High-angular-resolution wavefront measurement system and method based on random structure diffraction

Abstract

The invention discloses a high-angular-resolution wavefront measurement method and system based on random structure diffraction, and relates to the technical field of optical information measurement. According to the wavefront measurement method, phase modulation of incident light is realized by using a disordered diffractive optical element (DOE) in random binary phase distribution, and complex incident wavefront coding is realized through a Fresnel diffraction effect. The mapping relation between the diffraction pattern and the incident angle is further pre-calibrated through an experiment, and the wavefront to be measured is reconstructed in combination with a wavefront reconstruction algorithm. According to the scheme provided by the invention, the mapping relation between the DOE diffraction pattern and the incident angle is pre-calibrated through an experiment, accurate decoding of a local incident angle can be realized, a regional wavefront reconstruction algorithm is further combined, wavefront measurement with a large dynamic range, high spatial resolution and high angular resolution is satisfied, and the measurement precision is improved. And the method has higher reconstruction precision and real-time performance and better anti-noise capability.

Description

Technical Field

[0001] The present invention relates to the technical field of optical information measurement, and in particular to a high-angle resolution wavefront measurement system and method based on random structure diffraction. Background Art

[0002] Wavefront sensors can directly measure the wavefront information of incident light and are widely used in optical metrology, adaptive optics and other fields. Currently, there are two main methods for measuring wavefronts: interferometry and angle measurement. Among them, the interferometry method achieves high-precision spatial and angular resolution wavefront reconstruction by vertically scanning multiple frames of interference patterns. However, this method relies on continuous data acquisition, resulting in low temporal resolution. In addition, the bulky optical system and poor environmental robustness limit its further application in in-situ characterization.

[0003] In comparison, angle-based wavefront sensing methods offer excellent system stability and integration. For example, the Shack-Hartmann wavefront sensor (SHWFS) uses the local phase gradient value at the focal point of a microlens array to recover the wavefront by integrating it. However, its angular and spatial resolutions are limited by the inherent size of the microlens array. Wavefront sensing can also be achieved through the memory effect of speckle, which exploits the spatial correlation of speckle generated at different angles of incidence to reconstruct the wavefront. However, this method relies on the simple translation of speckle under local wavefront tilt, and its field of view is limited by the memory effect distance.

[0004] In recent years, through the precise design and preparation of subwavelength microstructures, light field manipulation technology has been able to precisely control the phase, amplitude, and polarization state of light, making significant progress in high-spatial-resolution wavefront sensing technology based on metasurfaces. For example, wavefront coding technology, metasurface shearing interferometry technology, superlens Shack-Hartmann sensors, and phase recovery methods based on metalens TIEs have been developed. Among them, wavefront imaging technology based on planar optical wavefront coding can significantly improve the dynamic range and spatial resolution. However, this solution faces challenges in manufacturing and calibration. Although angle-dependent filters can be inserted into the imaging optical path to achieve wavefront coding, which can greatly reduce the experimental difficulty, they cannot be directly integrated into the camera surface. Since the wavefront resolution of angle-coded wavefront imaging technology depends on the slope of the device's angular response curve, the current technology has a low slope of the angular response curve, making it challenging to achieve high-wavefront resolution imaging. Summary of the Invention

[0005] In response to the above-mentioned problems existing in the prior art, the present application provides a high-angular resolution wavefront measurement system and method based on random structure diffraction, which can simultaneously meet the wavefront measurement requirements of large dynamic range, high spatial resolution and high angular resolution.

[0006] In one aspect, the present invention provides a high angular resolution wavefront measurement method based on random structure diffraction, the method comprising the following steps:

[0007] S01, making the wavefront incident on the surface of a random diffractive optical element (DOE) with a random binary phase distribution, and encoding the wavefront information in the diffracted light field through random phase modulation;

[0008] S02, propagating the modulated wavefront to the plane of a CMOS image sensor (CIS), which collects a diffraction intensity image containing angle information;

[0009] S03, establishing macropixels, analyzing the distribution of macropixel intensities at different incident angles, establishing a mapping relationship between macropixel intensities and incident angles through calibration, and obtaining calibration data;

[0010] S04, obtaining a diffracted light intensity image of the wavefront to be measured after being modulated by a disordered diffractive optical element (DOE) and collected by a CMOS image sensor, and uniquely determining the incident angle of the wavefront to be measured using a retrieval algorithm;

[0011] S05 , calculating the local phase gradient of the wavefront to be measured according to the determined incident angle using a regional wavefront reconstruction method, and reconstructing the complete phase distribution of the wavefront to be measured based on the local phase gradient.

