A method and system for extending the dynamic range of a Shaker-Hartmann wavefront sensor

Abstract

This invention discloses a method and system for extending the dynamic range of a Hartmann wavefront sensor, comprising: acquiring a large-offset spot array and a reference spot array of the Hartmann wavefront sensor; demodulating the large-offset spot to obtain wrapping slope information; unfolding the wrapping slope information using an unwrapping algorithm; and reconstructing the unfolded slope using a modified Fourier transform reconstruction algorithm to extend the dynamic range of the Hartmann wavefront sensor and achieve quantitative detection of the phase of the wavefront to be measured. This slope-based unwrapping method for extending the dynamic range of a Hartmann wavefront sensor is more convenient, more efficient, and has a shorter processing time, and is more likely to be commercially viable. After calibration and correction of the Fourier transform reconstruction algorithm, quantitative phase detection can be achieved, which will promote the application of Hartmann wavefront sensors in the detection of surface shapes of high-curvature optical elements and the detection of aberrations in high-refractive-index human eyes.

Description

Technical Field

[0001] This invention belongs to the field of optical wavefront sensor technology, specifically relating to a method and system for extending the large dynamic range of a Shaker-Hartmann wavefront sensor. Background Technology

[0002] The dynamic range of a Shaker-Hartmann wavefront sensor characterizes its ability to measure the size of a distorted wavefront and is determined by the ratio of the maximum permissible spot offset to the focal length of the microlens. In traditional wavefront sensors, the spot of each microlens must be within its corresponding sub-aperture range, so the maximum permissible offset is half the side length of the sub-aperture minus the spot radius, indicating that the dynamic range of a Hartmann wavefront sensor is limited. However, in many applications, such as the measurement of the surface shape of large-curvature optical elements and the measurement of high-refractive-index aberrations in the human eye, a large dynamic range while maintaining high accuracy is required for the Shaker-Hartmann wavefront sensor.

[0003] Currently, methods for increasing the dynamic range of Shakhartmann wavefront sensors can be broadly categorized into two types: software-based and hardware-based methods. Hardware-based methods require designing sub-lenses or adding components to the Shakhartmann wavefront sensor system. For example, different directions of astigmatism can be introduced into each sub-lens to aid in identification using the spot shape, or an aperture can be added that only allows light to pass through a portion of the sub-lens at a time to confirm the correspondence between the spot and the sub-aperture. Software-based methods primarily use computational methods to establish the relationship between the microlens array and the spot array of the Shakhartmann wavefront sensor. Common methods include iterative spline fitting, iterative extrapolation, pre-ordered spot localization, and deep learning. Because these methods do not require specially designed mechanical equipment to modulate the microlens array of the Shakhartmann wavefront sensor, they offer greater flexibility and lower cost. The essence of these methods is to minimize the deviation of the spot from the corresponding sub-aperture range or to reposition and reorder any spots that have deviated. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and system for extending the dynamic range of a Shaker-Hartmann wavefront sensor. This method enables quantitative detection of the phase of the wavefront under test and effectively extends the dynamic range of the Hartmann wavefront sensor.

[0005] To achieve the above objectives, the present invention employs the following technical solution:

[0006] The first aspect of this invention is to provide a method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor, comprising:

[0007] S1, acquire the large offset spot array and reference spot array of the Hartmann wavefront sensor;

[0008] S2, demodulate the large offset spot to obtain the wrapping slope information;

[0009] S3, uses an unpacking algorithm to expand the package slope information;

[0010] S4. The slope after expansion is reconstructed using a modified Fourier transform reconstruction algorithm to expand the dynamic range of the Hartmann wavefront sensor and realize the quantitative detection of the phase of the wavefront to be measured.

[0011] As a further improvement to the present invention, obtaining the large offset spot array and reference spot array of the Hartmann wavefront sensor includes:

[0012] A large-offset beam pattern array and a reference beam pattern array are acquired using a Hartmann wavefront sensor. The offsets of the offset beam pattern and the reference beam pattern along the x-axis and y-axis are calculated respectively. Based on the relationship between the magnitude of the offset and the focal length f, the slope distribution is obtained.

[0013]

[0014] Where i and j represent the row and column of the two-dimensional sub-aperture array, respectively. This represents the offset along the y-axis, where f is the focal length of the microlens. This represents the slope distribution along the y-axis. By demodulating each spot, the slope distribution of all sub-apertures can be obtained.

