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

Abstract

The invention discloses a structured light generation method, device and equipment based on phase recovery optimization, and relates to the technical field of photoelectricity, and the method comprises the steps: carrying out the phase recovery of an initial random phase based on the prior information of target intensity distribution, and obtaining optimized phase distribution; on the basis of the optimized phase distribution, constructing a metasurface silicon-based nano-column array; and based on the metasurface silicon-based nanopillar array, performing phase modulation on input light to obtain target structured light. Through the above mode, the phase recovery optimization algorithm is designed, the prior information of target intensity distribution is effectively utilized to optimize the phase recovery process, the precision and calculation efficiency of phase recovery are improved, and the metasurface silicon-based nanopillar array is utilized to realize accurate phase modulation, so that structured light with high uniformity and high diffraction efficiency is generated. And the quality and the performance of the structured light can be obviously improved.

Description

Technical Field

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

[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, it is usually necessary to modulate a certain form of light source (such as plane wave, laser, etc.) through an optical device (such as a phase controller, optical diffraction plate, etc.) to form a light field with a specific spatial distribution. In these optical modulators, diffraction efficiency, light field uniformity and precise control of phase distribution are key factors.

[0003] At present, the iterative Fourier Transform Algorithm (IFTA) is usually used for phase recovery in the process of generating structured light. The IFTA algorithm starts with iterative calculation from the initial intensity distribution and phase information, and gradually approaches the target phase distribution. However, the traditional IFTA algorithm has problems such as slow convergence speed, poor recovery quality and low diffraction efficiency. The accuracy of phase recovery is insufficient, which affects the quality and performance of the generated structured light.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

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

[0006] To achieve the above objectives, the present application provides a structured light generation method based on phase recovery optimization, the method comprising: Based on the prior information of target intensity distribution, the initial random phase is phase recovered to obtain the optimized phase distribution; Based on the optimized phase distribution, constructing a supersurface silicon-based nanocolumn array; Based on the metasurface silicon-based nanocolumn array, the input light is phase modulated to obtain the target structured light.

[0007] In one embodiment, the prior information is symmetry 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 the optimized phase distribution includes: Based on the symmetry prior information of the target intensity distribution, adding a diagonal symmetry constraint to the initial random phase to obtain an initial optimized phase; Based on the initial optimized phase, calculating the complex amplitude of the input diffraction plane; determining a diffraction phase based on the complex amplitude of the input diffraction plane; Based on the symmetry prior information of the target intensity distribution, adding a diagonal symmetry constraint 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.

[0008] In one embodiment, the step of calculating the complex amplitude of the focal plane based on the initial optimized phase comprises: Based on the initial optimized phase, determining an initial complex amplitude of the output light field in the focal plane; 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.

[0009] 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: Determine a triangular phase corresponding to the initial random phase based on a target triangular range of the initial random phase, wherein the target triangular range is any one of an upper triangular range and a lower triangular range; Based on the transposed matrix of the initial random phase, determining the transposed phase corresponding to the initial random phase; The sum of the triangular phase and the transposed phase of the initial random phase is used as the initial optimized phase.

[0010] 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 the optimized phase distribution includes: Based on the initial random phase, determining the initial phase image; Based on the measured intensity information, intensity constraints are performed on the frequency domain data of the initial phase image to obtain a phase image; Based on the sparsity prior information of the target intensity distribution, the phase image is sparsely regularized to obtain an updated phase image; calculating an image difference between the updated phase image and the initial phase image; When the image difference satisfies a convergence condition, the phase distribution corresponding to the updated phase image is used as the optimized phase distribution.

[0011] In one embodiment, the step of performing intensity constraints on the frequency domain data of the initial phase image based on the measured intensity information to obtain the phase image includes: Performing Fourier transform on the initial phase image to obtain the frequency domain data; Based on the measurement strength information, adjusting the amplitude of the frequency domain data to obtain updated frequency domain data; Based on the updated frequency domain data, the phase image is determined.

[0012] In one embodiment, the step of performing sparse regularization on the phase image based on the sparsity prior information of the target intensity distribution to obtain an updated phase image comprises: Determining a target sparse transformation operation based on the sparsity prior information of the target intensity distribution; Based on the target sparse transform operation, performing a sparse transform on the phase image to obtain a sparse transformed phase image; Based on a preset norm and a sparsity constraint corresponding to the target sparse transform operation, the sparse transform phase image is regularized to obtain the updated phase image.