[0012] In another aspect, the present invention provides a high angular resolution wavefront measurement system based on random structure diffraction, the system comprising:

[0013] The wavefront sensing module includes a random phase modulation encoding module and a diffraction intensity acquisition unit. The random phase modulation encoding module uses the random phase of the surface of the disordered diffractive optical element (DOE) to encode the wavefront of the incident light beam; the diffraction intensity acquisition unit uses a CMOS image sensor (CIS) to record the diffraction intensity image containing angle information of the modulated wavefront.

[0014] The pre-calibration module uses a dual-stage that rotates along the incident angle to perform calibration, establishes an intensity mapping relationship between macro-pixels and incident angles, and obtains calibration data;

[0015] A retrieval module, which uses a retrieval algorithm to uniquely determine the incident angle of the wavefront to be measured in the calibration data according to the image of the diffracted light intensity to be measured corresponding to the wavefront to be measured;

[0016] The reconstruction module uses the reconstruction algorithm to calculate the local phase gradient of the wavefront to be measured, and reconstructs the complete phase distribution of the wavefront to be measured based on the local phase gradient

[0017] The beneficial effects of the present invention are:

[0018] The present invention provides a high-angular resolution wavefront measurement system and method based on random structure diffraction. By experimentally pre-calibrating the mapping relationship between the DOE diffraction pattern and the incident angle, accurate decoding of the local incident angle can be achieved. At the same time, it can be further combined with the regional wavefront reconstruction algorithm to meet the wavefront measurement requirements of large dynamic range, high spatial resolution and high angular resolution, and has higher reconstruction accuracy and real-time performance as well as better anti-noise ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the wavefront measurement method provided by the present invention.

[0020] Figure 2 It is a schematic diagram of the incident wavefront modulation process provided by the present invention.

[0021] Figure 3 It is a schematic diagram of the wavefront to be measured of the initial incident light provided by the present invention.

[0022] Figure 4 This is a schematic diagram of the partial structural distribution of the DOE element provided by the present invention under a 50× microscope.

[0023] Figure 5 It is a schematic diagram of a diffraction intensity image recorded by a CMOS image sensor provided by the present invention.

[0024] Figure 6 It is a schematic diagram of the corresponding relationship between the diffraction intensity image and the incident angle provided by the present invention.

[0025] Figure 7 It is a schematic diagram of the angle response curve provided by the present invention.

[0026] Figure 8 The present invention provides a schematic diagram of the θ angle distribution obtained by querying a calibration database.

[0027] Figure 9 This is a schematic diagram of the wavefront distribution of the reconstruction and restoration provided by the present invention.

[0028] Figure 10 This is a schematic diagram of the electric field distribution results at different angles simulated by angular spectrum theory provided by the present invention.

[0029] Figure 11 It is a schematic diagram of the calibration results provided by the present invention. DETAILED DESCRIPTION

[0030] To help those skilled in the art better understand the technical solutions of the present invention, the following provides a clear and complete description of the technical solutions of the present invention, in conjunction with the accompanying drawings. Based on the embodiments of this application, other similar embodiments obtained by those skilled in the art without inventive effort should fall within the scope of protection of this application.

[0031] Example 1

[0032] like Figure 1 This paper presents a high-angular-resolution wavefront measurement method based on random structure diffraction. This method utilizes a random diffractive optical element (DOE) with a random binary phase distribution to achieve phase modulation of the incident light and implements complex encoding of the incident wavefront through the Fresnel diffraction effect. Furthermore, the mapping relationship between the diffraction pattern and angle is pre-calibrated experimentally, and the wavefront to be measured is reconstructed using a wavefront reconstruction algorithm. Specifically, the wavefront measurement method includes the following steps:

[0033] In step S01 , a wavefront is incident on a surface of a random diffractive optical element (DOE) with a random binary phase distribution, and wavefront information is encoded in the diffracted light field through random phase modulation.

