Structured light generation method, device and equipment based on phase recovery optimization

Abstract

This application discloses a method, apparatus, and device for generating structured light based on phase retrieval optimization, relating to the field of optoelectronic technology. The method includes: performing phase retrieval on an initial random phase based on prior information of the target intensity distribution to obtain an optimized phase distribution; constructing a metasurface silicon-based nanopillar array based on the optimized phase distribution; and performing phase modulation on the input light based on the metasurface silicon-based nanopillar array to obtain the target structured light. Through the above approach, a phase retrieval optimization algorithm is designed to effectively utilize prior information of the target intensity distribution to optimize the phase retrieval process, improving the accuracy and computational efficiency of phase retrieval. Furthermore, the metasurface silicon-based nanopillar array is used to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, significantly improving the quality and performance of the structured light.

Description

Technical Field

[0001] This application relates to the field of optoelectronic technology, and in particular to a method, apparatus and device for generating structured light based on phase retrieval optimization. Background Technology

[0002] Structured light technology is widely used in 3D scanning, optical imaging, sensors, optical computing, and other fields. In the process of generating structured light, a light source of some form (such as a plane wave or laser) is typically modulated using optical devices (such as phase controllers or optical diffractometers) to form a light field with a specific spatial distribution. In these optical modulators, diffraction efficiency, light field uniformity, and precise control of the phase distribution are key factors.

[0003] Currently, the Iterative Fourier Transform Algorithm (IFTA) is commonly used for phase retrieval in the process of generating structured light. The IFTA algorithm starts from the initial intensity distribution and phase information and performs iterative calculations to gradually approximate the target phase distribution. However, the traditional IFTA algorithm has problems such as slow convergence speed, poor recovery quality and low diffraction efficiency. The phase retrieval accuracy is insufficient, which affects the quality and performance of the generated structured light.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a structured light generation method, apparatus, and device based on phase retrieval optimization, aiming to solve the technical problem that insufficient phase retrieval accuracy in the prior art affects the quality and performance of the generated structured light.

[0006] To achieve the above objectives, this application provides a structured light generation method based on phase retrieval optimization, the method comprising:

[0007] Based on prior information about the target intensity distribution, phase recovery is performed on the initial random phase to obtain an optimized phase distribution;

[0008] Based on the optimized phase distribution, a metasurface silicon-based nanopillar array was constructed.

[0009] Based on the aforementioned metasurface silicon-based nanopillar array, the input light is phase-modulated to obtain the target structured light.

[0010] In one embodiment, the prior information is symmetric prior information, and the step of performing phase recovery on the initial random phase based on the prior information of the target intensity distribution to obtain an optimized phase distribution includes:

[0011] Based on the symmetry prior information of the target intensity distribution, a diagonal symmetry constraint is added to the initial random phase to obtain the initial optimized phase;

[0012] Based on the initial optimized phase, calculate the complex amplitude of the input diffraction plane;

[0013] The diffraction phase is determined based on the complex amplitude of the input diffraction plane;

[0014] Based on the prior information of the symmetry of the target intensity distribution, a diagonal symmetry constraint is added to the diffraction phase to obtain the optimized diffraction phase;

[0015] When the iteration termination condition is met, the diffraction optimization phase is taken as the target optimization phase.

[0016] In one embodiment, the step of calculating the complex amplitude of the focal plane based on the initial optimized phase includes:

[0017] Based on the initial optimized phase, the initial complex amplitude of the output light field in the focal plane is determined;

[0018] Based on the target amplitude distribution, the initial complex amplitude is adjusted to obtain a new initial complex amplitude;

[0019] Based on the new initial complex amplitude, the complex amplitude of the input diffraction plane is determined.

[0020] In one embodiment, the step of adding a diagonal symmetry constraint to the initial random phase based on the symmetry prior information of the target intensity distribution to obtain the initial optimized phase includes:

[0021] Based on the target triangle range of the initial random phase, determine the triangle phase corresponding to the initial random phase, wherein the target triangle range is either the upper triangle range or the lower triangle range.

[0022] Based on the transpose matrix of the initial random phase, determine the transpose phase corresponding to the initial random phase;

[0023] The sum of the triangular phase and the transpose phase of the initial random phase is used as the initial optimized phase.

[0024] In one embodiment, the prior information is sparse prior information, and the step of performing phase recovery on the initial random phase based on the prior information of the target intensity distribution to obtain an optimized phase distribution includes:

[0025] Based on the initial random phase, the initial phase image is determined;

[0026] Based on the measured intensity information, intensity constraints are applied to the frequency domain data of the initial phase image to obtain the phase image;

[0027] Based on the sparse prior information of the target intensity distribution, the phase image is sparsely regularized to obtain an updated phase image;

[0028] Calculate the image difference between the updated phase image and the initial phase image;

[0029] When the image differences satisfy the convergence condition, the phase distribution corresponding to the updated phase image is taken as the optimized phase distribution.

[0030] In one embodiment, the step of applying intensity constraints to the frequency domain data of the initial phase image based on measured intensity information to obtain the phase image includes:

[0031] The initial phase image is subjected to Fourier transform to obtain the frequency domain data;

[0032] Based on the measured intensity information, the amplitude of the frequency domain data is adjusted to obtain updated frequency domain data;

[0033] The phase image is determined based on the updated frequency domain data.