[0013] In one embodiment, the step of constructing a supersurface silicon-based nanorod array based on the optimized phase distribution includes: Based on the optimized phase distribution, determining the rotation angle of each supersurface structural unit in the supersurface silicon-based nanocolumn array; The supersurface silicon-based nanocolumn array is constructed based on preset geometric parameters and the rotation angle of each supersurface structural unit.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a structured light generation device based on phase recovery optimization, and the structured light generation device based on phase recovery optimization includes: A phase recovery optimization module is used to recover the initial random phase based on the prior information of the target intensity distribution to obtain an optimized phase distribution; A metasurface device building module, used to build a metasurface silicon-based nanopillar array based on the optimized phase distribution; The structured light generation module is used to phase modulate the input light based on the metasurface silicon-based nanocolumn array to obtain the target structured light.

[0015] In addition, to achieve the above-mentioned objectives, the present application also proposes a structured light generation device based on phase recovery optimization. The structured light generation device based on phase recovery 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 recovery optimization as described above.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the structured light generation method based on phase recovery optimization as described above are implemented.

[0017] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the structured light generation method based on phase recovery optimization as described above are implemented.

[0018] The present application provides a structured light generation method based on phase recovery optimization, which performs phase recovery on the initial random phase based on the prior information of the target intensity distribution to obtain an optimized phase distribution; constructs a metasurface silicon-based nanocolumn array based on the optimized phase distribution; and phase modulates the input light based on the metasurface silicon-based nanocolumn array to obtain the target structured light. The present application designs a phase recovery optimization algorithm, which effectively uses the prior information of the target intensity distribution to optimize the phase recovery process, improves the accuracy and computational efficiency of phase recovery, and uses the metasurface silicon-based nanocolumn array to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, which can significantly improve the quality and performance of the structured light, and solves the technical problem of insufficient phase recovery accuracy affecting the quality and performance of the generated structured light. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 This is a flow chart of Embodiment 1 of the structured light generation method based on phase recovery optimization of the present application; Figure 2 A schematic diagram of a supersurface silicon-based nanocolumn array of a structured light generation method based on phase recovery optimization provided in Example 1 of the present application; Figure 3 A schematic diagram of a metasurface structure unit of a structured light generation method based on phase recovery optimization provided in Example 1 of the present application; Figure 4 This is a flow chart of Embodiment 2 of the structured light generation method based on phase recovery optimization of the present application; Figure 5 This is a flow chart of Embodiment 3 of the structured light generation method based on phase recovery optimization of the present application; Figure 6 This is a schematic diagram of the module structure of a structured light generating device based on phase recovery optimization according to an embodiment of the present application; Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the structured light generation method based on phase recovery optimization in an embodiment of the present application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0024] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solutions of the embodiments of the present application are: 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 nanocolumn array is constructed; based on the metasurface silicon-based nanocolumn array, the input light is phase modulated to obtain the target structured light.

[0026] At present, the IFTA algorithm is usually used for phase recovery in the process of generating structured light. The IFTA algorithm starts with iterative calculation from the initial intensity distribution and phase information, and gradually approaches the target phase distribution. However, the traditional IFTA algorithm has problems such as slow convergence speed, poor recovery quality and low diffraction efficiency. The accuracy of phase recovery is insufficient, which affects the quality and performance of the generated structured light.

[0027] The present application provides a solution to design a phase recovery optimization algorithm, effectively utilize the prior information of the target intensity distribution to optimize the phase recovery process, improve the accuracy and computational efficiency of phase recovery, and utilize a metasurface silicon-based nanorod array to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, which can significantly improve the quality and performance of the structured light and solve the technical problem of insufficient phase recovery accuracy that affects the quality and performance of the generated structured light.

[0028] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device that can realize the above functions, a structured light generation device based on phase recovery optimization, etc., and this embodiment does not specifically limit this. The following takes the structured light generation device based on phase recovery optimization as an example to illustrate this embodiment and the following embodiments.

[0029] The present application embodiment provides a structured light generation method based on phase recovery optimization, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the structured light generation method based on phase recovery optimization of the present application.

[0030] In this embodiment, the structured light generation method based on phase recovery optimization includes steps S10 to S30: Step S10, based on the prior information of the target intensity distribution, performing phase recovery on the initial random phase to obtain an optimized phase distribution; It should be noted that the target intensity distribution is the desired intensity distribution in the focal plane. When generating structured light, the IFTA algorithm is commonly used. The IFTA algorithm is based on the principle of iterative amplitude and phase, and can enforce constraints on amplitude and phase in two Fourier domains until the target result is achieved. Generally speaking, in order to obtain the target intensity distribution in the focal plane, a series of calculations are required. The complex amplitude formula of the input light field of a pure phase diffractive optical element (DOE) is as follows:

[0031] In the formula, represents the complex amplitude of the input light field in the focal plane, represents the complex amplitude of the incident light, represents the phase distribution of DOE, and its transfer function is Similarly, the formula for the complex amplitude of the output light field in the focal plane is as follows:

[0032] In the formula, represents the complex amplitude of the output light field in the focal plane, represents the complex amplitude of the input light field in the focal plane, and represent the target amplitude distribution (desired amplitude distribution) and target phase distribution (desired phase distribution) of the focal plane, respectively. is the wave vector, The calculation formula for the target intensity distribution in the focal plane is as follows:

[0033] In the formula, represents the target intensity distribution in the focal plane, represents the complex amplitude of the output light field in the focal plane, represents the target amplitude distribution in the focal plane. Thus, the DOE phase can be designed by solving the above equation.