[0034] like Figure 2 As shown in Figure 2, when a beam of incoherent light with a wavelength of 633 nm is irradiated vertically along the normal direction, the phase distortion caused by the sample to be measured is carried by the wavefront of the beam, as shown in Figure 2. Figure 3 The distorted light beam and the disturbed wavefront are then incident on the surface of a random diffractive optical element (DOE), where the random phase of the surface encodes the light beam wavefront.

[0035] In a preferred embodiment, the random diffractive optical element (DOE) used is a two-dimensional phase distribution grating with a size of 5×5 mm.

[0036] In order to obtain the phase distribution of the disordered diffractive optical element (DOE), in this embodiment, an optical microscope is used to detect the edge information in the grayscale image to analyze the geometric characteristics of the disordered diffractive optical element (DOE). The grayscale image of the partial structure distribution obtained is as follows: Figure 4 As shown, the random phase distribution of the disordered diffractive optical element (DOE) is calculated using the optical path difference equation As shown in formula (1):

[0037]

[0038] The complex amplitude distribution U0(x,y) of the incident parallel light is expressed as:

[0039]

[0040] The light beam is incident on the surface of the random diffractive optical element (DOE) and then passes through the random phase distribution on the surface of the random diffractive optical element (DOE). The beam wavefront is randomly phase modulated, and the wavefront information is encoded in the diffracted light field to obtain the modulated wavefront U d (x,y) is as shown in formula (3):

[0041]

[0042] Compared with the pinhole diffraction and multilayer film structures in the existing technology, random phase modulation based on disordered diffractive optical elements (DOEs) can greatly improve the complexity of the diffraction pattern, making the system have a complex angular response curve, thereby improving the angular resolution of wavefront sensing.

[0043] Step S02: Modulate the wavefront U d (x,y) is propagated to the plane of the CMOS image sensor (CIS) according to scalar diffraction theory, and the CMOS image sensor records the diffraction intensity pattern containing angular information.

[0044] According to the scalar diffraction theory, the incident light beam is captured by the CMOS image sensor (CIS) when it is modulated by the disordered diffractive optical element (DOE) and propagates to the plane of the CMOS image sensor (CIS), as shown in Figure 2 shown.

[0045] When the modulated light beam propagates to the CMOS image sensor (CIS) plane, it is only necessary to transform the angular spectrum A d (f x ,f y ) and the phase transfer function H(f x ,f y ) can be combined to obtain the angular spectrum A1(f x ,f y ), as shown in formula (4).

[0046] A1(f x ,f y )=A d (f x ,f y )·H(f x ,f y ) (4)

[0047] The complex amplitude distribution U1(x,y) of the corresponding output light field can be obtained by Fourier inversion, as shown in formula (5):

[0048]

[0049] The corresponding diffraction intensity pattern I totalIt can be obtained by light intensity superposition according to the characteristics of incoherent light sources, as shown in formula (6):

[0050]

[0051] The diffraction pattern recorded by the CMOS image sensor (CIS) is as follows Figure 5 As shown in Figure 2, it can be seen that the wave vector direction of the incident light beam determines the diffraction intensity pattern recorded by the CMOS image sensor (CIS). This characteristic provides a theoretical basis for wavefront detection based on near-field diffraction coding.

[0052] Step S03 , establishing macro-pixels, analyzing the distribution of macro-pixel intensities at different incident angles, establishing a mapping relationship between macro-pixel intensities and incident angles by calibration, and adapting to arbitrary wavefront angle modulation characteristics.

[0053] The incident angle includes an inclination angle θ and an azimuth angle ρ.

[0054] Because the diffraction intensity map recorded by the CMOS image sensor (CIS) in the previous step has a one-to-one correspondence with the incident angle of the incident light beam (including the tilt angle θ and the azimuth angle ρ), a corresponding intensity relationship can be established between the macropixel and different incident angles. Therefore, this embodiment further calibrates the diffraction intensity in two incident angle directions on a macropixel basis, thereby establishing an intensity mapping relationship between the macropixel and the incident angle.