[0034] In one embodiment, the step of performing sparse regularization on the phase image based on the sparse prior information of the target intensity distribution to obtain an updated phase image includes:

[0035] Based on the prior information about the sparsity of the target intensity distribution, the target sparse transformation operation is determined;

[0036] Based on the target sparse transformation operation, the phase image is sparsely transformed to obtain a sparsely transformed phase image.

[0037] Based on the preset norm and the sparsity constraints corresponding to the target sparse transformation operation, the sparse transformation phase image is regularized to obtain the updated phase image.

[0038] In one embodiment, the step of constructing a metasurface silicon-based nanopillar array based on the optimized phase distribution includes:

[0039] Based on the optimized phase distribution, the rotation angle of each metasurface structural unit in the metasurface silicon-based nanopillar array is determined;

[0040] The metasurface silicon-based nanopillar array is constructed based on preset geometric parameters and the rotation angle of each metasurface structural unit.

[0041] Furthermore, to achieve the above objectives, this application also proposes a structured light generation device based on phase retrieval optimization, which includes:

[0042] The phase recovery optimization module is used to recover the initial random phase based on prior information about the target intensity distribution, and obtain an optimized phase distribution.

[0043] A metasurface device construction module is used to construct a metasurface silicon-based nanopillar array based on the optimized phase distribution;

[0044] The structured light generation module is used to perform phase modulation on the input light based on the metasurface silicon-based nanopillar array to obtain the target structured light.

[0045] Furthermore, to achieve the above objectives, this application also proposes a structured light generation device based on phase retrieval optimization. The structured light generation device based on phase retrieval optimization includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the structured light generation method based on phase retrieval optimization as described above.

[0046] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, it implements the steps of the structured light generation method based on phase retrieval optimization as described above.

[0047] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the structured light generation method based on phase retrieval optimization as described above.

[0048] This application provides a structured light generation method based on phase retrieval optimization. Based on prior information about the target intensity distribution, phase retrieval is performed on the initial random phase to obtain an optimized phase distribution. Based on the optimized phase distribution, a metasurface silicon-based nanopillar array is constructed. Based on the metasurface silicon-based nanopillar array, the input light is phase-modulated to obtain the target structured light. This application designs a phase retrieval optimization algorithm that effectively utilizes prior information about the target intensity distribution to optimize the phase retrieval process, improving the accuracy and computational efficiency of phase retrieval. Furthermore, it utilizes a metasurface silicon-based nanopillar array to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency. This significantly improves the quality and performance of the structured light, solving the technical problem of insufficient phase retrieval accuracy affecting the quality and performance of the generated structured light. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic flowchart of a first embodiment of the structured light generation method based on phase retrieval optimization in this application;

[0052] Figure 2 This is a schematic diagram of a metasurface silicon-based nanopillar array based on a phase retrieval optimized structured light generation method provided in Embodiment 1 of this application;

[0053] Figure 3 This is a schematic diagram of a metasurface structure unit for a structured light generation method based on phase retrieval optimization provided in Embodiment 1 of this application;

[0054] Figure 4 This is a schematic flowchart of Embodiment 2 of the structured light generation method based on phase retrieval optimization of this application;

[0055] Figure 5 This is a schematic flowchart of Embodiment 3 of the structured light generation method based on phase retrieval optimization of this application;

[0056] Figure 6 This is a schematic diagram of the module structure of the structured light generation device based on phase retrieval optimization according to an embodiment of this application;

[0057] Figure 7 This is a schematic diagram of the hardware operating environment involved in the structured light generation method based on phase retrieval optimization in the embodiments of this application.

[0058] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0059] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0060] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0061] The main solution of this application embodiment is: based on the prior information of the target intensity distribution, the initial random phase is phase recovered to obtain an optimized phase distribution; based on the optimized phase distribution, a metasurface silicon-based nanopillar array is constructed; based on the metasurface silicon-based nanopillar array, the input light is phase-modulated to obtain the target structured light.

[0062] Currently, the IFTA algorithm is commonly used for phase retrieval in the process of generating structured light. The IFTA algorithm starts with the initial intensity distribution and phase information and performs iterative calculations to gradually approximate the target phase distribution. However, the traditional IFTA algorithm has problems such as slow convergence speed, poor recovery quality and low diffraction efficiency. The phase retrieval accuracy is insufficient, which affects the quality and performance of the generated structured light.

[0063] This application provides a solution by designing a phase retrieval optimization algorithm that effectively utilizes prior information about the target intensity distribution to optimize the phase retrieval process, thereby improving the accuracy and computational efficiency of phase retrieval. Furthermore, it utilizes a metasurface silicon-based nanopillar array to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency. This significantly improves the quality and performance of structured light and solves the technical problem of insufficient phase retrieval accuracy affecting the quality and performance of the generated structured light.

[0064] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a structured light generation device based on phase retrieval optimization, etc. This embodiment does not specifically limit it. The following uses a structured light generation device based on phase retrieval optimization as an example to describe this embodiment and the following embodiments.

[0065] This application provides a structured light generation method based on phase retrieval optimization, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the structured light generation method based on phase retrieval optimization in this application.