[0034] In addition, it should be noted that in the process of solving the integral equation through 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 initial phase selection greatly affects the convergence speed and accuracy of the algorithm. Therefore, this embodiment improves and optimizes the IFTA algorithm, and designs a phase recovery optimization algorithm (MIFTA) to optimize the determination of the initial phase and the iterative update of the phase. The MIFTA algorithm utilizes the prior information of the target distribution of the structured light array (such as sparsity, symmetry, etc.), which can not only speed up the convergence speed of the algorithm, but also improve the accuracy of phase recovery and improve the precision compared with the traditional IFTA algorithm.

[0035] It can be understood that, in this embodiment, the prior information of the target intensity distribution can be the symmetry of the target intensity distribution, that is, symmetry prior information, or the sparsity of the target intensity distribution, that is, sparsity prior information, and there is no specific limitation on this.

[0036] 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 recovery optimization. When optimizing phase recovery using the symmetry prior information of the target intensity distribution, it is usually necessary to impose symmetry constraints on the diagonal lines on the initial random phase and the updated phase to improve the accuracy of phase recovery. When optimizing phase recovery using the sparsity prior information of the target intensity distribution, it is usually necessary to introduce different sparsity constraints based on different target distributions, so that the final phase distribution can satisfy the sparsity prior information, adapt to different situations, and improve the accuracy of phase recovery.

[0037] The traditional IFTA algorithm recovers the phase through multiple iterations in the frequency domain and the spatial domain, and its convergence speed is relatively slow, especially in high-dimensional space where the computational cost is high. In this embodiment, the MIFTA algorithm can more effectively utilize prior information (such as symmetry or sparsity), optimize the phase recovery process, and improve the convergence speed and recovery accuracy of the algorithm.

[0038] Step S20, constructing a supersurface silicon-based nanorod array based on the optimized phase distribution; It should be noted that the reference Figure 2The supersurface silicon-based nanorod array is a supersurface device, which is composed of multiple supersurface structural units distributed in an array. Figure 3 The metasurface structural unit consists of a single-layer amorphous silicon cuboid nanorod placed on a SiO2 substrate, and the rotation angle of the nanorod is , the length is L, the width is W, the height is H, and the lattice constant of the SiO2 substrate is P.

[0039] In a feasible implementation, step S20 may include steps S201-S202: Step S201, determining the rotation angle of each supersurface structural unit in the supersurface silicon-based nanorod array based on the optimized phase distribution; It can be understood that, according to the basic principle of geometric phase, phase modulation can be performed 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 structure unit (nanorod) are the same, and the rotation angle is set according to the optimized phase distribution obtained in step S10. The rotation angle of the nanorods in the range of 0-180° on the incident plane , corresponding to the geometric phase in the range of 0-360° transmitted on the output plane .

[0040] Step S202, constructing the supersurface silicon-based nanorod array based on preset geometric parameters and the rotation angle of each supersurface structural unit.

[0041] It should be noted that the preset geometric parameters are pre-set related parameters, including at least the lattice constant of the substrate and the length, width and height of the metasurface structural unit (nanorod).

[0042] In specific implementation, the lattice constant P can be set to 250nm, and by optimizing the cross-polarization parameters, a set of length L, width W and height H with higher polarization conversion efficiency is selected when the incident light wavelength is 630nm, for example: H=300nm, L=135nm, W=85nm.

[0043] It can be understood that after determining the lattice constant of the substrate and the length, width, height and rotation angle of the metasurface structural unit (nanorod), the preparation of the metasurface silicon-based nanorod array can be completed by using the general standard electron beam lithography technology and etching process.

[0044] Step S30: Based on the metasurface silicon-based nanorod array, phase modulate the input light to obtain target structured light.

[0045] It should be noted that the input light is the input light, and the target structured light is the structured light finally generated. In this embodiment, the target structured light is generated by a supersurface silicon-based nanorod array, and the far-field light spot is uniform and has a high diffraction efficiency.

