A structured light detection system and method based on photon-level single-pixel wavefront imaging

Abstract

This invention provides a structured light detection system and method based on photon-level single-pixel wavefront imaging, belonging to the field of wavefront imaging technology. It solves the problem of low detection sensitivity faced by single-pixel wavefront imaging technology in extreme environmental communication. The system includes a structured light generation module, a beam transmission module, and a structured light detection module. The structured light generation module generates structured light carrying different topological charges. The structured light is transmitted over a long distance to the structured light detection module via the beam transmission module. The structured light detection module is based on the coaxial phase-shifting interferometry wavefront imaging principle and uses single-photon detection technology, digital micromirror devices, and Hadamard mask modulation technology to process the long-distance transmitted structured light signals. Under a long-distance transmission of 50m, this invention requires only an average of 30 photons per mask to complete the identification of the topological charge, achieving a structured light identification accuracy of over 96%. It is suitable for structured light detection under ultra-long distance and extremely weak light conditions.

Description

Technical Field

[0001] This invention provides a structured light detection system and method based on photon-level single-pixel wavefront imaging, belonging to the field of wavefront sensing and computational imaging technology. Background Technology

[0002] Structured light is a type of light field with a unique spatial structure distribution, constructed through specific optical modes and phase control. In recent years, structured light has demonstrated immense application potential in optical communication. By manipulating the properties of light waves, structured light can achieve efficient information transmission and precise measurement. For example, based on the manipulation of orbital angular momentum, structured light can achieve multi-channel information transmission, significantly improving communication capacity and security. With the increasing demand for precise measurement and efficient transmission, accurate detection of the effective information carried by structured light is particularly important. Traditional structured light detection techniques mainly include phase modulation, amplitude modulation, and polarization modulation. Phase modulation detection technology uses the phase information of light for encoding. This method requires precise interferometers, and its detection accuracy significantly decreases under unstable environmental conditions or with significant noise interference, making it difficult to maintain consistency and stability. Amplitude modulation is simple to operate, but due to its low signal-to-noise ratio, it is difficult to achieve high-precision detection under extremely weak light conditions. Polarization modulation has certain advantages in anti-interference, but it requires high precision and stability of the detection equipment and is easily affected by changes in the external environment. Therefore, traditional structured light detection technology faces significant challenges in complex transmission environments (extremely weak light, long-distance transmission, underwater turbulence, and scattering, etc.). Due to the difficulty in capturing enough signal photons, traditional detection systems struggle to maintain high signal-to-noise ratios and high accuracy.

[0003] In recent years, single-pixel wavefront imaging has emerged as a novel structured light detection technology. It utilizes a single detector to acquire high-quality wavefront image information through temporal or spatial encoding, effectively identifying key information carried by structured light and offering advantages such as system simplicity, high stability, and strong anti-interference capabilities. However, in extremely complex environments, its detection sensitivity is low due to limitations of photodetectors, resulting in limited detection accuracy for structured light. Single-photon detection technology, leveraging highly sensitive single-photon detectors, can detect extremely weak light signals, even the arrival of a single photon. While offering high detection sensitivity and accuracy, making it suitable for light signal detection in extreme environments, it cannot identify the effective information carried by structured light. Therefore, it is necessary to combine single-pixel wavefront imaging and single-photon detection technologies to develop a structured light detection system based on photon-level single-pixel wavefront imaging, achieving high-sensitivity detection of structured light under extremely weak light conditions. Summary of the Invention

[0004] To address the problem of low detection sensitivity in extreme environment communication using single-pixel wavefront imaging technology, this invention proposes a structured light detection system and method based on photon-level single-pixel wavefront imaging.

[0005] The technical solution adopted in this invention is as follows: a structured light detection system based on photon-level single-pixel wavefront imaging, comprising a structured light generation module, a beam transmission module, and a structured light detection module. The structured light generation module is used to generate structured light carrying different topological charges. The structured light is transmitted over a long distance to the structured light detection module through the beam transmission module. The structured light detection module is based on the coaxial phase-shifting interference wavefront imaging principle and processes the long-distance transmitted structured light signal by using single-photon detection technology, digital micromirror devices, and Hadamard mask modulation technology.

