High-efficiency high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation

Abstract

The invention discloses a high-efficiency high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation, which comprises the following steps: firstly, obtaining three-dimensional cross-correlation photon counting data of a target, then, carrying out point time migration and clipping processing based on single-point cross-correlation data, and finally, carrying out fast Fourier transformation and linear interpolation to obtain a three-dimensional spectrum spread spectrum three-dimensional imaging result. And performing inverse fast Fourier transform operation to generate a final three-dimensional image. The time for all photons to reach the scanning surface is the same based on point time migration, so that the position of a hidden object can be more accurately reconstructed, the signal-to-noise ratio is enhanced, the data dimension is rearranged through data cutting operation, the calculated amount of subsequent Fourier transform is simplified, a linear interpolation method is used, the imaging resolution is improved, and the imaging precision is improved. The method is advantaged in that possible geometric distortion is corrected, data consistency is enhanced, final imaging detail representation is improved, the imaging effect is realized, and the imaging effect is better than that of a traditional non-line-of-sight imaging method.

Description

Technical Field

[0001] The present invention belongs to the field of non-line-of-sight imaging, image reconstruction and processing, and specifically relates to a high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation. Background Art

[0002] In recent years, the research on single photon counting three-dimensional imaging methods has been widely carried out. During the imaging process, the following problems exist:

[0003] There are often occluders or semi-occluders in front of complex targets. Light may reach the detector through multiple different paths. A spot or pixel contains multiple distances. An imaging method that can efficiently distinguish different distances is needed. The existing solutions can be roughly divided into four categories: The first category is based on the optimization method of a single pixel, such as the Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) based on the Bayesian model (Sergio Andrew M. Wallace, and Gavin J. Gibson. Bayesian Analysis of Lidar Signals with Multiple Returns [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29 (2): 2170-2180.), which uses the prior information of the parameters to be estimated to solve the image reconstruction problem. The constructed Markov chain can jump between different parameters and models. This method is suitable for the reconstruction of multiple distances of a single pixel, but the optimization process must be repeated pixel by pixel, and it takes many iterations to reach a stable distribution. The operation time is lengthy, the consumption of computing resources is large, and it is not suitable for real-time imaging; when the noise is very strong or the photon counting rate is very low, the imaging accuracy is greatly reduced. As a result, the second type of algorithm, the optimized RJMCMC technology, was born. For example, the adaptive RJMCMC method with random optimization adaptive automatic parameter adjustment mechanism (Yoann Altmann, Ximing Ren, et al., Lidar Waveform-Based Analysis of Depth Images Constructed Using Sparse Single-Photon [J]. Ieee Transactions On Image Processing, 2016, 25 (5): 1935-1946), based on the Markov field to establish a priori model of depth and intensity spatial correlation, the intensity accuracy of target imaging in turbid media is greatly improved. The third type of method is based on the local spatial correlation of three-dimensional data, using a three-dimensional deconvolution method to reconstruct a three-dimensional image through total variation optimization (Yu Hong, Shi Jie Liu, Zheng-Ping Li, Xin Huang, Peng Yu Jiang "Airborne single-photon LiDAR towards a small-sized and low-power payload," OPTICA 11 (5), 613-619 (2024)).The fourth type of algorithm is a target 3D image reconstruction method based on computer graphics. The authors directly use the point cloud denoising method to reconstruct the 3D image based on the local structural characteristics of the 3D point cloud data (Julián Tachella, Yoann Altmann, et al., Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers[J]. Nature Communications, 2019, 10(4984): 1-5.).

[0004] In summary, for three-dimensional imaging of complex large-scale scenes, optimizing individual pixels one by one is slow and computationally burdensome, which does not meet the needs of real-time imaging; local structural optimization is prone to losing key information and making it difficult to reconstruct high-resolution images; due to the large amount of data, global optimization will lead to slow processing speed and increased computational burden. Based on this, an efficient and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transform is proposed. Summary of the invention

[0005] In view of the above problems, the object of the present invention is to provide a high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation, which has better imaging effect than traditional non-line-of-sight imaging methods.