[0066] In this embodiment, the structured light generation method based on phase retrieval optimization includes steps S10~S30:

[0067] Step S10: Based on the prior information of the target intensity distribution, perform phase recovery on the initial random phase to obtain the optimized phase distribution;

[0068] It should be noted that the target intensity distribution refers to the desired intensity distribution in the focal plane. The IFTA algorithm is commonly used in generating structured light. Based on the principle of iterative amplitude and phase, the IFTA algorithm can forcibly constrain the amplitude and phase in two Fourier domains until the target result is achieved. Generally, a series of calculations are required to obtain the target intensity distribution in the focal plane. The complex amplitude formula for the input light field of a pure phase diffractive optical element (DOE) is shown below:

[0069]

[0070] In the formula, This represents the complex amplitude of the input light field in the focal plane. This represents the complex amplitude of the incident light. The phase distribution of the DOE is represented by its transfer function. Similarly, the formula for the complex amplitude of the output light field in the focal plane is as follows:

[0071]

[0072] In the formula, This represents the complex amplitude of the output light field in the focal plane. This represents the complex amplitude of the input light field in the focal plane. and These represent the target amplitude distribution (desired amplitude distribution) and the target phase distribution (desired phase distribution) at the focal plane, respectively. For wave vector, The formula for calculating the target intensity distribution in the focal plane is as follows:

[0073]

[0074] In the formula, This represents the target intensity distribution at the focal plane. This represents the complex amplitude of the output light field in the focal plane. This represents the target amplitude distribution at the focal plane. Therefore, the DOE phase can be designed by solving the above equations.

[0075] Furthermore, it should be noted that in solving the integral equation using IFTA, the initial phase needs to be used as input, and the phase value is iteratively updated according to the target amplitude distribution. Therefore, the selection of the initial phase greatly affects the convergence speed and accuracy of the algorithm. Thus, this embodiment improves and optimizes the IFTA algorithm by designing a Phase Retrieval Optimization Algorithm (MIFTA) to optimize the determination of the initial phase and the iterative update of the phase. The MIFTA algorithm utilizes prior information about the target distribution of the structured light array (such as sparsity and symmetry), which not only accelerates the convergence speed of the algorithm but also improves the accuracy of phase retrieval compared to the traditional IFTA algorithm, thus increasing precision.

[0076] It is understood that, in this embodiment, the prior information of the target intensity distribution can be the symmetry of the target intensity distribution, i.e., symmetric prior information, or the sparsity of the target intensity distribution, i.e., sparse prior information, and no specific limitation is made thereto.

[0077] It should be understood that the initial random phase is the randomly determined input phase, and the optimized phase distribution is the phase distribution obtained after phase retrieval optimization. When using symmetric prior information of the target intensity distribution to optimize phase retrieval, symmetric constraints are typically applied to the initial random phase and the updated phase to improve the accuracy of phase retrieval. When using sparse prior information of the target intensity distribution to optimize phase retrieval, different sparsity constraints are typically introduced based on different target distributions to ensure that the final phase distribution satisfies the sparse prior information, adapts to different situations, and improves the accuracy of phase retrieval.

[0078] Traditional IFTA algorithms recover phase through multiple iterations in the frequency and spatial domains, resulting in relatively slow convergence, especially in high-dimensional spaces where computational costs are significant. In this embodiment, the MIFTA algorithm can more effectively utilize prior information (such as symmetry or sparsity) to optimize the phase recovery process, thereby improving the algorithm's convergence speed and recovery accuracy.

[0079] Step S20: Based on the optimized phase distribution, construct a metasurface silicon-based nanopillar array;

[0080] It should be noted that the reference Figure 2 Metasurface silicon-based nanopillar arrays are metasurface devices composed of multiple metasurface structural units distributed in an array. (Reference) Figure 3 The metasurface structure unit consists of a single-layer amorphous silicon cuboid nanorod placed on a SiO2 substrate, with the nanorod rotated at an angle of _____. The substrate has a length of L, a width of W, a height of H, and a lattice constant of P.

[0081] In one feasible implementation, step S20 may include steps S201-S202:

[0082] Step S201: Based on the optimized phase distribution, determine the rotation angle of each metasurface structural unit in the metasurface silicon-based nanopillar array;

[0083] It is understandable that, based on the fundamental principles of geometric phase, phase modulation can be achieved using nanorods with the same length, width, and height but different rotation angles. Therefore, in this embodiment, the length, width, and height of the metasurface structural unit (nanoring) are the same, and the rotation angle is set according to the optimized phase distribution obtained in step S10. The rotation angle of the nanorod on the incident plane is within the range of 0-180°. This corresponds to the geometric phase within the 0-360° range transmitted on the output plane. .

[0084] Step S202: Based on preset geometric parameters and the rotation angle of each metasurface structural unit, construct the metasurface silicon-based nanopillar array.

[0085] It should be noted that the preset geometric parameters are the relevant parameters that are set in advance, including at least the lattice constant of the substrate and the length, width and height of the metasurface structural unit (nanoring).

[0086] In practical implementation, the lattice constant P can be set to 250nm. By optimizing the cross-polarization parameters, a set of length L, width W and height H with high polarization conversion efficiency can be selected when the incident light wavelength is 630nm. For example: H=300nm, L=135nm, W=85nm.