[0015] As a further improvement of the present invention, for sub-aperture spots that exceed the corresponding detection window, a direct spot demodulation method or a Fourier transform method is used to demodulate large offset spots.

[0016] As a further improvement of the present invention, the large offset spot is demodulated to obtain the wrapping slope information, including:

[0017] When obtaining the slope distribution at each location Then, calculate the slope of the package:

[0018]

[0019] Where P represents the sub-aperture spacing, when the offset magnitude satisfy The slope within the detection window will not exhibit any wrapping phenomenon; the slope range is between (-π, π), and the magnitude of the slope will be determined by... The ratio determines the value;

[0020] When the offset satisfies When the light spot deviates outside the sub-aperture range, the slope corresponding to the detection window will be wrapped with respect to π, denoted as .

[0021] As a further improvement of the present invention, a Fourier transform is performed on the large offset spot array, which will present a multi-level spectrum in the frequency domain. All non-zero level spectra are moved to the origin and the spectrum is superimposed. A suitable filtering window is set and the remaining spectrum is filtered out. Finally, an inverse Fourier transform is performed to demodulate and obtain the wrapping slope, which is wrapped in the range of (-π, π).

[0022] As a further improvement to this invention, an unwrapping algorithm is used to expand the slope information of the package. The slope unwrapping is expressed as:

[0023]

[0024] Here, Unwrap[] represents the unwrap algorithm, and the slope information after unwrap in the y-direction is represented by G. y express.

[0025] As a further improvement of the present invention, the wavefront reconstruction algorithm includes a modified Fourier transform reconstruction algorithm, a deep learning-based wavefront reconstruction algorithm, a pattern method, or a region method.

[0026] As a further improvement to this invention, the slope after expansion is reconstructed using a wavefront restoration algorithm, including:

[0027] The least squares unwrapping algorithm based on discrete cosine transform is used to expand the wrapped slope. First, the differences between adjacent slopes in the x and y directions of the continuous slope and the wrapped slope are obtained and the difference operation is performed. The loss function is constructed by the least squares algorithm and its first-order partial derivative is calculated. The discrete Poisson equation is obtained by rearranging it. After setting the corresponding boundary conditions, the continuous slope distribution can be obtained by performing discrete cosine transform and inverse discrete cosine transform. The slope unwrapping algorithm mainly expands the slope wrapped in the range of (-π, π) to obtain the continuously distributed slope information.

[0028] As a further improvement to this invention, the slope after expansion is reconstructed using a modified Fourier transform reconstruction algorithm, including:

[0029] First, Fourier transforms are performed on the slopes of the expanded values ​​in the x and y directions respectively, and then the two values ​​are linearly combined in the frequency domain. Next, an inverse Fourier transform is performed, and the real part of the complex function is taken to obtain the wavefront phase information. The reconstructed wavefront phase is multiplied by the system calibration factor to obtain the accurate reconstructed phase, thus achieving quantitative phase detection.

[0030] As a further improvement of the present invention, after unwrapping the wrapping slopes in the x and y directions, the expansion slopes G in the x and y directions are obtained respectively. x With G yThe phase of the wavefront under test is reconstructed using a Fourier transform reconstruction algorithm. This Fourier transform algorithm is then calibrated and corrected. The corrected Fourier transform reconstruction algorithm is expressed as follows:

[0031]

[0032] Where u = x / M, v = y / N, and Re{} denotes the operation of taking the real part of the complex function. and Let represent the Fourier transform and the inverse Fourier transform, respectively; ω is the system calibration correction factor; and W(x,y) represents the reconstructed wavefront phase.

[0033] A second aspect of the present invention is to provide a large dynamic range extension system for a Shaker-Hartmann wavefront sensor, comprising:

[0034] The acquisition module is used to acquire the large offset spot array and the reference spot array of the Hartmann wavefront sensor;

[0035] The demodulation module is used to demodulate large offset light spots to obtain the wrapping slope information;

[0036] The unfolding module is used to unfold the slope information of the package using an unwrapping algorithm;

[0037] The reconstruction module is used to reconstruct the expanded slope using a modified Fourier transform reconstruction algorithm, in order to expand the dynamic range of the Hartmann wavefront sensor and realize the quantitative detection of the phase of the wavefront to be measured.