[0006] The specific technical solution for achieving the purpose of the present invention is:

[0007] A high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation comprises the following steps:

[0008] Step 1: Obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ;

[0009] Step 2, obtaining the size data and related parameter data of the target area;

[0010] Step 3, processing the point time offset of the single-point cross-correlation waveform data of the target and performing data clipping;

[0011] Step 4: Based on the Fourier high-dimensional transform imaging method, the processed data is subjected to fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations to generate a three-dimensional image.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] The present invention is based on the three-dimensional cross-correlated photon counting data of the original target to be imaged. During the processing, the data is preprocessed to solve the problem of data point time offset, so that all photons arrive at the scanning surface at the same time, which is conducive to more accurate reconstruction of the position of the hidden object and enhances the signal-to-noise ratio.

[0014] In subsequent operations, data cropping and rearrangement of data dimensions greatly simplifies the calculation of subsequent Fourier transforms. The use of linear interpolation methods improves imaging resolution, corrects possible geometric distortions, enhances data consistency, and improves the details of the final imaging to achieve the best imaging effect.

[0015] The present invention is further described below in conjunction with specific implementation modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the process flow of the efficient and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation in this scheme. DETAILED DESCRIPTION

[0017] A high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation comprises the following steps:

[0018] Step 1: Obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ;

[0019] That is, to obtain the single-point photon technical data, based on the fast Fourier transform method, calculate the single-point cross-correlation photon counting data x of the target e ∈R 1×M , based on the scanning galvanometer, the target is scanned by N rows and N columns to obtain N×N single-point cross-correlated photon counting data x e , finally forming the three-dimensional cross-correlation photon counting data m e ∈R N×N×M .

[0020] Step 2: Obtain the size data and related parameter data of the target area, including:

[0021] The size data of the target area w a :

[0022] w a =L×W

[0023] Among them, L and W represent the length and width of the target area respectively;

[0024] The relevant parameter data includes the detector parameter data and the cropping index c r, where the parameter data of the SPAD detector includes the time interval resolution b i , speed of light c, spatial resolution parameter N∈R, and time resolution parameter M∈R.

[0025] Step 3: Process the point time offset of the single-point cross-correlation waveform data of the target and perform data clipping:

[0026] Step 3-1: Process the point time offset of the single-point cross-correlation waveform data of the target so that all photons arrive at the scanning surface at the same time:

[0027] Step 3-1-1, extract matrix m e ∈R N×N×M All rows (actually representing the y-axis direction in space) and m e ∈R N ×N×M All columns of the matrix (actually representing the x-axis direction in space) constitute a two-dimensional space array m e_1 ∈R N×N ;

[0028] Step 3-1-2, get the two-dimensional space array m e_1 ∈R N×N The cross-correlation photon counting data in t o ∈R N×N (located in the z-axis direction, i.e., the time dimension), the cross-correlated photon counting data t o t o ∈R N×N Divide by the time interval resolution b of the detector i , get the two-dimensional space array m e_1 ∈R N×N The required displacement of each point in N×N ; and shift each point by its own shift amount p, which eliminates the time offset caused by the different optical paths of points at different positions, so that all photons arrive at the scanning surface at the same time.

[0029] Step 3-2: Crop the data, rearrange the data dimensions, and define the voxel distance grid:

[0030] Step 3-2-1, by extracting the matrix m e ∈R N×N×M The first c of the time dimension r elements, get the array

[0031] Step 3-2-2: Rearrange the array Dimension order, the array The dimension order is adjusted to: time, Y axis, X axis, and get an array

[0032] Step 3-2-3, create a vector with values ​​ranging from 0 to 1 and equally spaced, with a length of c r , and then copy the above vector into a three-dimensional matrix This matrix represents the distance of each voxel from the target area wall, where c r corresponds to the time dimension, and N corresponds to the space dimension.