[0087] Understandably, once the lattice constant of the substrate and the length, width, height, and rotation angle of the metasurface structural unit (nanoran) are determined, the fabrication of the metasurface silicon-based nanorod array can be completed using standard electron beam lithography and etching processes.

[0088] Step S30: Based on the metasurface silicon-based nanopillar array, the input light is phase-modulated to obtain the target structured light.

[0089] It should be noted that the input light is the light that is fed into the system, and the target structured light is the final generated structured light. In this embodiment, the target structured light is generated through a metasurface silicon-based nanopillar array, resulting in a uniform far-field light spot with high diffraction efficiency.

[0090] Furthermore, using Finite Difference Time Domain (FDTD) software, the depth of focus (DOF) of zero-order Bessel beam arrays generated based on the traditional IFTA algorithm and the MIFTA algorithm were calculated respectively. The theoretical formula for calculating the DOF of a zero-order Bessel beam is:

[0091]

[0092] In the formula, This represents the theoretical value of the depth of focus. Let represent the diameter of the metasurface. It is deduced that by increasing the size of the metasurface and decreasing NA (numerical aperture), the non-diffraction propagation distance can be further increased. For NA = 0.2, The theoretical value is 117.58 μm, obtained experimentally from the traditional IFTA algorithm and the MIFTA algorithm. The measured values ​​are 115 μm and 117 μm, respectively, which are in good agreement with the theoretical values. Furthermore, the non-uniformity of the traditional IFTA algorithm and the MIFTA algorithm can be calculated to be 0.1479 and 0.0999, respectively. The MIFTA algorithm exhibits better background noise suppression and center zero-order intensity suppression capabilities, and also has higher uniformity, thus achieving superior image quality.

[0093] Understandably, the phase distribution recovered using the MIFTA algorithm results in structured light with higher uniformity and accuracy. Compared to the traditional IFTA algorithm, the phase retrieval process in this embodiment achieves a more uniform light spot, thereby improving the quality of the generated structured light. Furthermore, utilizing the precise phase modulation characteristics of the metasurface silicon-based nanopillar array enables the generated structured light to exhibit higher diffraction efficiency. The metasurface structure can achieve high diffraction efficiency structured light output with lower loss. In addition, this embodiment combines phase retrieval with optical device design, avoiding multiple iterative calculations and complex adjustment steps of optical components, thus improving overall computational efficiency.

[0094] This embodiment provides a structured light generation method based on phase retrieval optimization. Based on prior information about the target intensity distribution, phase retrieval is performed on the initial random phase to obtain an optimized phase distribution. Based on the optimized phase distribution, a metasurface silicon-based nanopillar array is constructed. Based on the metasurface silicon-based nanopillar array, the input light is phase-modulated to obtain the target structured light. A phase retrieval optimization algorithm is designed to effectively utilize prior information about the target intensity distribution to optimize the phase retrieval process, improving the accuracy and computational efficiency of phase retrieval. Furthermore, the metasurface silicon-based nanopillar array is used to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, significantly improving the quality and performance of the structured light.

[0095] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 Step S10 may include steps S101 to S105:

[0096] Step S101: Based on the prior information of the symmetry of the target intensity distribution, add a diagonal symmetry constraint to the initial random phase to obtain the initial optimized phase;

[0097] It should be noted that the initial optimized phase is the data obtained after adjusting the initial random phase using symmetry prior information. The random selection range is 0 to... The initial random phase is taken as the input phase. The symmetry prior information of the target intensity distribution is used to apply a diagonal symmetry constraint to the initial random phase to obtain the initial optimized phase.

[0098] In one feasible implementation, step S101 may include: determining the triangular phase corresponding to the initial random phase based on the target triangular range of the initial random phase, wherein the target triangular range is either the upper triangular range or the lower triangular range; determining the transposed phase corresponding to the initial random phase based on the transpose matrix of the initial random phase; and using the sum of the triangular phase and the transposed phase of the initial random phase as the initial optimized phase.

[0099] It should be noted that the target triangle range, or the specified triangular portion, can be either the upper or lower triangle range. The upper triangle range refers to the upper triangular portion, and the lower triangle range refers to the lower triangular portion. In other words, the target triangle range of the initial random phase is the upper / lower triangular portion of the initial random phase. The corresponding data obtained according to the target triangle range of the initial random phase is the triangular phase of the initial random phase, and the corresponding data obtained according to the transpose matrix of the initial random phase is the transpose phase of the initial random phase.

[0100] It is understandable that the initial optimized phase consists of two parts: the triangular phase of the initial random phase and the transpose phase of the initial random phase. In other words, the initial optimized phase is the sum of the triangular phase and the transpose phase of the initial random phase, as shown below:

[0101]

[0102] In the formula, Indicates the initial optimized phase. The triangular phase represents the initial random phase. This represents the transpose of the initial random phase.

[0103] Step S102: Calculate the complex amplitude of the input diffraction plane based on the initial optimized phase;

[0104] In one feasible implementation, step S102 may include: determining the initial complex amplitude of the output light field in the focal plane based on the initial optimized phase; adjusting the initial complex amplitude based on the target amplitude distribution to obtain a new initial complex amplitude; and determining the complex amplitude of the input diffraction plane based on the new initial complex amplitude.