[0006] The structured light generation module includes a laser. The laser emitted by the laser is collimated and expanded by a beam expander, and then reflected by a mirror. The reflected light is polarized by a polarizer and becomes linearly polarized light. A half-wave plate is used to adjust the polarization direction of the beam so that the beam is aligned with the alignment direction of the liquid crystal molecules in the spatial light modulator. A first computer loads holograms with different topological charges onto the spatial light modulator to control the phase of the laser, thereby generating structured light carrying different topological charges. A 4f system consisting of a first convex lens, an aperture, and a second convex lens is used to select the first-order diffracted beam.

[0007] The structured light detection module includes a digital micromirror device (DMM), a single-photon detector, a time-correlated single-photon counter, and a second computer. A telescope focuses the structured light wavefront signal onto the DMM. A binary grating-Hadamard mask based on a checkerboard reference mode is loaded onto the DMM to modulate the wavefront signal, achieving coaxial phase-shifting interference of the structured light. A third convex lens performs a Fourier transform on the first-order diffracted light after interference. The zero-frequency component, filtered by a pinhole at the spectral plane, is detected by the single-photon detector. The time-correlated single-photon counter records the photon count per unit time for each mask of the single-photon detector and transmits it to the second computer. The second computer reconstructs the structured light wavefront image using a second-order correlation algorithm, improves the imaging quality through a phase denoising neural network, and identifies the topological charge number carried in the structured light using an orbital angular momentum recognition neural network.

[0008] The aperture r of the pinhole needs to meet the following conditions:

[0009]

[0010] In the above formula: λ is the wavelength of the light beam, f is the focal length of the third convex lens, and d is the diameter of the light beam.

[0011] The structured light generated by the structured light generation module is a Laguerre-Gaussian beam, a Bessel-Gaussian beam, or a Hermit-Gaussian beam carrying different topological charges.

[0012] A structured light detection method based on photon-level single-pixel wavefront imaging, employing a structured light detection system based on photon-level single-pixel wavefront imaging, includes the following steps:

[0013] Step 1: Generation and emission of structured light wavefront signals;

[0014] Step 2: The structured light wavefront signal is transmitted over a long distance in the beam transmission module;

[0015] Step 3: Wavefront signal reception and imaging process:

[0016] Step 3.1: Use a telescope to focus the structured light wavefront signal onto the digital micromirror device;

[0017] Step 3.2: A binary grating-Hadamard mask based on a checkerboard reference pattern is loaded onto the digital micromirror device to modulate the wavefront signal, thereby realizing coaxial phase-shifting interference of structured light;

[0018] The steps for generating a binary grating-Hadamard mask based on a checkerboard reference pattern on a digital micromirror device are as follows:

[0019] Step 3.2.1: Divide the mask into signal region and reference region in a checkerboard pattern, set the target wavefront imaging resolution to N×N, and use 2×2 checkerboard grids to form a superpixel. In the four-step phase-shifting method, each checkerboard grid corresponds to 4×4 digital micromirror device pixels, and the total size of the loaded mask corresponds to 8N×8N digital micromirror device pixels.

[0020] Step 3.2.2: Encode the wavefront signal in the signal region using a Hadamard mask. The number of Hadamard masks in the full sampling method is N. 2 Zhang, the total number of masks loaded using the four-step phase-shifting method is 4N. 2 open;

[0021] Step 3.2.3: Load binary gratings with a period of 4 pixels into both the checkerboard signal region and the reference region. The phase of the binary grating loaded into the corresponding signal region of each Hadamard mask is... Keeping the reference region constant, shifting the phase of the binary grating by 0, π / 2, π, and 3π / 2 respectively, yields the corresponding detection values.

[0022] Step 3.3: The first-order diffracted light after interference is subjected to Fourier transform using the third convex lens. The zero-frequency component filtered by the pinhole at the spectral plane is detected by the single-photon detector.