[0033] Step 4: Based on the Fourier high-dimensional transform imaging method, the processed data is subjected to fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations to generate a three-dimensional image:

[0034] Step 4-1: Array To process, where ⊙ represents the Hadamard product, and then creates a three-dimensional array of all zeros Finally, the processed data d a1 Fill to The first half of c r ×N×N, t d_a The rest of remains zero;

[0035] Step 4-2: For a three-dimensional array Perform a three-dimensional fast Fourier transform to convert the time domain data to the frequency domain:

[0036]

[0037] in, Represents the input signal t d_a The spectral components at frequency indices u, v, w, where u, v, w are frequency indices corresponding to x, respectively. 1 ,y 1 ,z 1 The frequency positions in the direction, their value ranges are: u∈[0,2N-1],v∈[0,2N-1],w∈[0,2c r -1], whose index interval is x 1 ,y 1 ,z 1 is a spatial or temporal index used to traverse the input data t d_a Each element in has a value range of: 1 ∈[0,2N-1],y 1 ∈[0,2N-1],z 1 ∈[0,2c r -1], whose index interval is is the element in the input data matrix, which contains the original time domain and spatial domain data points. is a complex exponential function, which is the core part of the Fourier transform. It defines how to map a point in the time domain or space domain to the corresponding position in the frequency domain; i is the imaginary unit;

[0038] Step 4-3: Use the linear interpolation algorithm to calculate T(t d_a The z-axis direction data of )(u,v,w) is from the original frequency position, that is, wave number k z , mapped to the new frequency position k ′ z , and the y and x directions remain unchanged, that is:

[0039]

[0040] Where f is the frequency, k is x is the wave number on the x-axis, k y is the wave number on the y-axis, k z is the original wave number on the z-axis;

[0041] If the new value to be interpolated exceeds the specified range of the known data point in dimension, 0 is returned. ′ z The original frequency data k of the x and y axes x ,k y Together they form a new three-dimensional array Its dimensions are The size of the array remains the same, and each value in the z-axis direction is the new interpolated data calculated. This process implements remapping in the frequency domain so that the data is distributed at the new frequency position. For the generated three-dimensional array Let the part where z=0 be 0, and only keep the part where z>0, that is, only keep the forward propagation data;

[0042] Step 4-4: For a three-dimensional array Perform inverse fast Fourier transform to determine t(t v )(x 1 ,y 1 ,z 1 ):

[0043]

[0044] Step 4-5: Confirm The square of the absolute value of , thereby determining the square of the amplitude of the target reconstructed volume data, extract The first half, namely [c r ×N×N] part of the data to obtain the final reconstructed volume data, and name this new three-dimensional array Its size and original data Same, finally according to Generate images along the x, y, and z axes to complete three-dimensional imaging.

[0045] The present invention also provides a high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging system based on global frequency domain transformation, comprising the following modules:

[0046] Cross-correlation module: used to obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ;

[0047] Data processing module: used to process the point time offset of the single-point cross-correlation waveform data of the target and perform data clipping;

[0048] Three-dimensional imaging module: used to perform fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations on the processed data based on the Fourier high-dimensional transform imaging method to generate a three-dimensional image.

[0049] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0050] Step 1: Obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ;

[0051] Step 2, obtaining the size data and related parameter data of the target area;

[0052] Step 3, processing the point time offset of the single-point cross-correlation waveform data of the target and performing data clipping;

[0053] Step 4: Based on the Fourier high-dimensional transform imaging method, the processed data is subjected to fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations to generate a three-dimensional image.

[0054] The present invention also provides a computer storable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0055] Step 1: Obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ;

[0056] Step 2, obtaining the size data and related parameter data of the target area;

[0057] Step 3, processing the point time offset of the single-point cross-correlation waveform data of the target and performing data clipping;

[0058] Step 4: Based on the Fourier high-dimensional transform imaging method, the processed data is subjected to fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations to generate a three-dimensional image.

[0059] Example

[0060] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an kind" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0062] Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values ​​of the parts and steps set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to those of ordinary skill in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be considered as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so that once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0063] Combination Figure 1 , a high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation, comprising the following steps:

[0064] Step 1: Obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ; In this embodiment, the single-point cross-correlation waveform data x of the target is obtained k ∈R 1×2048, by scanning 512×512 pixels, 512×512 cross-correlation waveform data are obtained, denoted as m e ∈R 512×512×2048 ;

[0065] Step 2: Obtain the size data and related parameter data of the target area, including:

[0066] The size data of the target area w a :

[0067] w a =L×W

[0068] Among them, L and W represent the length and width of the target area respectively;

[0069] The relevant parameter data includes the detector parameter data and the cropping index c r , where the parameter data of the SPAD detector includes the time interval resolution b i , speed of light c, spatial resolution parameter N∈R, and time resolution parameter M∈R.