[0046] Furthermore, the finite difference time domain (FDTD) software is used to calculate the depth of focus (DOF) of the zero-order Bessel beam array generated by the traditional IFTA algorithm and the MIFTA algorithm. The theoretical calculation formula for the depth of focus of the zero-order Bessel beam is:

[0047] In the formula, represents the theoretical value of the depth of focus, represents the diameter of the metasurface, and it is deduced that the non-diffraction propagation distance can be further increased by increasing the size of the metasurface and reducing the NA (numerical aperture). For NA=0.2, The theoretical value is 117.58 μm, and the experimental results of the traditional IFTA algorithm and the MIFTA algorithm are The non-uniformity of the traditional IFTA algorithm and the MIFTA algorithm are 0.1479 and 0.0999, respectively. The MIFTA algorithm shows better background noise suppression and center zero-order intensity suppression capabilities, and has higher uniformity, which can achieve better imaging quality.

[0048] It can be understood that the phase distribution restored by the MIFTA algorithm can generate structured light with higher uniformity and accuracy. Compared with the traditional IFTA algorithm, the phase recovery process of this embodiment can obtain a more uniform light spot, thereby improving the quality of the generated structured light. In addition, by utilizing the precise phase modulation characteristics of the metasurface silicon-based nanocolumn array, the generated structured light has a higher diffraction efficiency, and the metasurface structure can achieve high diffraction efficiency structured light output with lower loss. In addition, this embodiment combines phase recovery with optical device design to avoid multiple iterative calculations and complex adjustment steps of optical elements, thereby improving the overall calculation efficiency.

[0049] This embodiment provides a structured light generation method based on phase recovery optimization. Based on the prior information of the target intensity distribution, the initial random phase is phase recovered to obtain the optimized phase distribution; based on the optimized phase distribution, a metasurface silicon-based nanocolumn array is constructed; based on the metasurface silicon-based nanocolumn array, the input light is phase modulated to obtain the target structured light. A phase recovery optimization algorithm is designed to effectively use the prior information of the target intensity distribution to optimize the phase recovery process, improve the accuracy and computational efficiency of phase recovery, and use the metasurface silicon-based nanocolumn array to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, which can significantly improve the quality and performance of the structured light.

[0050] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 4 , step S10 may include steps S101 to S105: Step S101, adding a diagonal symmetry constraint to the initial random phase based on the symmetry prior information of the target intensity distribution to obtain an initial optimized phase; It should be noted that the initial optimized phase is the data obtained by adjusting the initial random phase using the symmetry prior information. The random selection range is 0 to The phase is used as the initial random phase of the input, and the symmetry prior information of the target intensity distribution is used to impose symmetry constraints on the diagonal line on the initial random phase to obtain the initial optimized phase.

[0051] In a 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, the target triangular range being any one of an upper triangular range and a lower triangular range; determining the transposed phase corresponding to the initial random phase based on the transposed 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.

[0052] It should be noted that the target triangular range, i.e., the designated triangular part, can be any one of an upper triangular range and a lower triangular range, the upper triangular range being the upper triangular part, and the lower triangular range being the lower triangular part, that is, the target triangular range of the initial random phase is the upper triangular part / lower triangular part of the initial random phase. According to the target triangular range of the initial random phase, the corresponding data obtained is the triangular phase of the initial random phase, and according to the transposed matrix of the initial random phase, the corresponding data obtained is the transposed phase of the initial random phase.

[0053] It can be understood that the initial optimization phase consists of two parts, namely, the triangular phase of the initial random phase and the transposed phase of the initial random phase. In other words, the initial optimization phase is the sum of the triangular phase and the transposed phase of the initial random phase, as shown below:

[0054] In the formula, represents the initial optimization phase, represents the triangular phase of the initial random phase, Represents the transposed phase of the initial random phase.

[0055] Step S102, calculating the complex amplitude of the input diffraction plane based on the initial optimized phase; In a 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.

[0056] It should be noted that the initial complex amplitude is the complex amplitude obtained by preliminary calculation. The initial complex amplitude of the focal plane is calculated using Fast Fourier Transform (FFT). The calculation relationship between the initial optimized phase and the initial complex amplitude is as follows:

[0057]

[0058] In the formula, represents the initial complex amplitude of the output light field in the focal plane, represents the complex amplitude of the input light field in the focal plane, and represent the target amplitude distribution and target phase distribution in the focal plane, respectively. is the wave vector, , represents the complex amplitude of the incident light, represents the initial optimization phase.

[0059] It can be understood that using the target amplitude distribution Replace the calculated initial complex amplitude , and reset the initial complex amplitude, , thus obtaining a new initial complex amplitude 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 calculation relationship between the new initial complex amplitude and the complex amplitude of the input DOE plane is as follows:

[0060] In the formula, represents the complex amplitude of the input diffraction plane, represents the new initial complex amplitude.