[0105] It should be noted that the initial complex amplitude is the complex amplitude obtained from preliminary calculation. The initial complex amplitude of the focal plane is calculated using the Fast Fourier Transform (FFT), and the relationship between the initial optimized phase and the initial complex amplitude is shown below:

[0106]

[0107]

[0108] In the formula, This represents the initial complex amplitude of the output light field in the focal plane. This represents the complex amplitude of the input light field in the focal plane. and These represent the target amplitude distribution and target phase distribution at the focal plane, respectively. For wave vector, , This represents the complex amplitude of the incident light. This represents the initial optimized phase.

[0109] It is understandable that the target amplitude distribution is used. Replace the calculated initial complex amplitude And reset the initial complex amplitude. Thus, a new initial complex amplitude is obtained. The diffraction plane is the DOE plane. The complex amplitude of the input DOE plane is calculated using the inverse fast Fourier transform (IFFT). The relationship between the new initial complex amplitude and the complex amplitude of the input DOE plane is shown below:

[0110]

[0111] In the formula, This represents the complex amplitude of the input diffraction plane. This represents the new initial complex amplitude.

[0112] Step S103: Determine the diffraction phase based on the complex amplitude of the input diffraction plane;

[0113] It should be noted that the diffraction phase is the DOE phase. The DOE phase is calculated based on the complex amplitude of the input DOE plane, and the calculation relationship is shown below:

[0114]

[0115] In the formula, Indicates the diffraction phase. This represents the complex amplitude of the input diffraction plane.

[0116] Step S104: Based on the prior information of the symmetry of the target intensity distribution, add a diagonal symmetry constraint to the diffraction phase to obtain the optimized diffraction phase;

[0117] In one feasible implementation, step S104 may include: determining the triangular phase corresponding to the diffraction phase based on the target triangular range of the diffraction phase, wherein the target triangular range is either the upper triangular range or the lower triangular range; determining the transposed phase corresponding to the diffraction phase based on the transpose matrix of the diffraction phase; and using the sum of the triangular phase and the transposed phase of the diffraction phase as the optimized diffraction phase.

[0118] It should be noted that the optimized diffraction phase is the data obtained after adjusting the diffraction phase using prior information about symmetry. The target triangular range of the diffraction phase refers to the upper / lower triangular portion of the diffraction phase. The corresponding data obtained according to the target triangular range of the diffraction phase is the triangular phase of the diffraction phase, and the corresponding data obtained according to the transpose matrix of the diffraction phase is the transpose phase of the diffraction phase.

[0119] It is understandable that the diffraction-optimized phase consists of two parts: the triangular phase of the diffraction phase and the transpose phase of the diffraction phase. In other words, the diffraction-optimized phase is the sum of the triangular phase and the transpose phase of the diffraction phase, as shown below:

[0120]

[0121] In the formula, Indicates the optimized phase of diffraction. Triangular phase representing the diffraction phase, The transpose of the diffraction phase.

[0122] Step S105: When the iteration termination condition is met, the diffraction optimization phase is taken as the target optimization phase.

[0123] It should be noted that the iteration termination condition is the condition that must be met to terminate the iteration, such as the error between the actual amplitude distribution and the target amplitude distribution. Less than or equal to the set error threshold, or the number of iterations The number of occurrences must be greater than or equal to a set threshold. The error between the actual amplitude distribution and the target amplitude distribution can be calculated using the following formula:

[0124]

[0125] In the formula, Indicates error. This represents the actual amplitude distribution, i.e., the initial complex amplitude of the output light field in the focal plane. This indicates the target amplitude distribution.

[0126] It is understandable that if the iteration termination condition is met, the currently obtained diffraction optimization phase is taken as the target optimization phase, and step S20 is continued; if the iteration termination condition is not met, the initial phase is updated to the diffraction optimization phase, and step S102 is returned to be executed.

[0127] Furthermore, set a 4 Using a uniform point matrix as the target intensity distribution, phase retrieval was performed using both the IFTA and MIFTA algorithms, with 200 iterations per iteration. Utilizing the symmetry of the target intensity distribution as prior information, the MIFTA algorithm imposed diagonal symmetry constraints on both the initial random phase and the updated phase. The average intensity and non-uniformity after phase retrieval using the IFTA algorithm were 0.949 and 0.0569, respectively, while those using the MIFTA algorithm were 0.9922 and 0.0076, respectively. This demonstrates that MIFTA performs better in terms of accuracy compared to IFTA.

[0128] This embodiment provides a structured light generation method based on phase retrieval optimization. Based on prior information about the symmetry of the target intensity distribution, a diagonal symmetry constraint is added to the initial random phase to obtain an initial optimized phase. Based on the initial optimized phase, the complex amplitude of the input diffraction plane is calculated. Based on the complex amplitude of the input diffraction plane, the diffraction phase is determined. Based on the prior information about the symmetry of the target intensity distribution, a diagonal symmetry constraint is added to the diffraction phase to obtain a diffraction-optimized phase. When the iteration termination condition is met, the diffraction-optimized phase is used as the target optimized phase. A phase retrieval optimization algorithm is designed to effectively utilize prior information about the target intensity distribution to optimize the phase retrieval process, improving the accuracy and computational efficiency of phase retrieval. Furthermore, a metasurface silicon-based nanopillar array is used to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, significantly improving the quality and performance of structured light.

