iteration, the update parameters corresponding to the ith diffraction pattern are:
[0057]
[0058] Among them, is the first update parameter corresponding to the ith diffraction pattern in the kth iteration, is the second update parameter corresponding to the ith diffraction pattern in the kth iteration, is the third update parameter corresponding to the ith diffraction pattern in the kth iteration, is the second update parameter corresponding to the i-1th diffraction pattern in the kth iteration, is the third update parameter corresponding to the i-1th diffraction pattern in the kth iteration, TV (·) is the denoising operator of the total variation algorithm, P S,i (·) is the support domain projection operator corresponding to the ith diffraction pattern, P M,i (·) is the amplitude projection operator corresponding to the ith diffraction pattern, β is the iteration coefficient; W(·) is the wavelength mapping operator, z∈{y, ω}, λ i is the wavelength of the incident light corresponding to the ith diffraction pattern, λ i-1 is the wavelength of the incident light corresponding to the i-1th diffraction pattern, exp is the exponential function with natural constant as base, j is the imaginary unit, ∠ is the phase symbol.
[0059] Only amplitude constraint and support constraint are sometimes insufficient to complete phase retrieval, and more prior information is needed. The present application uses a total variation algorithm for denoising, which realizes the optimization of the result. The optimization problem solved by the total variation denoising algorithm is as follows:
[0060]
[0061] Wherein, μ is the denoised image, η is the regularization coefficient, Δ is the gradient operator, ||·||1 is the L1 norm, and ||·||2 is the L2 norm.
[0062] In the present embodiment, FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) is used to solve the optimization problem of the total variation denoising algorithm.
[0063] In an exemplary embodiment, as shown in Figure 4 Before phase retrieval, the input parameters are first initialized to obtain the initial input parameters and For the kth iteration, first, the update parameters corresponding to the first diffraction pattern are obtained according to the input parameters of the kth iteration and the update formula corresponding to the first diffraction pattern. Then, the update parameters corresponding to the second diffraction pattern are obtained according to the update parameters corresponding to the first diffraction pattern and the update formula corresponding to the second diffraction pattern, and so on. The update parameters corresponding to the last diffraction pattern and are taken as the input parameters of the (k+1)th iteration.
[0064] Based on the same inventive concept, the present application also provides a multi-wavelength coherent diffraction imaging phase retrieval system for implementing the multi-wavelength coherent diffraction imaging phase retrieval algorithm described above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more multi-wavelength coherent diffraction imaging phase retrieval system embodiments provided below can refer to the limitations of the multi-wavelength coherent diffraction imaging phase retrieval algorithm described above, and will not be repeated here.
[0065] In an exemplary embodiment, as shown in Figure 5 A multi-wavelength coherent diffraction imaging phase retrieval system is provided, which includes an image acquisition module 501 and a phase retrieval module 502. The image acquisition module 501 is configured to irradiate a sample to be measured with a plurality of different wavelengths of incident light to obtain a diffraction pattern corresponding to each incident light. The phase retrieval module 502 is configured to obtain a phase retrieval result of the sample to be measured based on the alternating direction method and the total variation algorithm according to the diffraction patterns corresponding to all incident lights.
[0066] In an exemplary embodiment, the working principle of the image acquisition module 501 is as follows: Figure 6 As shown. n incident lights of different wavelengths λ1, λ2, ..., λ n The sample to be tested 601 is illuminated, and a diffraction pattern corresponding to each incident light is obtained by a camera 602. In this embodiment, the sample to be tested 601 includes a resolution plate 6011 and a binary mask 6012.
[0067] In another exemplary embodiment, the image acquisition module 501 may be as follows: Figure 7 The experimental device shown in the figure includes n excitation wavelengths, namely λ1, λ2, ..., λ n The laser 701 is used to sequentially illuminate the sample to be tested to obtain diffraction patterns corresponding to different incident lights. Each incident light passes through the reflector M and the attenuation plate D, and is incident on the beam splitter BS. After the optical path is adjusted by the beam splitter BS, it passes through the microscope objective L1, the pinhole S and the lens L2 in sequence, and is illuminated onto the sample to be tested 601. The corresponding diffraction pattern is obtained by the camera 602. In this embodiment, the camera 602 is a BASLER CCD (model a2A4504-18umPRO) with a pixel size of 2.74um. In the phase retrieval algorithm, the actual array size used is 1200×1200 pixels. The aperture of the metal sheet and the sample are tightly together.
[0068] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store diffraction patterns corresponding to incident light of different wavelengths. The input and output interfaces of the computer device are used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-wavelength coherent diffraction imaging phase recovery algorithm is implemented.
[0069] Those skilled in the art will understand that Figure 8The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0070] In an exemplary embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.
[0071] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implements the steps in the above method embodiments when executed by a processor.
[0072] In an exemplary embodiment, a computer program product is provided, including a computer program, and the computer program implements the steps in the above method embodiments when executed by a processor.
[0073] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.
[0074] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0075] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.
[0076] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.
[0077] The principles and implementation manners of the present application are described herein by using specific examples, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will have changes. In conclusion, the content of the specification should not be understood as a limitation of the present application.