[0061] Step S103, determining a diffraction phase based on the complex amplitude of the input diffraction plane; 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. The calculation relationship is as follows:

[0062] In the formula, represents the diffraction phase, represents the complex amplitude of the input diffraction plane.

[0063] Step S104, adding a diagonal symmetry constraint to the diffraction phase based on the symmetry priori information of the target intensity distribution to obtain a diffraction optimized phase; In a 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, the target triangular range being any one of an upper triangular range and a lower triangular range; determining the transposed phase corresponding to the diffraction phase based on the transposed matrix of the diffraction phase; and taking the sum of the triangular phase and the transposed phase of the diffraction phase as the diffraction optimization phase.

[0064] It should be noted that the diffraction optimized phase is the data obtained after adjusting the diffraction phase using the symmetry prior information. The target triangular range of the diffraction phase is the upper triangular part / lower triangular part of the diffraction phase. According to the target triangular range of the diffraction phase, the corresponding data obtained is the triangular phase of the diffraction phase, and according to the transposed matrix of the diffraction phase, the corresponding data obtained is the transposed phase of the diffraction phase.

[0065] It can be understood that the diffraction optimization phase consists of two parts, namely, the triangular phase of the diffraction phase and the transposed phase of the diffraction phase. In other words, the diffraction optimization phase is the sum of the triangular phase and the transposed phase of the diffraction phase, as shown below:

[0066] In the formula, represents the diffraction-optimized phase, represents the trigonometric phase of the diffraction phase, Represents the transposed phase of the diffraction phase.

[0067] Step S105: when the iteration termination condition is met, the diffraction optimized phase is used as the target optimized phase.

[0068] It should be noted that the iteration termination condition is the condition that needs to be met for the iteration to terminate, for example: 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 Greater than or equal to the set number of times threshold. The error between the actual amplitude distribution and the target amplitude distribution can be calculated using the following calculation relationship:

[0069] In the formula, Indicates the error, represents the actual amplitude distribution, i.e., the initial complex amplitude of the output light field in the focal plane, Represents the target amplitude distribution.

[0070] It can be understood that if the iteration termination condition is met, the currently obtained diffraction optimized phase is used as the target optimized phase, and step S20 is continued; if the iteration termination condition is not met, the initial phase is updated to the diffraction optimized phase, and the process returns to step S102.

[0071] Furthermore, set a 4 4 uniform dot matrix as the target intensity distribution, the IFTA algorithm and MIFTA algorithm are used for phase recovery respectively, and the number of iterations is set to 200. Using the symmetry of the target intensity distribution as prior information, the MIFTA algorithm imposes symmetry constraints on the diagonal line on the initial random phase and the updated phase. The average intensity and non-uniformity after phase recovery by the IFTA algorithm are 0.949 and 0.0569, respectively, and the average intensity and non-uniformity after phase recovery by the MIFTA algorithm are 0.9922 and 0.0076, respectively. It can be seen that compared with IFTA, MIFTA performs better in terms of accuracy.

[0072] This embodiment provides a structured light generation method based on phase recovery optimization. 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; 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 symmetry prior information of the target intensity distribution, a diagonal symmetry constraint is added to the diffraction phase to obtain the diffraction optimized phase; when the iteration termination condition is met, the diffraction optimized phase is used as the target optimized phase. A phase recovery optimization algorithm is designed to effectively use the prior information of the target intensity distribution to optimize the phase recovery process, improve the accuracy and computational efficiency of phase recovery, and use the metasurface silicon-based nanorod array to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, which can significantly improve the quality and performance of structured light.

[0073] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiment can refer to the above introduction, and will not be repeated later. Figure 5 , step S10 may include steps S101'~S105': Step S101', determining the initial phase image based on the initial random phase; It should be noted that the initial phase image, ie the initial phase image, is determined according to the initial random phase. Generally speaking, the initial phase image is usually a zero matrix or a random phase image.

[0074] It can be understood that, assuming that the complex phase information of the target object is controlled by some sparse pattern (such as a sparse array distribution), the actual measured intensity information (intensity image) is associated with the phase information through Fourier transform.

[0075] Step S102', performing intensity constraints on the frequency domain data of the initial phase image based on the measured intensity information to obtain a phase image; In a feasible implementation, step S102' may include: performing Fourier transform on the initial phase image to obtain the frequency domain data; adjusting the amplitude of the frequency domain data based on the measurement intensity information to obtain updated frequency domain data; and determining the phase image based on the updated frequency domain data.