[0023] Step 3.4: Use a time-correlated single-photon counter to record the photon count of the single-photon detector per unit time on each mask and transmit it to the second computer;

[0024] Step 4: Photon-level single-pixel wavefront imaging process:

[0025] Step 4.1: The complex coefficients C can be calculated from the four-step phase shift. n :

[0026]

[0027] In the formula, n = 1, 2, 3…N 2 , represents the number of Hadamard masks; i represents the imaginary unit;

[0028] Step 4.2: Reconstruct the wavefront W(x,y) of the structured light using a second-order correlation algorithm:

[0029]

[0030] In the formula, H n (x,y) represents the nth Hadamard mask;

[0031] Step 4.3, calculate the phase of the structured light wavefront from the reconstructed wavefront W(x,y):

[0032]

[0033] Step 5: Image denoising and orbital angular momentum recognition process.

[0034] The specific implementation process of step 1 is as follows:

[0035] Step 1.1: The laser emitted by the laser is collimated and expanded by the beam expander, and after being reflected by the mirror, it is polarized into linearly polarized light by the polarizer.

[0036] Step 1.2: Use a half-wave plate to adjust the polarization direction of the beam to be consistent with the alignment direction of the liquid crystal molecules in the spatial light modulator;

[0037] Step 1.3: Using the first computer, load holograms with different topological charges onto the spatial light modulator to control the phase of the laser and generate structured light with different topological charges;

[0038] Step 1.4: Use the first convex lens, the aperture, and the second convex lens to form a 4f system to select the first-order diffraction beam.

[0039] The specific implementation process of step 5 is as follows:

[0040] Step 5.1: Denoise the wavefront phase image reconstructed based on the second-order correlation algorithm using a phase denoising neural network:

[0041]

[0042] In the formula, D is the phase denoising neural network, S is the denoised phase image, and θ D These are the parameters of the neural network;

[0043] Step 5.2: Use the orbital angular momentum recognition neural network to identify the topological charge number carried in the phase image:

[0044] (l,p)=R(S);

[0045] In the formula, R is the orbital angular momentum recognition neural network, S is the denoised phase image, l is the topological charge number, and p is the radial mode number.

[0046] The advantages of this invention over the prior art are as follows:

[0047] 1. This invention modulates the structured light wavefront signal using a binary grating-Hadamard mask based on a checkerboard reference mode, achieving coaxial phase-shifting interference of the wavefront signal. Combined with a second-order correlation algorithm, the wavefront image is reconstructed with high precision, enabling photon-level high-sensitivity structured light detection under extreme scattering environments and long-distance transmission of 50m.

[0048] 2. This invention employs a phase denoising neural network, which effectively removes noise introduced during transmission and significantly improves the image signal-to-noise ratio by more than 5dB;

[0049] 3. This invention uses an orbital angular momentum recognition neural network based on structured light prior information to accurately identify the topological charge in phase images. On average, only 30 photons are needed per mask to complete the identification of the topological charge, and the recognition accuracy can reach more than 96%, which significantly improves the ability to resolve the information carried in structured light. Attached Figure Description

[0050] The present invention will be further described below with reference to the accompanying drawings:

[0051] Figure 1 This is a system structure diagram of the present invention;

[0052] Figure 2 This is a flowchart of the binary grating-Hadamard mask construction method of the present invention;

[0053] Figure 3 The image shows the wavefront phase imaging results for different topological charge numbers according to the present invention.

[0054] Figure 4 The image shows the wavefront phase imaging results under different scattering conditions of this invention.

[0055] Figure 5This is a diagram of the phase denoising neural network structure of the present invention;

[0056] Figure 6 The diagram shows the phase denoising results of this invention under different average photon numbers;

[0057] Figure 7 This is a diagram of the orbital angular momentum recognition neural network structure of the present invention;

[0058] Figure 8 This is a line graph showing the topological charge number recognition accuracy of the present invention under different average photon numbers;

[0059] In the diagram: 1 is the structured light generation module, 2 is the beam transmission module, 3 is the structured light detection module, 101 is the laser, 102 is the beam expander, 103 is the mirror, 104 is the polarizer, 105 is the half-wave plate, 106 is the spatial light modulator, 1071 is the first convex lens, 1072 is the second convex lens, 108 is the aperture stop, 109 is the first computer, 301 is the telescope, 302 is the digital micromirror device, 303 is the third convex lens, 304 is the pinhole camera, 305 is the single-photon detector, 306 is the time-correlated single-photon counter, and 307 is the second computer. Detailed Implementation