[0070] In this embodiment, the wall size w of the scan area is measured. a = L × W (L, W represent the length and width of the wall respectively, in meters, L = W = 2m), initialize the crop index c r =64, SPAD detector time interval resolution b i =3.2000e-11ps, speed of light c = 3×10 8 m / s, spatial resolution parameter N = 512, temporal resolution parameter M = 2048.;

[0071] Step 3: Process the point time offset of the single-point cross-correlation waveform data of the target and perform data clipping:

[0072] Step 3-1: Process the point time offset of the single-point cross-correlation waveform data of the target so that all photons arrive at the scanning surface at the same time:

[0073] Step 3-1-1, access matrix m e ∈R 512×512×2048 All rows of , actually represent the y-axis direction in space. e ∈R 512×512×2048 All the columns of the matrix actually represent the x-axis direction in space. These two operations together access the entire three-dimensional array m e ∈R 512×512×2048 Each point on the spatial dimension [512,512] in . These points form a two-dimensional spatial array m e_1 ∈R 512×512 ;

[0074] Step 3-1-2, then access the above two-dimensional space array m e_1 ∈R 512×512 The cross-correlated photon counting data at each point in t o ∈R 512×512 (located in the z-axis direction, i.e., the time dimension), and then the obtained cross-correlated photon counting data t o ∈R 512×512 Divided by the time interval resolution of the SPAD detector 3.2000e-11ps, we get m e_1 ∈R 512×512 The required displacement of each point on 512×512 , and then each point is shifted by its own shift amount p, which eliminates the time offset caused by the different optical path lengths of points at different positions, making the time for all photons to arrive at the scanning surface the same.

[0075] Step 3-2: Crop the data, rearrange the data dimensions, and define the voxel distance grid:

[0076] Step 3-2-1, by selecting m e ∈R 512×512×2048 The first 64 elements of the third dimension (time dimension) in the array reduce unnecessary calculations and ensure that only the data related to reconstruction is retained, which now becomes d a ∈R 512×512×64 ;

[0077] Step 3-2-2, rearrange the order of the array dimensions. Here, the data matrix d a ∈R 512×512×64 The three dimensions are rearranged as [time, Y axis, X axis], making the time dimension the new first dimension, i.e., d a ∈R 64×512×512 ;

[0078] Step 3-2-3, create a vector with equal intervals from 0 to 1, with a length of 64. Then copy the above vector into a three-dimensional matrix g r_z ∈R 64×512×512 , this matrix represents the distance of each voxel from the wall, where the length of the corresponding time dimension is 64 and the length of the corresponding space dimension is 512.

[0079] Step 4: Based on the Fourier high-dimensional transform imaging method, the processed data is subjected to fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations to generate a three-dimensional image:

[0080] Step 4-1, pair d a ∈R 64×512×512 To process the data, where ⊙ represents the Hadamard product (i.e., element-wise multiplication). Then create a three-dimensional array of all zeros t d_a ∈0 128×1024×1024 , and finally the processed data d a1 Fill to t d_a ∈0 128×1024×1024 The first half of 64×512×512, t d_d The rest of remains zero;

[0081] Step 4-2, then t d_a Perform a three-dimensional fast Fourier transform (3D FFT), that is

[0082]

[0083] Where (1)T(t d_a )(u,v,w)∈R 128×1024×1024 , is a complex value in the frequency domain, representing the input signal t d_a The spectral components at frequency indices u, v, w. u, v, w are frequency indices corresponding to x, respectively. 1 ,y 1 ,z 1 The frequency positions in the direction, their value ranges are: u∈[0,1023],v∈[0,1023],w∈[0,127]. The index interval is x 1 ,y 1 ,z 1 is a spatial or temporal index used to traverse the input data td a Each element in . Their value ranges are: 1 ∈[0,1023],y 1 ∈[0,1023],z 1 ∈[0,127]. Its index interval is t(t d_a )(x 1 ,y 1 ,z 1 )∈R 128×1024×1024 It is the element in the input data matrix, which contains the original time domain and spatial domain data points. is the complex exponential function, which is the core part of the Fourier transform. It defines how to map a point in the time domain or space domain to the corresponding position in the frequency domain. i is the imaginary unit, i 2 =-1.