[0076] It should be noted that the measured intensity information is the actual measured intensity information, that is, the intensity image. The frequency domain data is the value obtained after the initial phase image is Fourier transformed. The updated frequency domain data is the updated frequency domain data. The phase image is the image obtained after the initial phase image is subjected to intensity constraints.

[0077] It can be understood that the initial phase image is Fourier transformed to obtain a frequency domain representation. Since the actual measurement obtains intensity information, the intensity constraint is , that is, the amplitude of the frequency domain data is forced to be adjusted to match the measured intensity information, thereby obtaining updated frequency domain data, and then calculating the updated phase image, as shown below:

[0078] In the formula, represents the phase image, represents the initial phase image, Indicates the measurement intensity information, represents the inverse Fourier transform, Indicates updating frequency domain data.

[0079] Step S103', based on the sparsity prior information of the target intensity distribution, sparse regularization is performed on the phase image to obtain an updated phase image; In a 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 a sparsity constraint corresponding to the target sparse transformation operation to obtain the updated phase image.

[0080] It should be noted that this embodiment introduces a sparsity constraint so that the final solution can satisfy the sparsity prior information. The target sparse transformation operation is the selected sparse transformation operation. According to the sparsity prior information, the corresponding target sparse transformation operation is selected. Different sparsity prior information usually corresponds to different target sparse transformation operations, so that it can adapt to different scenes. The sparse transformation phase image is the phase image after sparse transformation. The updated phase image is the iteratively updated phase image.

[0081] It can be understood that according to the target sparse transformation operation, the phase image Perform a sparse transformation to obtain a sparse transformed phase image The preset norm is the L1 norm (the sum of the absolute values ​​of each element in the vector), and the sparse transformed phase image is regularized using the L1 norm and sparsity constraint as shown below:

[0082] In the formula, To update the phase image, is the data fidelity term, measuring the error of the current estimate, is a regularization term that makes the solution sparse. is the L0 norm after sparse transformation (the number of non-zero elements in the vector), indicating the sparsity of the solution and controlling the sparsity mode. and are regularization parameters, which control the strength of the L1 norm and sparsity constraint respectively. is the measurement matrix, usually the Fourier transform operation matrix. Solved by gradient descent method or variational method, we get .

[0083] Step S104', calculating the image difference between the updated phase image and the initial phase image; It can be understood that the update amount of the phase image is checked, that is, the update phase image is calculated. and the initial phase image The image difference between .

[0084] Step S105 ′: when the image difference satisfies a convergence condition, taking the phase distribution corresponding to the updated phase image as the optimized phase distribution.

[0085] It can be understood that the convergence condition is the condition that needs to be met for the iteration to terminate, for example: the image difference between the updated phase image and the initial phase image is less than or equal to the set convergence threshold. If the image difference meets the convergence condition, the iteration is stopped 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.

[0086] This embodiment provides a structured light generation method based on phase recovery optimization, which determines the initial phase image based on the initial random phase; performs intensity constraints on the frequency domain data of the initial phase image based on the measured intensity information to obtain the phase image; performs sparse regularization on the phase image based on the sparsity prior information of the target intensity distribution to obtain the updated phase image; calculates the image difference between the updated phase image and the initial phase image; and when the image difference meets the convergence condition, uses the phase distribution corresponding to the updated phase image as the optimized phase distribution. A phase recovery optimization algorithm is designed to effectively use the prior information of the target intensity distribution to optimize the phase recovery process, improve the accuracy and computational efficiency of phase recovery, and use the metasurface silicon-based nanocolumn array to achieve precise phase modulation, thereby generating structured light with high uniformity and high diffraction efficiency, which can significantly improve the quality and performance of structured light.

[0087] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the structured light generation method based on phase recovery optimization of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0088] This application also provides a structured light generation device based on phase recovery optimization, please refer to Figure 6 , the structured light generating device based on phase recovery optimization includes: The phase recovery optimization module 10 is used to perform phase recovery on the initial random phase based on the prior information of the target intensity distribution to obtain an optimized phase distribution; A metasurface device construction module 20, for constructing a metasurface silicon-based nanocolumn array based on the optimized phase distribution; The structured light generation module 30 is used to phase modulate the input light based on the metasurface silicon-based nanorod array to obtain target structured light.

[0089] In a feasible implementation manner, the phase recovery optimization module 10 is further used to add a diagonal symmetry constraint to the initial random phase based on the symmetry prior information of the target intensity distribution to obtain an initial optimized phase; Based on the initial optimized phase, calculating the complex amplitude of the input diffraction plane; determining a diffraction phase based on the complex amplitude of the input diffraction plane; Based on the symmetry prior information of the target intensity distribution, adding a diagonal symmetry constraint 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.