[0060] like Figures 1 to 8 As shown, this invention provides a structured light detection system based on photon-level single-pixel wavefront imaging, including a structured light generation module 1, a beam transmission module 2, and a structured light detection module 3. The structured light generation module 1 is used to generate structured light carrying different topological charges. The structured light is transmitted over a long distance to the structured light detection module 3 through the beam transmission module 2. The structured light detection module 3 is based on the coaxial phase-shifting interference wavefront imaging principle and processes the long-distance transmitted structured light signal by using single-photon detection technology, digital micromirror devices, and Hadamard mask modulation technology.

[0061] Specifically, such as Figure 1 As shown, the structured light generation module 1 includes a laser 101. The laser emitted by the laser 101 is collimated and expanded by a beam expander 102, and then reflected by a mirror 103. The reflected light is polarized by a polarizer 104 and becomes linearly polarized light. A half-wave plate 105 is used to adjust the polarization direction of the beam so that it is consistent with the alignment direction of the liquid crystal molecules in the spatial light modulator 106. A first computer 109 loads holograms with different topological charges on the spatial light modulator 106 to control the phase of the laser and generate structured light carrying different topological charges. A 4f system consisting of a first convex lens 1071, an aperture 108, and a second convex lens 1072 is used to select the first-order diffracted beam.

[0062] The structured light can be a Laguerre-Gaussian beam, a Bessel-Gaussian beam, or a Hermit-Gaussian beam carrying different topological charges.

[0063] The structured light detection module 3 includes a digital micromirror device (DMM) 302, a single-photon detector (SPD) 305, a time-correlated single-photon counter (TRC) 306, and a second computer 307. A telescope 301 focuses the structured light wavefront signal onto the DMM 302. A binary grating-Hadamard mask based on a checkerboard reference mode is loaded onto the DMM 302 to modulate the wavefront signal, achieving coaxial phase-shifting interference of the structured light. A third convex lens 303 performs a Fourier transform on the first-order diffracted light after interference. The zero-frequency component filtered by a pinhole 304 at the spectral plane is detected by the SPDD 305. The TRC SPDD 306 records the photon count per unit time for each mask of the SPDD 305 and transmits it to the second computer 307. A second-order correlation algorithm is used to reconstruct the structured light wavefront image. Finally, a phase denoising neural network improves the imaging quality, and an orbital angular momentum recognition neural network identifies the topological charge carried in the structured light.

[0064] The aperture r of pinhole 304 needs to meet the following conditions:

[0065]

[0066] In the above formula: λ is the wavelength of the light beam, f is the focal length of the third convex lens, and d is the diameter of the light beam.

[0067] This invention proposes a structured light detection method based on photon-level single-pixel wavefront imaging, which includes the following steps based on the aforementioned detection system:

[0068] Step 1: Generation and emission of structured light wavefront signals:

[0069] Step 1.1: The laser emitted by the laser 101 is collimated and expanded by the beam expander 102, and after being reflected by the mirror 103, it is polarized into linearly polarized light by the polarizer 104.

[0070] Step 1.2: Use half-wave plate 105 to adjust the polarization direction of the beam to be consistent with the alignment direction of the liquid crystal molecules in spatial light modulator 106;

[0071] Step 1.3: Using the first computer 109, holograms with different topological charges are loaded onto the spatial light modulator 106 to control the phase of the laser and generate structured light with different topological charges.

[0072] Step 1.4: Use the first convex lens 1071, the aperture 108, and the second convex lens 1072 to form a 4f system to select the first-order diffraction beam.

[0073] Step 2: The structured light wavefront signal is transmitted over a long distance in beam transmission module 2.