[0038] Compared with existing technologies, this invention has the following advantages: This method eliminates the need to rearrange the large-offset spot array, instead directly demodulating it. The resulting slope exhibits a wrapping phenomenon; by utilizing an unwrapping algorithm and Fourier transform reconstruction, quantitative detection of the wavefront phase can be achieved. This method effectively extends the dynamic range of Hartmann wavefront sensors. Specific advantages are as follows:

[0039] 1) Unlike current methods that restrict or reposition large-offset light spots, the proposed method directly demodulates the large-offset light spot array. Since the light spot exceeds the sub-aperture range, causing slope encapsulation, simply unencapsulating the slope expands the dynamic range. 2) This method is more convenient, more efficient, has shorter processing time, and is more likely to be commercially viable. 3) Calibration and correction of the Fourier transform reconstruction algorithm enables quantitative phase detection, which will advance the application of Hartmann wavefront sensors in the detection of surface shapes of high-curvature optical elements and the detection of aberrations in highly refractive human eyes. Attached Figure Description

[0040] Figure 1This is a schematic diagram of the theoretical structure of the light spot offset and slope wrapping provided in the embodiment of the present invention, wherein (a) is a schematic diagram of the offset light spot array, and (b) is a schematic diagram of the wrapping slope corresponding to the light spot;

[0041] Figure 2 The simulation data of the Hartmann wavefront sensor provided in the embodiments of the present invention includes (a) a reference spot array, (b) a large offset spot array, (c) the wrapping slope in the y direction, and (d) the center wrapping slope profile in the vertical direction.

[0042] Figure 3 The wavefront phase reconstruction results based on the modified Fourier transform method provided in the embodiments of the present invention include: (a) simulated wavefront phase to be measured; (b) slope information after expansion by the least squares unwrapping algorithm; (c) wavefront phase directly reconstructed by the wrapping slope; and (d) reconstruction results of the expanded slope by the modified Fourier transform method.

[0043] Figure 4 The present invention provides a flowchart of a method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor.

[0044] Figure 5 This invention provides a large dynamic range extension system for a Shaker-Hartmann wavefront sensor;

[0045] Figure 6 This is a schematic diagram of an electronic device provided by the present invention. Detailed Implementation

[0046] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0047] Obviously, the described embodiments are only some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0048] It should be noted that the terminals involved in the embodiments of this application may include, but are not limited to, mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers, personal computers (PCs), MP3 players, MP4 players, wearable devices (e.g., smart glasses, smartwatches, smart bracelets), smart home devices, and other smart devices.

[0049] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0050] like Figure 4 As shown, this invention provides a method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor, comprising:

[0051] S1, acquire the large offset spot array and reference spot array of the Hartmann wavefront sensor;

[0052] S2, demodulate the large offset spot to obtain the wrapping slope information;

[0053] S3, uses an unpacking algorithm to expand the package slope information;

[0054] S4. The slope after expansion is reconstructed using a modified Fourier transform reconstruction algorithm to expand the dynamic range of the Hartmann wavefront sensor and realize the quantitative detection of the phase of the wavefront to be measured.

[0055] This invention acquires a large-offset spot array and a reference spot array from a Hartmann wavefront sensor. The large-offset spot is demodulated using a direct demodulation method to obtain the wrapping slope information. The wrapping slope is expanded using an unwrapping algorithm, and a modified Fourier transform reconstruction algorithm is applied to the expanded slope to extend the dynamic range of the Hartmann wavefront sensor and achieve quantitative detection of the phase of the wavefront under test. This slope-based unwrapping method for extending the dynamic range of the Hartmann wavefront sensor is more convenient, more efficient, and has a shorter processing time, making it more likely to be commercially viable. After calibration and correction of the Fourier transform reconstruction algorithm, quantitative phase detection can be achieved, which will advance the application of Hartmann wavefront sensors in the detection of surface shapes of high-curvature optical elements and the detection of aberrations in highly refractive human eyes.

[0056] For example, in step S1, the large offset spot array is caused by the large phase curvature of the wavefront to be measured, which causes the spot to overflow the corresponding sub-aperture range. For the sub-aperture spots that exceed the corresponding detection window, these large offset spots are allowed to stick to or even overlap with other nearby spots.

[0057] For example, in step S2, a Fourier transform is performed on the large offset spot array, which will present a multi-level spectrum in the frequency domain. All non-zero level spectra are moved to the origin and the spectrum is superimposed. An appropriate filter window is set and the remaining spectrum is filtered out. Finally, an inverse Fourier transform is performed to demodulate and obtain the wrapping slope, which is wrapped in the range of (-π, π).