[0090] In a feasible implementation manner, the phase recovery optimization module 10 is further used to determine the initial complex amplitude of the output light field in the focal plane based on the initial optimized phase; 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.

[0091] In a feasible implementation manner, the phase recovery optimization module 10 is further used to determine the triangular phase corresponding to the initial random phase based on the target triangular range of the initial random phase, and the target triangular range is any one of an upper triangular range and a lower triangular range; Based on the transposed matrix of the initial random phase, determining the transposed phase corresponding to the initial random phase; The sum of the triangular phase and the transposed phase of the initial random phase is used as the initial optimized phase.

[0092] In a feasible implementation manner, the phase recovery optimization module 10 is further used to determine the initial phase image based on the initial random phase; Based on the measured intensity information, intensity constraints are performed on the frequency domain data of the initial phase image to obtain a phase image; Based on the sparsity prior information of the target intensity distribution, the phase image is sparsely regularized to obtain an updated phase image; calculating an image difference between the updated phase image and the initial phase image; When the image difference satisfies a convergence condition, the phase distribution corresponding to the updated phase image is used as the optimized phase distribution.

[0093] In a feasible implementation manner, the phase recovery optimization module 10 is further used to perform Fourier transform on the initial phase image to obtain the frequency domain data; Based on the measurement strength information, adjusting the amplitude of the frequency domain data to obtain updated frequency domain data; Based on the updated frequency domain data, the phase image is determined.

[0094] In a feasible implementation manner, the phase recovery optimization module 10 is further used to determine a target sparse transformation operation based on the sparsity prior information of the target intensity distribution; Based on the target sparse transform operation, performing a sparse transform on the phase image to obtain a sparse transformed phase image; Based on a preset norm and a sparsity constraint corresponding to the target sparse transform operation, the sparse transform phase image is regularized to obtain the updated phase image.

[0095] In a feasible implementation manner, the metasurface device construction module 20 is further used to determine the rotation angle of each metasurface structural unit in the metasurface silicon-based nanocolumn array based on the optimized phase distribution; The supersurface silicon-based nanocolumn array is constructed based on preset geometric parameters and the rotation angle of each supersurface structural unit.

[0096] The structured light generation device based on phase recovery optimization provided by the present application adopts the structured light generation method based on phase recovery optimization in the above-mentioned embodiment, which can solve the technical problem that the accuracy of phase recovery is insufficient and affects 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 recovery optimization provided by the present application are the same as the beneficial effects of the structured light generation method based on phase recovery optimization provided by the above-mentioned embodiment, and the other technical features of the structured light generation device based on phase recovery optimization are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0097] The present application provides a structured light generation device based on phase recovery optimization, and the structured light generation device based on phase recovery optimization includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the structured light generation method based on phase recovery optimization in the above-mentioned embodiment one.

[0098] Reference below Figure 7 , which shows a schematic diagram of the structure of a phase recovery optimization-based structured light generation device suitable for implementing the embodiment of the present application. The phase recovery optimization-based structured light generation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The structured light generation device based on phase recovery optimization shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0099] like Figure 7As shown, the structured light generation device based on phase recovery optimization may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from the storage device 1003 to RAM (Random Access Memory) 1004. Various programs and data required for the operation of the structured light generation device based on phase recovery optimization are also stored in RAM1004. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the phase recovery optimization-based structured light generation device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a phase recovery optimization-based structured light generation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0100] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0101] The structured light generation device based on phase recovery optimization provided by the present application adopts the structured light generation method based on phase recovery optimization in the above-mentioned embodiment, which can solve the technical problem that the accuracy of phase recovery is insufficient and affects 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 recovery optimization provided by the present application are the same as the beneficial effects of the structured light generation method based on phase recovery optimization provided by the above-mentioned embodiment, and the other technical features in the structured light generation device based on phase recovery optimization are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0102] It should be understood that the various parts disclosed in this application can be implemented by 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 one or more embodiments or examples in a suitable manner.

[0103] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0104] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the structured light generation method based on phase recovery optimization in the above-mentioned embodiment.

[0105] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0106] The computer-readable storage medium may be included in the structured light generation device based on phase recovery optimization; or may exist independently without being assembled into the structured light generation device based on phase recovery optimization.

[0107] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by a structured light generation device based on phase recovery optimization, the structured light generation device based on phase recovery optimization: based on prior information of target intensity distribution, performs phase recovery on the initial random phase to obtain an optimized phase distribution; based on the optimized phase distribution, constructs a metasurface silicon-based nanocolumn array; based on the metasurface silicon-based nanocolumn array, phase modulates the input light to obtain the target structured light.