[0074] Step 3: Wavefront signal reception and imaging process:

[0075] Step 3.1: Use telescope 301 to focus the structured light wavefront signal onto digital micromirror device 302;

[0076] Step 3.2: A binary grating-Hadamard mask based on a checkerboard reference pattern is loaded onto the digital micromirror device 302 to modulate the wavefront signal, thereby realizing coaxial phase-shifting interference of structured light;

[0077] The steps for generating a binary grating-Hadamard mask based on a checkerboard reference pattern on the digital micromirror device 302 are as follows:

[0078] Step 3.2.1: Divide the mask into signal region and reference region in a checkerboard pattern, set the target wavefront imaging resolution to N×N, and use 2×2 checkerboard grids to form a superpixel. In the four-step phase-shifting method, each checkerboard grid corresponds to 4×4 digital micromirror devices (302 pixels), and the total size of the loaded mask corresponds to 8N×8N digital micromirror devices (302 pixels).

[0079] In this embodiment, the target wavefront imaging resolution is set to 64×64, and a superpixel is formed by 2×2 checkerboard grids. In the four-step phase-shifting method, each checkerboard grid corresponds to 4×4 digital micromirror devices (302 pixels), and the total size of the loaded mask is 512×512 digital micromirror devices (302 pixels).

[0080] Step 3.2.2: Encode the wavefront signal in the signal region using a Hadamard mask. The number of Hadamard masks in the full sampling method is N. 2 Zhang, the total number of masks loaded using the four-step phase-shifting method is 4N. 2 open;

[0081] In this embodiment, the number of Hadamard masks in the full sampling method is 4096, and the total number of masks loaded by the four-step phase-shifting method is 16384.

[0082] Step 3.2.3: Load binary gratings with a period of 4 pixels into both the checkerboard signal region and the reference region. The phase of the binary grating loaded into the corresponding signal region of each Hadamard mask is... Keeping the reference region constant, shifting the phase of the binary grating by 0, π / 2, π, and 3π / 2 respectively, yields the corresponding detection values.

[0083] Step 3.3: The first-order diffracted light after interference is subjected to Fourier transform by the third convex lens 303, and the zero-frequency component filtered by the pinhole 304 at the spectral plane is detected by the single-photon detector 305.

[0084] Step 3.4: Use the time-correlated single-photon counter 306 to record the photon count of the single-photon detector 305 per unit time for each mask and transmit it to the second computer 306.

[0085] Step 4: Photon-level single-pixel wavefront imaging process:

[0086] Step 4.1: The complex coefficients C can be calculated from the four-step phase shift. n :

[0087]

[0088] In the formula, n = 1, 2, 3…N 2 , represents the number of Hadamard masks; i represents the imaginary unit;

[0089] Step 4.2: Reconstruct the wavefront W(x,y) of the structured light using a second-order correlation algorithm:

[0090]

[0091] In the formula, H n (x,y) represents the nth Hadamard mask;

[0092] Step 4.3, calculate the phase of the structured light wavefront from the reconstructed wavefront W(x,y):

[0093]

[0094] Step 5: Image denoising and orbital angular momentum recognition process;

[0095] Step 5.1: Denoise the wavefront phase image reconstructed based on the second-order correlation algorithm using a phase denoising neural network:

[0096]

[0097] In the formula, D is the phase denoising neural network, S is the denoised phase image, and θ DThe parameters of the neural network are defined here. The phase denoising neural network is designed based on the U-Net framework and incorporates a masked self-attention mechanism. Masked self-attention uses a mask to prevent each location from selecting subsequent input information. The encoder part of the network extracts features from the input phase image through a multi-layer convolutional neural network and gradually reduces the spatial dimension through downsampling. A masked self-attention module is added after each convolutional layer to specifically focus on regions in the phase image that undergo drastic changes, identifying pixels that may contain noise, thereby improving the denoising effect. The decoder part restores the image resolution through upsampling and passes fine-grained feature information to the corresponding encoder layer through skip connections, ensuring that the spatial information of the image is preserved. The network finally outputs a denoised phase image, and its loss function uses mean squared error (MSE). By minimizing the difference between the denoised image and the original image, it ensures effective noise removal while preserving the detailed phase features.