[0055] In order to calculate the tilt angle θ and azimuth angle ρ parameters, it is necessary to first divide and confirm a macro pixel. In this embodiment, 3×3 pixels are preferably used as a macro pixel. By increasing the number of receiving pixels, the resolution of incident light information can be significantly improved. At the same time, the angular spectrum theory can be used to quantitatively analyze the macro pixels.

[0056] Since one pixel is only 2.9 microns, the macro-pixel size constructed in this embodiment is 8.7μm×8.7μm, which is smaller than the 10.4μm macro-pixel size used in the prior art. Compared with the existing solution using a 2×2 pixel structure, the improved 3×3 structure in this embodiment provides more characteristic information of the light intensity distribution, thereby effectively improving the numerical stability and calculation accuracy of the angle search algorithm.

[0057] Taking an incident angle of 0°-20° as a preferred embodiment, first calculate the diffraction complex amplitude U1(x,y) of the incident light U0(x,y) with an incident angle in the range of 0°-20° after being modulated by the disordered diffractive optical element (DOE) and mapped on the CMOS image sensor (CIS) plane according to steps S01-S02. Then, the light intensities are superimposed to obtain a diffraction intensity map corresponding to an incident angle of 0°-20°.

[0058] Furthermore, the calibration process utilizes a collimated LED light source, primarily using a top rotating platform and a bottom rotating stage that rotate along the tilt angle θ and azimuth angle ρ, respectively. The top rotating platform is mounted with a random diffractive optical element (DOE) and a CMOS image sensor (CIS). The top rotating platform moves the camera along the azimuth angle ρ, while the bottom rotating platform moves the camera along the tilt angle θ, enabling calibration across the entire hemispherical coordinate system.

[0059] As a preferred embodiment, the calibration range is θ∈[0°, 20°], ρ∈[0°, 360°], with a step size of Δθ=0.25 and Δρ=2°. Five raw image signals are recorded for each angle, and the average of each image signal is calculated. The calibration method used in this embodiment is very efficient, ensuring that the angle sensors on the CMOS image sensor chip can be calibrated in one go using the motorized translation stage.

[0060] Therefore, the diffraction intensity image obtained by the CMOS image sensor (CIS) can be mapped one-to-one with the tilt angle θ and azimuth angle ρ within the incident angle range of 0°-20°. By comparing the pixels at the same position under the light intensity graph at different angles, the angle response curve can be obtained, such as Figure 7 shown.

[0061] In step S04, the diffraction intensity image of the wavefront to be measured is obtained after being modulated by the disordered diffractive optical element (DOE) and recorded by the CMOS image sensor (CIS), and the incident angle of the wavefront to be measured, including the tilt angle θ and the azimuth angle ρ, is uniquely determined based on the pixel intensity difference corresponding to the wavefront to be measured using a retrieval algorithm.

[0062] After obtaining the calibration data of the diffraction intensity corresponding to all angles through the above steps, the angle index can be searched in the calibration data using the table lookup method after obtaining the measured diffraction intensity data corresponding to the measured wavefront, thereby quickly restoring the angle value corresponding to the measured diffraction intensity image.

[0063] Specifically, the search algorithm primarily traverses each pixel of the diffraction intensity image to be measured, performing high-resolution interpolation on the calibration data for the current pixel and its eight surrounding points to generate a ratio difference distribution map covering the angular space. In a preferred embodiment, the search step size is 3, corresponding to the macropixel size.

[0064] The ratio difference distribution is calculated as shown in formula (7):

[0065] P0=(p1-p2) / (p1+p2) (7)

[0066] Among them, p1 and p2 represent the light intensity of any two pixels in the macro pixel, and there are 36 possible combinations of 9 pixels.

[0067] The adjacent ratios of the actual pixel values ​​are then compared with the calibration data to select matching regions within a tolerance range (e.g., error ≤ 0.001). The maximum number of matches is then counted to determine the corresponding tilt angle θ and azimuth angle ρ. Outliers are then filtered using the quartile method. The final selected angle values ​​are then adjusted for boundaries (e.g., ensuring continuity between 0° and 360°) and converted to Cartesian coordinates for the tilt angle θ and azimuth angle ρ, which represent the incident angle of the wavefront to be measured.