[0084] Step 4-3: Use the linear interpolation algorithm to calculate T(t d_a The z-axis direction data of )(u,v,w) is from the original frequency position, that is, wave number k z, mapped to the new frequency position k ′ z , and the y and x directions remain unchanged, that is:

[0085]

[0086] Where f is the frequency, k is x is the wave number on the x-axis, k y is the wave number on the y-axis, k z is the original wave number on the z-axis;

[0087] If the new value to be interpolated exceeds the specified range of the known data point in dimension, 0 is returned. ′ z The original frequency data k of the x and y axes x ,k y Together they form a new three-dimensional array t v ∈R 128×1024×1024 , whose dimensions are similar to The size of the array remains the same, and each value in the z-axis direction is the new interpolated data calculated. This process achieves remapping in the frequency domain so that the data is distributed at the new frequency position. For the generated three-dimensional array t v ∈R 128×1024×1024 , let the part where z=0 be 0, and only keep the part where z>0, that is, only keep the forward propagation data;

[0088] Step 4-4: For the three-dimensional array t v ∈R 128×1024×1024 Perform inverse fast Fourier transform to determine t(t v )(x 1 ,y 1 ,z 1 ):

[0089]

[0090] Step 4-5: Determine t(t v )(x 1 ,y 1 ,z 1 )∈R 128×1024×1024 The square of the absolute value of t(t v )(x 1 ,y 1 ,z 1 )∈R 128×1024×1024 The first half, that is, the data of the [64×512×512] part, obtains the final reconstructed volume data, and this new three-dimensional array is named v o ∈R 64×512×512, whose size is the same as the original data d a ∈R 64×512×512 Same, finally according to v o ∈R 64×512×512 Generate images along the x, y, and z axes to complete three-dimensional imaging.

[0091] This solution handles the time offset of data points by preprocessing and cropping the data, so that all photons arrive at the scanning surface at the same time, which is conducive to more accurate reconstruction of the position of hidden objects and enhances the signal-to-noise ratio; rearranging the data dimensions greatly simplifies the calculation of the subsequent Fourier transform. The use of linear interpolation method improves the imaging resolution, corrects possible geometric distortion, enhances the consistency of data, and improves the details of the final imaging to achieve the best imaging effect.

[0092] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation, characterized in that: The following steps are involved: Step 1: Obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ; Step 2, obtaining the size data and related parameter data of the target area; Step 3, processing the point time offset of the single-point cross-correlation waveform data of the target and performing data clipping; Step 4: Based on the Fourier high-dimensional transform imaging method, the processed data is subjected to fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations to generate a three-dimensional image.

2. The high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation according to claim 1 is characterized in that: The photon counting data m in step 1 e ∈R N×N×M The acquisition process is: Obtain single-point photon technology data, and calculate the single-point cross-correlation photon counting data x of the target based on the fast Fourier transform method e ∈R 1×M , based on the scanning galvanometer, the target is scanned by N rows and N columns to obtain N×N single-point cross-correlated photon counting data x e , finally forming the three-dimensional cross-correlation photon counting data m e ∈R N×N×M .

3. The high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation according to claim 1 is characterized in that: The size data of the target area and the parameter data of the detector in step 2 include: The size data of the target area w a : In a =L×W Among them, L and W represent the length and width of the target area respectively; The relevant parameter data includes the detector parameter data and the cropping index c r , where the detector parameter data includes the time interval resolution b i , speed of light c, spatial resolution parameter N∈R, and time resolution parameter M∈R.

4. The high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation according to claim 3 is characterized in that: The point time offset of the single-point cross-correlation waveform data of the target in step 3 is processed and data is clipped, specifically: Step 3-1, processing the point time offset of the single-point cross-correlation waveform data of the target so that all photons arrive at the scanning surface at the same time; Step 3-2: perform data trimming.