Claims
1. A multi-wavelength coherent diffraction imaging phase retrieval algorithm, characterized in that: The multi-wavelength coherent diffraction imaging phase retrieval algorithm includes: Irradiating a sample to be measured with incident lights of multiple different wavelengths to obtain a diffraction pattern corresponding to each incident light; Based on the diffraction patterns corresponding to all the incident lights, and using the alternating direction method and the total variation algorithm, obtaining the phase retrieval result of the sample to be measured.
2. The multi-wavelength coherent diffraction imaging phase retrieval algorithm according to claim 1, characterized in that: Based on the diffraction patterns corresponding to all the incident lights, and using the alternating direction method and the total variation algorithm, obtaining the phase retrieval result of the sample to be measured, specifically including: Setting the maximum number of iterations K and initializing the input parameters to obtain the initial input parameters; Based on the diffraction pattern corresponding to any one of the incident lights, and using the alternating direction method and the total variation algorithm, obtaining the update formula corresponding to the diffraction pattern; For the k-th iteration, based on the input parameters of the k-th iteration and the update formula corresponding to each diffraction pattern, obtaining the input parameters of the (k + 1)-th iteration; k > 0; the input parameters of the first iteration are the initial input parameters; If k < K, then perform the (k + 1)-th iteration; if k = K, then based on the input parameters of the K-th iteration, obtaining the phase retrieval result of the sample to be measured.
3. The multi-wavelength coherent diffraction imaging phase retrieval algorithm according to claim 2, characterized in that: Based on the diffraction pattern corresponding to any one of the incident lights, and using the alternating direction method and the total variation algorithm, obtaining the update formula corresponding to the diffraction pattern, specifically including: For the diffraction pattern corresponding to any one of the incident lights, based on the amplitude constraint and the support domain constraint, obtaining the amplitude projection operator and the support domain projection operator corresponding to the diffraction pattern; Based on the amplitude projection operator and the support domain projection operator corresponding to the diffraction pattern, and using the alternating direction method and the total variation algorithm, obtaining the update formula corresponding to the diffraction pattern.
4. The multi-wavelength coherent diffraction imaging phase retrieval algorithm according to claim 3, characterized in that: For the diffraction pattern corresponding to any one of the incident lights, the amplitude projection operator corresponding to the diffraction pattern is: Among them, P M (·) is the amplitude projection operator corresponding to the diffraction pattern, p is the operation input, D(·) is the diffraction propagation function, I is the diffraction pattern, exp is the exponential function with the natural constant as the base, j is the imaginary unit, and ∠ is the phase sign; The support domain projection operator corresponding to the diffraction pattern is: Among them, P S (·) is the support domain projection operator corresponding to the diffraction pattern, and a is the boundary of the support domain.
5. The multi-wavelength coherent diffraction imaging phase retrieval algorithm according to claim 2, characterized in that: For the k-th iteration, based on the input parameters of the k-th iteration and the update formula corresponding to each diffraction pattern, obtaining the input parameters of the (k + 1)-th iteration, specifically including: For the k-th iteration, based on the input parameters of the k-th iteration and the update formula corresponding to the first diffraction pattern, obtaining the updated parameters corresponding to the first diffraction pattern; Based on the updated parameters corresponding to the (i - 1)-th diffraction pattern and the update formula corresponding to the i-th diffraction pattern, obtaining the updated parameters corresponding to the i-th diffraction pattern; n ≥ i ≥ 2, n is the number of diffraction patterns; Taking the updated parameters corresponding to the n-th diffraction pattern as the input parameters of the (k + 1)-th iteration.
6. The multi-wavelength coherent diffraction imaging phase retrieval algorithm according to claim 5, characterized in that: The updated parameters include the first updated parameter, the second updated parameter, and the third updated parameter; For the k-th iteration, the updated parameters corresponding to the i-th diffraction pattern are: in, is the first updated parameter corresponding to the i-th diffraction pattern in the k-th iteration, is the second updated parameter corresponding to the i-th diffraction pattern in the k-th iteration, is the third updated parameter corresponding to the i-th diffraction pattern in the k-th iteration, is the second updated parameter corresponding to the i-1th diffraction pattern in the kth iteration, is the third updated parameter corresponding to the i-1th diffraction pattern in the kth iteration, P TV (·) is the denoising operator of the total variation algorithm, P S,i (·) is the support domain projection operator corresponding to the i-th diffraction pattern, P M,i (·) is the amplitude projection operator corresponding to the i-th diffraction pattern, β is the iteration coefficient; W(·) is the wavelength mapping operator, z∈{y,ω},λ i is the wavelength of the incident light corresponding to the i-th diffraction pattern, λ i-1 is the wavelength of the incident light corresponding to the i-1th diffraction pattern, exp is an exponential function with a natural constant as the base, j is an imaginary unit, and ∠ is the phase sign.
7. A multi-wavelength coherent diffraction imaging phase retrieval system, characterized in that: The multi-wavelength coherent diffraction imaging phase retrieval system includes: An image acquisition module, configured to irradiate a sample to be measured with incident lights of multiple different wavelengths to obtain a diffraction pattern corresponding to each incident light; A phase retrieval module, configured to based on the diffraction patterns corresponding to all the incident lights, and using the alternating direction method and the total variation algorithm, obtain the phase retrieval result of the sample to be measured.