[0129] Based on the first embodiment of this application, in the third embodiment of this application, the same or similar content as the above embodiment can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 Step S10 may include steps S101'~S105':

[0130] Step S101': Determine the initial phase image based on the initial random phase;

[0131] It should be noted that the initial phase image is determined based on the initial random phase. Generally, the initial phase image is usually a zero matrix or a random phase image.

[0132] It is understandable that, assuming the complex phase information of the target object is controlled by certain sparse patterns (such as sparse array distribution), the actual measured intensity information (intensity image) is correlated with the phase information through Fourier transform.

[0133] Step S102': Based on the measured intensity information, intensity constraints are applied to the frequency domain data of the initial phase image to obtain the phase image;

[0134] In one feasible implementation, step S102' may include: performing a Fourier transform on the initial phase image to obtain the frequency domain data; adjusting the amplitude of the frequency domain data based on the measured intensity information to obtain updated frequency domain data; and determining the phase image based on the updated frequency domain data.

[0135] It should be noted that the measured intensity information refers to the actual measured intensity information, i.e., the intensity image. Frequency domain data refers to the values ​​obtained after performing a Fourier transform on the initial phase image. Updated frequency domain data refers to the updated frequency domain data. The phase image is the image obtained after applying intensity constraints to the initial phase image.

[0136] Understandably, performing a Fourier transform on the initial phase image yields its frequency domain representation. Since the actual measurement provides intensity information, the intensity constraint is... This involves forcibly adjusting the amplitude of the frequency domain data to match the measured intensity information, thereby obtaining updated frequency domain data, and then calculating the updated phase image, as shown below:

[0137]

[0138] In the formula, Represents a phase image. Represents the initial phase image. Indicates measured intensity information, This represents the inverse Fourier transform. This indicates that the frequency domain data is being updated.

[0139] Step S103': Based on the sparse prior information of the target intensity distribution, the phase image is sparsely regularized to obtain an updated phase image;

[0140] In one feasible implementation, step S103' may include: determining a target sparse transformation operation based on the sparsity prior information of the target intensity distribution; performing a sparse transformation on the phase image based on the target sparse transformation operation to obtain a sparse transformed phase image; and regularizing the sparse transformed phase image based on a preset norm and the sparsity constraint corresponding to the target sparse transformation operation to obtain the updated phase image.

[0141] It should be noted that this embodiment introduces sparsity constraints to ensure that the final solution satisfies the sparsity prior information. The target sparse transformation operation is the selected sparse transformation operation. Based on the sparsity prior information, the corresponding target sparse transformation operation is selected. Different sparsity prior information typically corresponds to different target sparse transformation operations, thus adapting to different scenarios. The sparse transformed phase image is the phase image after the sparse transformation. The updated phase image is the phase image after iterative updates.

[0142] It is understandable that, according to the target sparse transformation operation, the phase image... Perform a sparsity transformation to obtain the sparse transform phase image. The predefined norm is the L1 norm (the sum of the absolute values ​​of all elements in the vector). The L1 norm and sparsity constraints are used to regularize the sparse transform phase image, as shown below:

[0143]

[0144] In the formula, To update the phase image, For data fidelity, measure the error of the current estimate. As a regularization term, it promotes sparsity of solutions. The L0 norm (the number of non-zero elements in the vector) after the sparse transformation represents the sparsity of the solution and controls the sparsity mode. and These are regularization parameters, controlling the strength of the L1 norm and the sparsity constraint term, respectively. The measurement matrix is ​​typically a Fourier transform operation matrix. It is solved using gradient descent or variational methods to obtain... .

[0145] Step S104': Calculate the image difference between the updated phase image and the initial phase image;

[0146] Understandably, checking the update amount of the phase image involves calculating the updated phase image. and initial phase image Image differences .

[0147] Step S105': When the image difference satisfies the convergence condition, the phase distribution corresponding to the updated phase image is taken as the optimized phase distribution.

[0148] Understandably, the convergence condition is the condition that must be met for the iteration to terminate. For example, the image difference between the updated phase image and the initial phase image must be less than or equal to a set convergence threshold. If the image difference meets the convergence condition, the iteration stops, and the phase distribution corresponding to the updated phase image is used as the final optimized phase distribution. If the image difference does not meet the convergence condition, the initial phase image is updated to the updated phase image, and the iteration continues.

[0149] This embodiment provides a structured light generation method based on phase retrieval optimization. An initial phase image is determined based on an initial random phase. Intensity constraints are applied to the frequency domain data of the initial phase image based on measured intensity information to obtain another phase image. Based on prior information about the sparsity of the target intensity distribution, the phase image is sparsely regularized to obtain an updated phase image. The image difference between the updated and initial phase images is calculated. When the image difference satisfies the convergence condition, the phase distribution corresponding to the updated phase image is used as the optimized phase distribution. A phase retrieval optimization algorithm is designed to effectively utilize prior information about the target intensity distribution to optimize the phase retrieval process, improving the accuracy and computational efficiency of phase retrieval. Furthermore, a metasurface silicon-based nanopillar array is used to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, significantly improving the quality and performance of structured light.