[0058] For example, in step S3, a least squares unwrapping algorithm based on discrete cosine transform is used to expand the wrapped slope. First, the differences between adjacent slopes in the x and y directions of the continuous slope and the wrapped slope are obtained and differential operations are performed. A loss function is constructed using the least squares algorithm, and its first-order partial derivative is calculated. This yields the discrete Poisson equation. After setting the corresponding boundary conditions, a discrete cosine transform and inverse discrete cosine transform are performed to obtain a continuous slope distribution. The slope unwrapping algorithm mainly expands the slopes wrapped within the range of (-π, π) to obtain continuously distributed slope information.

[0059] For example, in step S4, firstly, Fourier transforms are performed on the slopes after expansion in the x and y directions respectively, and then the two are linearly combined in the frequency domain. Next, an inverse Fourier transform is performed, and the real part of the complex function is taken to obtain the wavefront phase information. The reconstructed wavefront phase is multiplied by the system calibration factor to obtain the accurate reconstructed phase, thus realizing quantitative phase detection.

[0060] The present invention will now be described in further detail with reference to the accompanying drawings, including the following steps:

[0061] Step 1: Acquire a large-offset beam pattern array and a reference beam pattern array using a Hartmann wavefront sensor, respectively. (See...) Figure 1 This is a schematic diagram of the slope wrapping theory structure in this embodiment, wherein, Figure 1 (a) is a schematic diagram of the offset light spot array. Figure 1 (b) is a schematic diagram of the corresponding package slope. Figure 1 In the large offset spot array shown in (a), the central cross of the sub-aperture indicates the location of the reference spot, while spot 3 exceeds the corresponding sub-aperture range. The offsets of the offset spot and the reference spot on the x-axis and y-axis are calculated respectively. Based on the relationship between the magnitude of the offset and the focal length f, the slope distribution can be obtained:

[0062]

[0063] Where i and j represent the row and column of the two-dimensional sub-aperture array, respectively. This represents the offset along the y-axis, where f is the focal length of the microlens. This represents the slope distribution along the y-axis. Demodulating each of the above light spots yields the slope distribution for all sub-apertures.

[0064] Furthermore, for sub-aperture spots that exceed the corresponding detection window, these spots will adhere to or even overlap with other nearby spots, thus forming a large centroid detection error. The traditional centroid method is difficult to demodulate them. The direct spot demodulation method or the Fourier transform method can be used to effectively demodulate large offset spots.

[0065] Step 2: Obtain the slope distribution at each location Next, determine whether the slope introduces a wrapping phenomenon due to a large offset. The wrapping slope can be calculated using the following formula:

[0066]

[0067] Where P represents the sub-aperture spacing, when the offset magnitude satisfy At this point, the slope within the detection window will not exhibit a wrap-around phenomenon, and the slope range will be between (-π, π). The magnitude of the slope will be determined by... The ratio determines this. For example... Figure 1 The light spots 1, 2, 4, 5, and 6 are shown in the image. Figure 1 The magnitude of the slope corresponding to (b) and Figure 1 The offsets in (a) are proportional. For example, the offset of spot 1 is 0, therefore the slope is 0. The direction of the spot offset determines the sign of the slope; for instance, the offset of spot 2 is downwards. Figure 1 (b) The slope of the corresponding point is less than 0; the offset of spot 4 and spot 5 is upward, and the corresponding slope is greater than 0.

[0068] Furthermore, when the offset satisfies When the light spot deviates outside the sub-aperture range, the slope corresponding to the detection window will be wrapped with respect to π, denoted as... like Figure 1 As shown in spot 3 in (a), its offset exceeds the sub-aperture range, therefore it will be truncated at -π, i.e., the truncation position is as follows: Figure 1 (b) at point 3′.

[0069] Furthermore, the specific cutoff point of the slope is determined by the direction of the offset. Figure 1 In (a), if the offset of spot 3 is downward, it will be truncated at -π. Conversely, if the offset is upward, the slope will be truncated at +π.

[0070] Furthermore, since spot 3 is outside the range of its sub-aperture, the slope is truncated here. This results in spot 1, spot 2, and spot 3 being located in the first-level wrapping slope, while spot 4 and spot 5 are located in the second-level wrapping slope. This represents the wrapping slope in the y-direction after demodulation of the entire offset spot.