[0098] Step 5.2: Use the orbital angular momentum recognition neural network to identify the topological charge number carried in the phase image:

[0099] (l,p)=R(S);

[0100] In the formula, R represents the orbital angular momentum recognition neural network, S represents the denoised phase image, l represents the topological charge number, and p represents the radial pattern number. The structure of the orbital angular momentum recognition neural network is optimized based on the traditional convolutional neural network (CNN), focusing on recognizing the topological charge number carried by structured light. This network extracts features from the complex ripple structure in the input phase image through multiple deep convolutional layers, with specific optimizations for periodic changes and topological structures in the image. To improve the ability to capture both local and global features, the network incorporates multi-scale convolutional operations, ensuring the simultaneous extraction of local details and global trends in the phase image. Furthermore, a global average pooling layer is introduced in the network design to reduce the risk of overfitting and improve classification robustness. To enhance the network's performance when handling complex topological charges, a weighted cross-entropy loss function is used. By assigning weights to different classes during training, the class imbalance problem is effectively balanced, improving classification accuracy. Experimental results show that the network can still maintain a topological charge number recognition accuracy of over 96% even under low signal intensity conditions with an average of only 30 photons per mask. It has strong noise resistance and robustness, and is especially suitable for structured light signal analysis and classification in extremely low light environments.

[0101] The effectiveness of this invention will be verified through experiments below:

[0102] To verify the effectiveness of the invention, the performance of the system under different conditions was tested. First, Laguerre-Gaussian structured light holograms with different topological charges were loaded onto the spatial light modulator 106 in an atmospheric environment, with orbital angular momentum set to +1 to +7, respectively. Then, a set of binary grating-Hadamard masks was loaded onto the digital micromirror device 302 to modulate the structured light wavefront signal. The modulated signal was detected by a single-photon detector 305, and the wavefront image was reconstructed using a second-order correlation algorithm. Figure 3 Phase reconstruction images of Laguerre-Gaussian beams under different topological charge numbers are shown. It can be observed that the system can accurately reconstruct wavefront phase information.

[0103] By varying the water turbidity to 0.1 NTU, 5 NTU, 20 NTU, 40 NTU, 60 NTU, and 90 NTU, the transmission of structured light under different scattering underwater environments was simulated. Figure 4 As shown, when structured light encounters scattering and obstruction interference during transmission, significant noise appears in the reconstructed wavefront phase image, and the noise intensity increases with the degree of scattering.

[0104] To effectively remove this noise, this system proposes a phase denoising neural network. For example... Figure 5 As shown, this neural network utilizes prior information about the phase of structured light to suppress noise. Figure 6 As shown, by calculating the peak signal-to-noise ratio (PSNR), under the condition of an average of 15 photons per mask, the PSNR before denoising was 9.06 dB, and the PSNR after denoising was 15.84 dB, resulting in an image quality improvement of 6.78 dB; under the condition of an average of 30 photons per mask, the PSNR before denoising was 14.28 dB, and the PSNR after denoising was 20.26 dB, resulting in an image quality improvement of 6.34 dB; under the condition of an average of 50 photons per mask, the PSNR before denoising was 16.38 dB, and the PSNR after denoising was 22.53 dB, resulting in an image quality improvement of 6.15 dB. In summary, after processing with the phase denoising neural network, the image quality is improved by an average of more than 5 dB. Experimental results show that the phase denoising neural network of this invention significantly reduces noise interference in images.

[0105] To verify the system's ability to identify orbital angular momentum, we conducted experiments with different average photon numbers. For example... Figure 7 As shown, an orbital angular momentum recognition neural network is used to analyze the reconstructed wavefront signal and identify the topological charge carried in the structured light. Experimental results demonstrate that the recognition neural network can stably and accurately identify the orbital angular momentum information carried in the structured light under different conditions. Figure 8As shown in Table 1, the accuracy rate of orbital angular momentum recognition is 84.3% when the average number of photons per mask is 15; when the average number of photons per mask is 30, the accuracy rate exceeds 96%, reaching a satisfactory level sufficient for most practical applications; when the average number of photons per mask is 50 and 200, the accuracy rates are 97.5% and 98.1%, respectively, further improving the recognition accuracy; and when the average number of photons per mask is 1000, the accuracy rate reaches 99.6%, demonstrating excellent recognition performance. This invention possesses highly sensitive and robust orbital angular momentum recognition capabilities, providing an effective solution for analyzing and recognizing structured light field features in complex environments.