[0108] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0109] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0110] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0111] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned structured light generation method based on phase recovery optimization, and can solve the technical problem that the accuracy of phase recovery is insufficient and affects 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 the present application are the same as the beneficial effects of the structured light generation method based on phase recovery optimization provided in the above-mentioned embodiment, and will not be elaborated here.

[0112] The present application also provides a computer program product, including a computer program, which implements the steps of the structured light generation method based on phase recovery optimization as described above when the computer program is executed by a processor.

[0113] The computer program product provided by the present application can solve the technical problem that the accuracy of phase recovery is insufficient, 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 by the present application are the same as the beneficial effects of the structured light generation method based on phase recovery optimization provided by the above embodiment, and will not be repeated here.

[0114] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A structured light generation method based on phase recovery optimization, characterized in that: The method comprises: Based on the prior information of target intensity distribution, the initial random phase is phase recovered to obtain the optimized phase distribution; Based on the optimized phase distribution, constructing a supersurface silicon-based nanocolumn array; Based on the metasurface silicon-based nanocolumn array, the input light is phase modulated to obtain the target structured light.

2. The method according to claim 1, characterized in that The prior information is symmetry prior information. The step of performing phase recovery on the initial random phase based on the prior information of target intensity distribution to obtain an optimized phase distribution includes: Based on the symmetry prior information of the target intensity distribution, adding a diagonal symmetry constraint to the initial random phase to obtain an initial optimized phase; Based on the initial optimized phase, calculating the complex amplitude of the input diffraction plane; determining a diffraction phase based on the complex amplitude of the input diffraction plane; Based on the symmetry prior information of the target intensity distribution, adding a diagonal symmetry constraint 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.

3. The method according to claim 2, characterized in that The step of calculating the complex amplitude of the focal plane based on the initial optimized phase comprises: Based on the initial optimized phase, determining an initial complex amplitude of the output light field in the focal plane; 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.

4. The method according to claim 2, characterized in that 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 comprises: Determine a triangular phase corresponding to the initial random phase based on a target triangular range of the initial random phase, wherein the target triangular range is any one of an upper triangular range and a lower triangular range; Based on the transposed matrix of the initial random phase, determining the transposed phase corresponding to the initial random phase; The sum of the triangular phase and the transposed phase of the initial random phase is used as the initial optimized phase.

5. The method according to claim 1, characterized in that The prior information is sparse prior information. The step of performing phase recovery on the initial random phase based on the prior information of target intensity distribution to obtain an optimized phase distribution includes: Based on the initial random phase, determining the initial phase image; Based on the measured intensity information, intensity constraints are performed on the frequency domain data of the initial phase image to obtain a phase image; Based on the sparsity prior information of the target intensity distribution, the phase image is sparsely regularized to obtain an updated phase image; calculating an image difference between the updated phase image and the initial phase image; When the image difference satisfies a convergence condition, the phase distribution corresponding to the updated phase image is used as the optimized phase distribution.

6. The method according to claim 5, characterized in that The step of performing intensity constraints on the frequency domain data of the initial phase image based on the measured intensity information to obtain the phase image comprises: Performing Fourier transform on the initial phase image to obtain the frequency domain data; Based on the measurement strength information, adjusting the amplitude of the frequency domain data to obtain updated frequency domain data; Based on the updated frequency domain data, the phase image is determined.

7. The method according to claim 5, characterized in that The step of performing sparse regularization on the phase image based on the sparsity prior information of the target intensity distribution to obtain an updated phase image comprises: Determining a target sparse transformation operation based on the sparsity prior information of the target intensity distribution; Based on the target sparse transform operation, performing a sparse transform on the phase image to obtain a sparse transformed phase image; Based on a preset norm and a sparsity constraint corresponding to the target sparse transform operation, the sparse transform phase image is regularized to obtain the updated phase image.

8. The method according to any one of claims 1 to 7, characterized in that The step of constructing a supersurface silicon-based nanocolumn array based on the optimized phase distribution comprises: Based on the optimized phase distribution, determining the rotation angle of each supersurface structural unit in the supersurface silicon-based nanocolumn array; The supersurface silicon-based nanocolumn array is constructed based on preset geometric parameters and the rotation angle of each supersurface structural unit.

9. A structured light generation device based on phase recovery optimization, characterized in that: The device comprises: A phase recovery optimization module is used to recover the initial random phase based on the prior information of the target intensity distribution to obtain an optimized phase distribution; A metasurface device building module, used to build a metasurface silicon-based nanopillar array based on the optimized phase distribution; The structured light generation module is used to phase modulate the input light based on the metasurface silicon-based nanocolumn array to obtain the target structured light.

10. 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 according to any one of claims 1 to 8.

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