[0150] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the structured light generation method based on phase retrieval optimization in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0151] This application also provides a structured light generation device based on phase retrieval optimization. Please refer to [link / reference]. Figure 6 The structured light generation device based on phase retrieval optimization includes:

[0152] The phase recovery optimization module 10 is used to recover the initial random phase based on the prior information of the target intensity distribution to obtain an optimized phase distribution;

[0153] Metasurface device construction module 20 is used to construct a metasurface silicon-based nanopillar array based on the optimized phase distribution;

[0154] The structured light generation module 30 is used to perform phase modulation on the input light based on the metasurface silicon-based nanopillar array to obtain the target structured light.

[0155] In one feasible implementation, the phase recovery optimization module 10 is further configured to add a diagonal symmetry constraint to the initial random phase based on the symmetry prior information of the target intensity distribution, so as to obtain an initial optimized phase;

[0156] Based on the initial optimized phase, calculate the complex amplitude of the input diffraction plane;

[0157] The diffraction phase is determined based on the complex amplitude of the input diffraction plane;

[0158] Based on the prior information of the symmetry of the target intensity distribution, a diagonal symmetry constraint is added to the diffraction phase to obtain the optimized diffraction phase;

[0159] When the iteration termination condition is met, the diffraction optimization phase is taken as the target optimization phase.

[0160] In one feasible implementation, the phase recovery optimization module 10 is further configured to determine the initial complex amplitude of the output light field in the focal plane based on the initial optimized phase.

[0161] Based on the target amplitude distribution, the initial complex amplitude is adjusted to obtain a new initial complex amplitude;

[0162] Based on the new initial complex amplitude, the complex amplitude of the input diffraction plane is determined.

[0163] In one feasible implementation, the phase recovery optimization module 10 is further configured to determine the triangular phase corresponding to the initial random phase based on the target triangular range of the initial random phase, wherein the target triangular range is either the upper triangular range or the lower triangular range.

[0164] Based on the transpose matrix of the initial random phase, determine the transpose phase corresponding to the initial random phase;

[0165] The sum of the triangular phase and the transpose phase of the initial random phase is used as the initial optimized phase.

[0166] In one feasible implementation, the phase recovery optimization module 10 is further configured to determine the initial phase image based on the initial random phase;

[0167] Based on the measured intensity information, intensity constraints are applied to the frequency domain data of the initial phase image to obtain the phase image;

[0168] Based on the sparse prior information of the target intensity distribution, the phase image is sparsely regularized to obtain an updated phase image;

[0169] Calculate the image difference between the updated phase image and the initial phase image;

[0170] When the image differences satisfy the convergence condition, the phase distribution corresponding to the updated phase image is taken as the optimized phase distribution.

[0171] In one feasible implementation, the phase recovery optimization module 10 is further configured to perform a Fourier transform on the initial phase image to obtain the frequency domain data;

[0172] Based on the measured intensity information, the amplitude of the frequency domain data is adjusted to obtain updated frequency domain data;

[0173] The phase image is determined based on the updated frequency domain data.

[0174] In one feasible implementation, the phase recovery optimization module 10 is further configured to determine the target sparse transformation operation based on the sparse prior information of the target intensity distribution;

[0175] Based on the target sparse transformation operation, the phase image is sparsely transformed to obtain a sparsely transformed phase image.

[0176] Based on the preset norm and the sparsity constraints corresponding to the target sparse transformation operation, the sparse transformation phase image is regularized to obtain the updated phase image.

[0177] In one feasible implementation, the metasurface device construction module 20 is further configured to determine the rotation angle of each metasurface structural unit in the metasurface silicon-based nanopillar array based on the optimized phase distribution.

[0178] The metasurface silicon-based nanopillar array is constructed based on preset geometric parameters and the rotation angle of each metasurface structural unit.

[0179] The structured light generation device based on phase retrieval optimization provided in this application, employing the structured light generation method based on phase retrieval optimization in the above embodiments, can solve the technical problem of insufficient phase retrieval accuracy affecting the quality and performance of the generated structured light. Compared with the prior art, the beneficial effects of the structured light generation device based on phase retrieval optimization provided in this application are the same as those of the structured light generation method based on phase retrieval optimization provided in the above embodiments, and other technical features in the structured light generation device based on phase retrieval optimization are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0180] This application provides a structured light generation device based on phase retrieval optimization. The structured light generation device based on phase retrieval optimization includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the structured light generation method based on phase retrieval optimization in the above embodiment 1.

[0181] The following is for reference. Figure 7This document illustrates a schematic diagram of a structured light generation device based on phase retrieval optimization suitable for implementing embodiments of this application. The structured light generation device based on phase retrieval optimization in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The structured light generation device based on phase retrieval optimization shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0182] like Figure 7 As shown, the phase retrieval-optimized structured light generation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the phase retrieval-optimized structured light generation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the phase-recovery-optimized structured light generation device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a phase-recovery-optimized structured light generation device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0183] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0184] The structured light generation device based on phase retrieval optimization provided in this application, employing the structured light generation method based on phase retrieval optimization in the above embodiments, can solve the technical problem of insufficient phase retrieval accuracy affecting the quality and performance of the generated structured light. Compared with the prior art, the beneficial effects of the structured light generation device based on phase retrieval optimization provided in this application are the same as those of the structured light generation method based on phase retrieval optimization provided in the above embodiments, and other technical features in this structured light generation device based on phase retrieval optimization are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0185] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0186] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0187] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the structured light generation method based on phase retrieval optimization in the above embodiments.