[0106] Average photon number 15 30 50 200 1000 Correct identification 4215 4807 4877 4905 4981 Identification error 785 193 123 95 19 Recognition accuracy 84.3％ 96.1％ 97.5％ 98.1％ 99.6％

[0107] Table 1 shows the accuracy of topological charge number recognition under different average photon numbers according to the present invention.

[0108] The detection system proposed in this invention is based on the coaxial phase-shifting interferometry wavefront imaging principle. By employing single-photon detection technology, it significantly improves detection capabilities in extremely low-light environments. Utilizing digital micromirror devices and Hadamard mask modulation technology, it effectively processes structured light signals transmitted over long distances. Combining phase denoising neural networks and orbital angular momentum recognition neural networks not only enhances the system's anti-interference capability in complex environments but also improves the accuracy and stability of structured light detection. This system provides a new approach to addressing the shortcomings of existing structured light detection technologies and has broad application prospects.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A structured light detection system based on photon-level single-pixel wavefront imaging, characterized in that: The system includes a structured light generation module (1), a beam transmission module (2), and a structured light detection module (3). The structured light generation module (1) is used to generate structured light carrying different topological charges. The structured light is transmitted over a long distance to the structured light detection module (3) through the beam transmission module (2). The structured light detection module (3) is based on the coaxial phase-shifting interference wavefront imaging principle. It uses single-photon detection technology, digital micromirror device (302), and Hadamard mask modulation technology to process the structured light signal transmitted over a long distance. The structured light detection module (3) includes a digital micromirror device (302), a single-photon detector (305), a time-correlated single-photon counter (306), and a second computer (307). A telescope (301) focuses the structured light wavefront signal onto the digital micromirror device (302). A binary grating-Hadamard mask based on a checkerboard reference mode is loaded onto the digital micromirror device (302) to modulate the wavefront signal, achieving coaxial phase-shifting interference of the structured light. A third convex lens (303) is used to filter the first-order diffracted light after interference. A Fourier transform is performed, and the zero-frequency component filtered by the pinhole (304) at the spectral plane is detected by the single-photon detector (305). The photon count of each mask of the single-photon detector (305) per unit time is recorded by the time-correlated single-photon counter (306) and transmitted to the second computer (307). The second computer (307) reconstructs the structured light wavefront image using a second-order correlation algorithm, improves the imaging quality by using a phase denoising neural network, and uses an orbital angular momentum recognition neural network to identify the topological charge number carried in the structured light.

2. The structured light detection system based on photon-level single-pixel wavefront imaging according to claim 1, characterized in that: The structured light generation module (1) includes a laser (101). The laser emitted by the laser (101) is collimated and expanded by a beam expander (102), and then reflected by a mirror (103). The reflected light is polarized by a polarizer (104) and becomes linearly polarized. A half-wave plate (105) is used to adjust the polarization direction of the beam so that the beam is aligned with the arrangement direction of the liquid crystal molecules in the spatial light modulator (106). A first computer (109) loads holograms with different topological charges on the spatial light modulator (106) to control the phase of the laser and generate structured light carrying different topological charges. A 4f system consisting of a first convex lens (1071), an aperture (108), and a second convex lens (1072) is used to select the first-order diffracted beam.

3. The structured light detection system based on photon-level single-pixel wavefront imaging according to claim 1, characterized in that: The aperture of the pinhole (304) r The following conditions must be met: ； In the above formula: λ Where is the wavelength of the light beam. f Let be the focal length of the third convex lens. d Let be the diameter of the beam.

4. A structured light detection system based on photon-level single-pixel wavefront imaging according to claim 2, characterized in that: The structured light generated by the structured light generation module (1) is a Laguerre-Gaussian beam, a Bessel-Gaussian beam, or a Hermit-Gaussian beam carrying different topological charges.