[0068] By querying the calibration database, we can get the angle distribution diagram of the tilt angle θ. Figure 8 The corresponding azimuth angle ρ is calculated in the same way as the tilt angle θ.

[0069] Step S05 , using the Southwell regional wavefront reconstruction method to calculate the local phase gradient of the wavefront to be measured, and reconstructing the complete phase distribution of the wavefront to be measured based on the local phase gradient.

[0070] Based on the local angular distribution obtained by the above steps, in this embodiment, the Southwell regional wavefront reconstruction method is preferably used to solve the local phase gradient, and finally the local phase gradient is integrated by the least squares method to reconstruct the complete phase distribution of the measured wavefront.

[0071] Specifically, when the local incident angle is known, the local phase gradient of the incident light field can be calculated according to the generalized Snell's law, as shown in formula (8):

[0072]

[0073] in, and θ i (i=x,y) represents the local phase gradient and the local incident tilt angle in the x-direction and y-direction, respectively, k=2πn / λ, where λ is the wavelength.

[0074] After obtaining the local phase gradient of the incident light field, the wavefront can be reconstructed using the finite difference based least squares integration (FLI) method. The representation method on the CMOS image sensor (CIS) pixel is shown in formula (9):

[0075]

[0076] Where x and y represent the horizontal and vertical distributions of the macropixels on the image sensor, respectively. The subscripts i and j correspond to the x- and y-coordinate positions of the macropixels on the image sensor, respectively. N and M are the number of macropixel sampling points in the x and y directions, respectively.

[0077] In the Southwell configuration, the relationship between the local wavefront and the local phase gradient can be expressed as:

[0078]

[0079] Where d is the macro pixel size, and are all N×M local phase gradient matrices, Then the phase value to be solved corresponds to the N×M phase gradient matrix. Let and Expressed as S, the central difference relationship can be used to construct the The linear equations for :

[0080]

[0081] where A is a sparse system of equations with constant coefficients. is the phase to be measured.

[0082] The reconstructed wavefront is the phase to be measured The least squares QR decomposition method can be used to solve it:

[0083]

[0084] Finally, the wavefront distribution restored using the Southwell wavefront reconstruction method is as follows Figure 9 shown.

[0085] Example 2

[0086] After completing the aforementioned wavefront detection, it is preferred to further conduct an effective evaluation of the wavefront detection method based on the random structure diffractive optical element proposed in this embodiment.

[0087] Since the effect of spectral restoration mainly depends on the spectral correlation between sensor pixels, the correlation between pixels after spectral encoding is further evaluated, including the correlation between the diffraction intensity pattern distribution generated by the plane wave irradiating the disordered diffractive optical element (DOE) and the tilt angle θ. Therefore, this embodiment simulates the electric field distribution at different angles through the angular spectrum theory, such as Figure 10 shown.

[0088] Figure 10 (a) shows a beam of incoherent parallel light with a wavelength of 633 nm incident on the wavefront sensing system. Note that because the incident light LED is an incoherent light source, the physical model here is calculated using the incoherent light angular spectrum theory, which simulates the superposition of different point source intensities.

[0089] Figure 10(b) The first row shows the electric field distribution in the XZ plane under different incident angles. A rich diffraction pattern is observed under normal incidence due to random phase modulation. When the tilt angle θ is 10°, the diffraction pattern differs significantly from that at a tilt angle of θ of 0°, demonstrating that the randomized diffractive optical element (DOE) modulates the incident light wavefront.

[0090] Figure 10 (b) The second row shows the diffraction intensity distribution captured by the CMOS image sensor (CIS) in the XY plane when the tilt angle θ is 0° and 10°, where the corresponding pixel size is 2.9 μm. The sensitivity intensity I of each pixel is s It is obtained by integrating the complex amplitude of the output light field in the pixel area.