[0188] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0189] The aforementioned computer-readable storage medium may be included in a phase-recovery-optimized structured light generation device; or it may exist independently and not be assembled into a phase-recovery-optimized structured light generation device.

[0190] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a phase-recovery-optimized structured light generation device, cause the phase-recovery-optimized structured light generation device to: perform phase recovery on an initial random phase based on prior information about the target intensity distribution to obtain an optimized phase distribution; construct a metasurface silicon-based nanopillar array based on the optimized phase distribution; and perform phase modulation on the input light based on the metasurface silicon-based nanopillar array to obtain the target structured light.

[0191] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0192] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0193] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0194] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described structured light generation method based on phase retrieval optimization. This solves the technical problem of insufficient phase retrieval accuracy affecting the quality and performance of the generated structured light. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the structured light generation method based on phase retrieval optimization provided in the above embodiments, and will not be repeated here.

[0195] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the structured light generation method based on phase retrieval optimization as described above.

[0196] The computer program product provided in this application can solve the technical problem of insufficient phase retrieval accuracy, which affects the quality and performance of the generated structured light. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the phase retrieval-optimized structured light generation method provided in the above embodiments, and will not be repeated here.

[0197] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A structured light generation method based on phase recovery optimization, characterized in that, The method includes: Based on prior information about the target intensity distribution, phase recovery is performed on the initial random phase to obtain an optimized phase distribution. Specifically, this includes: when the prior information is symmetric, adding a diagonal symmetry constraint to the initial random phase based on the symmetric prior information of the target intensity distribution to obtain an initial optimized phase; determining the initial complex amplitude of the output light field in the focal plane based on the initial optimized phase; adjusting the initial complex amplitude based on the target amplitude distribution to obtain a new initial complex amplitude; determining the complex amplitude of the input diffraction plane based on the new initial complex amplitude; determining the diffraction phase based on the complex amplitude of the input diffraction plane; adding a diagonal symmetry constraint to the diffraction phase based on the symmetric prior information of the target intensity distribution to obtain a diffraction optimized phase; and using the diffraction optimized phase as the target optimized phase when the iteration termination condition is met. Based on the optimized phase distribution, a metasurface silicon-based nanopillar array was constructed. Based on the aforementioned metasurface silicon-based nanopillar array, the input light is phase-modulated to obtain the target structured light; The step of adding diagonal symmetry constraints to the initial random phase based on the symmetry prior information of the target intensity distribution to obtain the initial optimized phase includes: determining the triangular phase corresponding to the initial random phase based on the target triangular range of the initial random phase, wherein the target triangular range is either the upper triangular range or the lower triangular range; determining the transposed phase corresponding to the initial random phase based on the transpose matrix of the initial random phase; and taking the sum of the triangular phase and the transposed phase of the initial random phase as the initial optimized phase.

2. The method of claim 1, wherein, The step of constructing a metasurface silicon-based nanopillar array based on the optimized phase distribution includes: Based on the optimized phase distribution, the rotation angle of each metasurface structural unit in the metasurface silicon-based nanopillar array is determined; The metasurface silicon-based nanopillar array is constructed based on preset geometric parameters and the rotation angle of each metasurface structural unit.

3. A structured light generation device based on phase retrieval optimization, characterized in that, The device includes: The phase recovery optimization module is used to recover the initial random phase based on prior information about the target intensity distribution, and obtain an optimized phase distribution. A metasurface device construction module is used to construct a metasurface silicon-based nanopillar array based on the optimized phase distribution; The structured light generation module is used to perform phase modulation on the input light based on the metasurface silicon-based nanopillar array to obtain the target structured light; The phase recovery optimization module is further configured to add a diagonal symmetry constraint to the initial random phase based on the symmetry prior information of the target intensity distribution when the prior information is symmetric prior information, thereby obtaining an initial optimized phase; Based on the initial optimized phase, the initial complex amplitude of the output light field in the focal plane is determined. Based on the target amplitude distribution, the initial complex amplitude is adjusted to obtain a new initial complex amplitude. Based on the new initial complex amplitude, the complex amplitude of the input diffraction plane is determined. The diffraction phase is determined based on the complex amplitude of the input diffraction plane; Based on the prior information of the symmetry of the target intensity distribution, a diagonal symmetry constraint is added to the diffraction phase to obtain the optimized diffraction phase; When the iteration termination condition is met, the diffraction optimization phase is taken as the target optimization phase; The phase recovery optimization module is further configured to determine the triangular phase corresponding to the initial random phase based on the target triangular range of the initial random phase, wherein the target triangular range is either the upper triangular range or the lower triangular range. Based on the transpose matrix of the initial random phase, determine the transpose phase corresponding to the initial random phase; The sum of the triangular phase and the transpose phase of the initial random phase is used as the initial optimized phase.

4. A structured light generation device based on phase recovery optimization, characterized in that, The device is configured to implement the steps of the structured light generation method based on phase recovery optimization as described in any one of claims 1 to 2.

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