5. A structured light detection method based on photon-level single-pixel wavefront imaging, characterized in that: The structured light detection system based on photonic-level single-pixel wavefront imaging as described in any one of claims 1-4 includes the following steps: Step 1: Generation and emission of structured light wavefront signals; Step 2: The structured light wavefront signal is transmitted over a long distance in the beam transmission module (2); Step 3: Wavefront signal reception and imaging process: Step 3.1: Use a telescope (301) to focus the structured light wavefront signal onto a digital micromirror device (302). Step 3.2: A binary grating-Hadamard mask based on a checkerboard reference pattern is loaded onto the digital micromirror device (302) to modulate the wavefront signal and realize coaxial phase-shifting interference of structured light; The steps for generating a binary grating-Hadamard mask based on a checkerboard reference pattern on the digital micromirror device (302) are as follows: Step 3.2.1: Divide the mask into signal area and reference area in a checkerboard pattern, set the target wavefront imaging resolution to N×N, and use 2×2 checkerboard grids to form a superpixel. In the four-step phase shifting method, each checkerboard grid corresponds to 4×4 digital micromirror device (302) pixels, and the total size of the loaded mask corresponds to 8N×8N digital micromirror device (302) pixels. Step 3.2.2: Encode the wavefront signal in the signal region using a Hadamard mask. The number of Hadamard masks in the full sampling method is N. 2 Zhang, the total number of masks loaded using the four-step phase-shifting method is 4N. 2 open; Step 3.2.3: Load binary gratings with a period of 4 pixels into both the checkerboard signal region and the reference region. The phase φ of the binary grating loaded into the corresponding signal region of each Hadamard mask is... r Keeping the reference region constant, shifting the phase of the binary grating by 0, π / 2, π, and 3π / 2 respectively, yields the corresponding detection values. , , , ; Step 3.3: The first-order diffracted light after interference is subjected to Fourier transform using the third convex lens (303), and the zero-frequency component filtered by the pinhole (304) at the spectral plane is detected by the single-photon detector (305). Step 3.4: Use a time-correlated single-photon counter (306) to record the photon count of the single-photon detector (305) per unit time on each mask and transmit it to the second computer (307). Step 4: Photon-level single-pixel wavefront imaging process: Step 4.1: The complex coefficients can be calculated using the four-step phase shift. C n : ； In the formula, n =1,2,3⋯ N 2 , indicating the number of Hadamard masks; i Represents the imaginary unit; Step 4.2: Reconstruct the wavefront of the structured light using a second-order correlation algorithm. W ( x,y ): ； In the formula, H n ( x,y ) indicates the first n Zhang Hadama mask; Step 4.3, from the reconstructed wavefront W ( x,y The phase of the structured light wavefront is calculated as follows: ； Step 5: Image denoising and orbital angular momentum recognition process.

6. The structured light detection method based on photon-level single-pixel wavefront imaging according to claim 5, characterized in that: The specific implementation process of step 1 is as follows: Step 1.1: The laser emitted by the laser (101) is collimated and expanded by the beam expander (102), and after being reflected by the mirror (103), it is polarized into linearly polarized light by the polarizer (104); Step 1.2: Use a half-wave plate (105) to adjust the polarization direction of the beam to be consistent with the arrangement direction of the liquid crystal molecules in the spatial light modulator (106); Step 1.3: Using the first computer (109), load holograms with different topological charges onto the spatial light modulator (106) to control the phase of the laser and generate structured light with different topological charges; Step 1.4: Use the first convex lens (1071), the aperture (108), and the second convex lens (1072) to form a 4f system to select the first-order diffraction beam.

7. The structured light detection method based on photon-level single-pixel wavefront imaging according to claim 5, characterized in that: The specific implementation process of step 5 is as follows: Step 5.1: Denoise the wavefront phase image reconstructed based on the second-order correlation algorithm using a phase denoising neural network: ； In the formula, D For phase denoising neural networks, S This is the denoised phase image. θ D These are the parameters of the neural network; Step 5.2: Use the orbital angular momentum recognition neural network to identify the topological charge number carried in the phase image: ( l,p )= R ( S )； In the formula, R A neural network for identifying orbital angular momentum. S This is the denoised phase image. l For topological load number, p This represents the radial mode number.

Images