[0091] It can be found that after phase modulation by the disordered diffractive optical element (DOE), the intensity distribution of the CMOS image sensor (CIS) shows a high degree of randomness, which further proves that the random phase modulation method can achieve effective wavefront coding.

[0092] In addition, in order to quantitatively analyze the coding quality of the angle response curve, this embodiment also uses the Pearson correlation coefficient (Cor) to calculate the correlation between pixels within the macro pixel:

[0093]

[0094] Among them Cor (i,j) Represents the correlation between the angular response curves of the i-th pixel and the j-th pixel.

[0095] After calculation, the correlation coefficient matrix of the angle response curve within a single macro pixel is as follows: Figure 10 (c) shows that the correlation coefficient matrix distribution shows that the angular response curves between pixels within the macro pixel have low similarity, and the average correlation coefficient is 0.49.

[0096] In order to illustrate the angular dependence of the diffraction pattern in the CMOS image sensor (CIS), this embodiment further calculates the angular response curves of 9 macro pixels under different incident angles (0°-20°), as shown in FIG. Figure 10 As shown in (d), it is not difficult to see that at different tilt angles θ, there are significant differences in light intensity between sensor pixels. Similarly, at different azimuth angles ρ, there are also significant differences in light intensity between sensor pixels. Therefore, this intensity difference can be used for angle retrieval.

[0097] In theory, the angular resolution of a wavefront sensing system is given by Definition, its value mainly depends on the slope of the angle response curve The slope of the angle response curve of 9 macro pixels is calculated in this embodiment. The calculation results show that within a single macro pixel, the average slope of the angle response curve is 0.17, as shown in Figure 10 (e) Therefore, for an 8-bit CMOS image sensor (CIS), the angular resolution Δθ is 401 μrad, which can be further improved by using a high-performance, high-bit CMOS camera.

[0098] It can be seen that the average slope of the random phase angle response curve obtained by the improved method in the embodiment of the present invention is improved by one order of magnitude compared with the linear angle response curve.

[0099] Example 3

[0100] In this preferred embodiment, a high angular resolution wavefront measurement system based on random structure diffraction is further provided.

[0101] The wavefront measurement system comprises:

[0102] The wavefront sensing module includes a random phase modulation encoding module and a diffraction intensity acquisition unit. The random phase modulation encoding module uses the random phase of the surface of the disordered diffractive optical element (DOE) to encode the wavefront of the incident light beam; the diffraction intensity acquisition unit uses a CMOS image sensor (CIS) to record the diffraction intensity image containing angle information of the modulated wavefront.

[0103] The pre-calibration module uses a dual-stage that rotates along the tilt angle θ and the azimuth angle ρ to perform calibration, establish a mapping relationship between macro-pixel intensity and incident angle, and obtain calibration data;

[0104] A retrieval module, which uses a retrieval algorithm to uniquely determine the incident angle of the wavefront to be measured in the calibration data according to the image of the diffracted light intensity to be measured corresponding to the wavefront to be measured;

[0105] The reconstruction module calculates the local phase gradient of the wavefront to be measured using a reconstruction algorithm, and reconstructs the complete phase distribution of the wavefront to be measured based on the local phase gradient.

[0106] The random phase modulation encoding module is composed of a random diffractive optical element (DOE). As a preferred embodiment, the random diffractive optical element (DOE) is provided by Laser Land, and the surface of the random diffractive optical element (DOE) is a random binary phase structure with a phase range of approximately [0-0.5π].

[0107] The diffraction intensity acquisition module is a CMOS image sensor (CIS). In a preferred embodiment, the CMOS image sensor (CIS) is Sony IMX290, wherein the size of each pixel is P=2.9 μm.

[0108] In a preferred embodiment, in order to increase the integration of the wavefront sensing module, a random diffractive optical element (DOE) is directly integrated on the pixel array surface of a CMOS image sensor (CIS).

[0109] During fabrication, the protective cover glass layer on the CMOS image sensor (CIS) is first removed through a high-temperature thermal stripping process. The pixel edges of the CIS are then used as alignment reference points to align the 5x5mm random diffractive optical element (DOE) with the pixel edges of the CIS. The DOE is then encapsulated using UV adhesive.