5. The high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation according to claim 4 is characterized in that: The point time offset of the single-point cross-correlation waveform data of the target in step 3-1 is processed, specifically: Step 3-1-1, extract matrix m e ∈R N×N×M All rows and m e ∈R N×N×M All columns of the matrix form a two-dimensional space array m e_1 ∈R N×N ; Step 3-1-2, get the two-dimensional space array m e_1 ∈R N×N The cross-correlated photon counting data at each point in t o ∈R N×N , the obtained cross-correlation photon counting data t o ∈R N×N Divide by the time interval resolution b of the detector i , get the two-dimensional space array m e_1 ∈R N×N The required displacement of each point in N×N ; And shift each point according to its own shift amount p.

6. The high-efficiency and high-resolution three-dimensional imaging method based on Fourier high-dimensional transform according to claim 4, characterized in that: The data clipping in step 3-2 is specifically as follows: Step 3-2-1, by extracting the matrix m e ∈R N×N×M The first c of the time dimension r elements, get the array Step 3-2-2: Rearrange the array Dimension order, the array The dimension order is adjusted to: time, Y axis, X axis, and get an array Step 3-2-3, create a vector with values ​​ranging from 0 to 1 and equally spaced, with a length of c r , and then copy the above vector into a three-dimensional matrix This matrix represents the distance of each voxel from the wall, where c r corresponds to the time dimension, and N corresponds to the space dimension.

7. The high-efficiency and high-resolution single-photon spread spectrum three-dimensional imaging method based on global frequency domain transformation according to claim 6 is characterized in that: The specific process in step 4 is as follows: Step 4-1: Array To process, where ⊙ represents the Hadamard product, and then creates a three-dimensional array of all zeros Finally, the processed data d a1 Fill to The first half of c r ×N×N, t d_a The rest of remains zero; Step 4-2: For a three-dimensional array Perform a 3D fast Fourier transform: in, Indicates the input signal t d_a The spectral components at the frequency index u, v, w, u, v, w are frequency indices corresponding to the frequency positions in the directions x1, y1, z1, respectively, and their value ranges are: u∈[0,2N-1], v∈[0,2N-1], w∈[0,2c r -1], whose index interval is x1, y1, z1 are spatial or temporal indices used to traverse the input data t d_a The value range of each element in is: x1∈[0,2N-1],y1∈[0,2N-1],z1∈[0,2c r -1]. Its index interval is is the element in the input data matrix, which contains the original time domain and spatial domain data points. is a complex exponential function, i is an imaginary unit; Step 4-3: Use the linear interpolation algorithm to calculate T(t d_a The z-axis direction data of )(u,v,w) is from the original frequency position, that is, wave number k z , mapped to the new frequency position k ′ z , and the y and x directions remain unchanged, that is: Where f is the frequency, k is x is the wave number on the x-axis, k y is the wave number on the y-axis, k z is the original wave number on the z-axis; If the new value to be interpolated exceeds the specified range of the known data point in dimension, 0 is returned. ′ z The original frequency data k of the x and y axes x ,k y Together they form a new three-dimensional array Its dimensions are The size of the array remains consistent. For the generated three-dimensional array Let the part where z=0 be 0, and only keep the part where z>0, that is, only keep the forward propagation data; Step 4-4: For a three-dimensional array Perform inverse fast Fourier transform to determine t(t v )(x1,y1,z1): Step 4-5: Confirm The square of the absolute value of , thereby determining the square of the amplitude of the target reconstructed volume data, extract The first half, namely [c r ×N×N] part of the data to obtain the final reconstructed volume data, and name this new three-dimensional array Its size and original data Same, finally according to Generate images along the x, y, and z axes to complete three-dimensional imaging.

8. An efficient and high-resolution single-photon spread spectrum three-dimensional imaging system based on global frequency domain transformation, characterized in that: Includes the following modules: Cross-correlation module: used to obtain the three-dimensional cross-correlation photon counting data m of the target to be imaged e ∈R N×N×M ; Data processing module: used to process the point time offset of the single-point cross-correlation waveform data of the target and perform data clipping; Three-dimensional imaging module: used to perform fast Fourier transform, linear interpolation, and inverse fast Fourier transform operations on the processed data based on the Fourier high-dimensional transform imaging method to generate a three-dimensional image.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer storable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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