[0110] In order to obtain the angular response curve of the wavefront sensing module, the pre-calibration module first calibrates the wavefront sensing module. First, a 633nm LED light source is collimated by a 10× beam expander. After collimation, the parallel light is incident on the CMOS image sensor (CIS). The CMOS image sensor (CIS) is calibrated by adjusting the angles ρ∈(0, 360°) and θ∈(0-20°) according to the calibration method in the previous embodiment.

[0111] Figure 11 (a) and Figure 11 (b) shows the intensity maps of two pixels A and C at tilt angle θ and azimuth angle ρ in the polar coordinate system, obtained through experimental calibration. Each point on the calibration hemisphere represents a combination of tilt angle θ and azimuth angle ρ. This constructs a specific mapping between pixel intensity and incident angle.

[0112] like Figure 11 (c) shows the angular response curve of the light intensity of a single pixel with the azimuth angle ρ when θ is 5° and 10°. Figure 11 (d) shows the dependence of pixel light intensity on tilt angle θ when the azimuth angle ρ is fixed at 0° and 180°. It can be seen that the angular response curve exhibits complex characteristics with the incident angle, which greatly improves reconstruction accuracy and angular resolution. Note that the angular response extreme value contrast of the wavefront sensing module provided in this embodiment can reach 1:3. A larger contrast ratio can improve noise immunity.

[0113] Furthermore, the accuracy of the angle search is verified. Figure 11 (a) Extract the angle contour lines to form the angle search grid, such as Figure 11(e) For any incident light, it is necessary to calculate intensity ratios, defined as etc. Therefore, it is possible to determine a specific incident angle by combining multiple intensity ratios, such as Figure 11 The only intersection point is shown in (e). Figure 11 (f) shows a search graph for determining the incident angle (θ = 11°, ρ = 135°) using a combination of intensity ratios. It can be seen that the extreme values ​​of the intersections of the intensity ratios are concentrated within the very small range marked by the red circle, demonstrating that the search error is smaller than the angle step size corresponding to a single pixel.

[0114] In summary, the present invention provides a high-angular-resolution wavefront measurement method and system based on random structure diffraction, which can simultaneously meet the requirements of a large dynamic range, high spatial resolution, and high angular resolution. By experimentally pre-calibrating the mapping relationship between the DOE diffraction pattern and the incident angle, the device can accurately decode the local incident angle. This is further combined with a regional wavefront reconstruction algorithm to reconstruct the wavefront, thereby simultaneously meeting the requirements of a large dynamic range, high spatial resolution, and high angular resolution. By experimentally pre-calibrating the mapping relationship between the DOE diffraction pattern and the incident angle, the device can accurately decode the local incident angle. This is further combined with a regional wavefront reconstruction algorithm to reconstruct the wavefront.

[0115] The present invention has been described in detail above. The above description is only a preferred embodiment of the present invention and should not limit the scope of implementation of the present invention. That is, all equivalent changes and modifications made within the scope of this application should still fall within the scope of the present invention.

Claims

1. A high angular resolution wavefront measurement method based on random structure diffraction, characterized in that: The method comprises: S01, making the wavefront incident on the surface of a random diffractive optical element (DOE) with a random binary phase distribution, and encoding the wavefront information in the diffracted light field through random phase modulation; S02, propagating the modulated wavefront to the plane of a CMOS image sensor (CIS), which collects a diffraction intensity image containing angle information; S03, establishing macropixels, analyzing the distribution of macropixel intensities at different incident angles, establishing a mapping relationship between macropixel intensities and incident angles through calibration, and obtaining calibration data; S04, obtaining a diffracted light intensity image of the wavefront to be measured after being modulated by a disordered diffractive optical element (DOE) and collected by a CMOS image sensor, and uniquely determining the incident angle of the wavefront to be measured using a retrieval algorithm; S05 , calculating the local phase gradient of the wavefront to be measured according to the determined incident angle using a regional wavefront reconstruction method, and reconstructing the complete phase distribution of the wavefront to be measured based on the local phase gradient.

2. The high angular resolution wavefront measurement method based on random structure diffraction according to claim 1, characterized in that: The incident angle includes an inclination angle θ and an azimuth angle ρ.

3. The high angular resolution wavefront measurement method based on random structure diffraction according to claim 1, characterized in that: In step S01, the random phase distribution of the disordered diffractive optical element (DOE) As shown in formula (1): The complex amplitude distribution U0(x,y) of the incident parallel light is expressed as: The modulated wavefront U d (x,y) is as shown in formula (3):

4. The high angular resolution wavefront measurement method based on random structure diffraction according to claim 1, characterized in that: In step S02, when the modulated light beam propagates to the CMOS image sensor (CIS) plane, the angular spectrum A1 (f x ,f y ) is shown in formula (4): A1(f x ,f y )=A d (f x ,f y )·H(f x ,f y ) (4) Among them, A d (f x ,f y ) is the angular spectrum, H(f x ,f y ) is the phase transfer function; The corresponding complex amplitude distribution U1(x,y) of the output light field is: Diffraction intensity image I total Obtained by light intensity superposition, as shown in formula (6):

5. The high angular resolution wavefront measurement method based on random structure diffraction according to claim 1, characterized in that: A macro pixel is 3×3 pixels.

6. The high angular resolution wavefront measurement method based on random structure diffraction according to claim 1, characterized in that: In step S03 , the diffraction intensity image obtained by the CMOS image sensor (CIS) is calibrated to correspond one-to-one with the tilt angle θ and the azimuth angle ρ within the incident angle range, thereby obtaining calibration data including the intensity mapping relationship between the macropixel and the incident angle.

7. The high angular resolution wavefront measurement method based on random structure diffraction according to claim 1, characterized in that: In step S04, the method of uniquely determining the incident angle of the wavefront to be measured by using a search algorithm specifically includes: First, each pixel of the diffraction intensity image to be measured is traversed, and the calibration data of the current pixel and its surrounding 8 points are interpolated at high resolution to generate a ratio difference distribution map covering the angle space; Then, the adjacent ratio of the actual pixel value is compared with the calibration data, the matching area within the error range is screened out, the maximum number of matches is counted to determine the corresponding tilt angle θ and azimuth angle ρ, and the abnormal values ​​are filtered out; After adjusting the boundary continuity, the screened angle value is converted into the tilt angle θ and azimuth angle ρ in Cartesian coordinates, which is the incident angle corresponding to the wavefront to be measured.

8. A high angular resolution wavefront measurement system based on random structure diffraction, characterized in that: The system comprises: The wavefront sensing module includes a random phase modulation encoding module unit and a diffraction intensity acquisition unit. The random phase modulation encoding module uses the random phase of the surface of the disordered diffractive optical element (DOE) to encode the wavefront of the incident light beam. The diffraction intensity acquisition unit uses a CMOS image sensor (CIS) to record the diffraction intensity image containing angle information of the modulated wavefront. The pre-calibration module uses a dual-stage that rotates along the incident angle to perform calibration, establishes an intensity mapping relationship between macro-pixels and incident angles, and obtains calibration data; A retrieval module, which uses a retrieval algorithm to uniquely determine the incident angle of the wavefront to be measured in the calibration data according to the image of the diffracted light intensity to be measured corresponding to the wavefront to be measured; The reconstruction module calculates the local phase gradient of the wavefront to be measured using a reconstruction algorithm, and reconstructs the complete phase distribution of the wavefront to be measured based on the local phase gradient.

9. The high angular resolution wavefront measurement system based on random structure diffraction according to claim 8, characterized in that: The wavefront sensing module is obtained by integrating a disordered diffractive optical element (DOE) on the pixel array surface of a CMOS image sensor (CIS).

10. The high angular resolution wavefront measurement system based on random structure diffraction according to claim 8, characterized in that: The dual translation stage used for calibration in the pre-calibration module consists of a top rotating platform and a bottom rotating translation stage that rotate along the tilt angle θ and azimuth angle ρ, respectively. The wavefront sensor module is installed on the top rotating platform. The top rotating platform moves the camera along the azimuth angle ρ, and the bottom rotating platform moves the camera along the tilt angle θ, so that calibration can be performed in the entire hemispherical coordinate system.

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