[0071] Furthermore, this embodiment discloses a simulation example of a Hartmann wavefront sensor, see [link to relevant documentation]. Figure 2 ,in, Figure 2 (a) is a reference spot array. Figure 2 (b) is a large offset spot array, and the enveloping slope obtained after demodulation using the direct spot demodulation method is as follows: Figure 2 As shown in (c) Figure 2 (d) gives the wrapping slope of the vertical center profile. This simulation example shows that a large offset spot array will cause the demodulated slope to be wrapped in the range of (-π, π).

[0072] Step 3: Use the unwrapping algorithm to expand the slope of the package. The slope unwrapping can be expressed by the following formula:

[0073]

[0074] Here, Unwrap[] represents the unwrapping algorithm, and any unwrapping algorithm can meet the requirements, such as path tracing algorithms (specifically including row-to-column point-by-point unwrapping algorithms, branch-cutting algorithms, quality graph-guided path tracing algorithms, region growing algorithms, Flynn algorithms, and diamond algorithms), minimum norm methods (including unweighted least squares methods, weighted least squares methods, etc.), and various deep learning-based phase unwrapping algorithms. The slope information after unwrapping in the y-direction is represented by G. y Indicates, such as Figure 3 As shown in (b), the results show that the slope range is expanded after processing by the unwrapping algorithm.

[0075] Step 4: After simultaneously performing the above unwrapping process on the wrapping slopes in the x and y directions, the expansion slopes G in the x and y directions are obtained respectively. x With G y The phase of the wavefront to be measured can be reconstructed using the Fourier transform reconstruction algorithm. To achieve quantitative phase detection, the Fourier transform algorithm needs to be calibrated and corrected. The corrected Fourier transform reconstruction algorithm can be expressed as:

[0076]

[0077] Where u = x / M, v = y / N. Re{} denotes the operation of taking the real part of a complex function. and Let represent the Fourier transform and the inverse Fourier transform, respectively; ω is the system calibration correction factor; and W(x,y) represents the reconstructed wavefront phase.

[0078] Furthermore, this embodiment also provides for Figure 2 (c) shows the wrapping slope after wavefront phase reconstruction. See [link to relevant documentation]. Figure 3 ,in, Figure 3 (a) is a simulation of the wavefront phase to be measured. Figure 3 (b) shows the slope information after the least squares unwrapping algorithm is expanded. Figure 3 (c) Wavefront phase reconstructed directly from the wrapping slope Figure 3 (d) is the reconstruction result of the extended slope using the modified Fourier transform method.

[0079] Furthermore, on Figure 2 (c) The slope of the wrapping shown is reconstructed using the Fourier transform method. The reconstructed phase will exhibit severe wavefront aliasing, such as... Figure 3 As shown in (c), the reconstructed wavefront and Figure 3 The simulated wavefronts in (a) have very different phases.

[0080] Furthermore, Figure 3 (d) presents the reconstructed wavefront phase after slope unwrapping and using the modified Fourier transform method. It shows that for slope wrapping caused by large offset spot arrays, slope unwrapping can avoid wavefront aliasing. Furthermore, after calibration and correction of the Fourier transform reconstruction algorithm, the wavefront phase can be accurately reconstructed, such as... Figure 3 As shown in (d). Comparison Figure 3 (c) The results show that the slope unwrapping algorithm and modified Fourier transform reconstruction method disclosed in this invention can effectively extend the dynamic range of the Hartmann wavefront sensor.

[0081] Furthermore, this requirement is not limited to modified Fourier transform reconstruction algorithms; any wavefront reconstruction algorithm, such as deep learning-based wavefront reconstruction algorithms, pattern methods, and region methods, will satisfy this requirement. Since the unwrapping algorithm extends the slope information, these wavefront reconstruction algorithms need to be calibrated to achieve quantitative phase detection.

[0082] This invention does not reposition or restrict the large offset spot, but directly utilizes the wrapping slope introduced by the large offset. The slope unwrapping algorithm disclosed in this invention can effectively expand the wrapping slope. It also discloses a modified Fourier transform reconstruction algorithm, which can achieve quantitative detection of the phase of the wavefront under large curvature. The solution disclosed in this invention will effectively expand the dynamic range of the Shackleton-Hartmann wavefront sensor, which will further promote its application in fields such as surface shape detection of large curvature optical elements and wavefront aberration detection of high-refractive-index eyes.

[0083] like Figure 5 As shown, the second objective of this invention is to provide a large dynamic range extension system for a Shaker-Hartmann wavefront sensor, comprising:

[0084] The acquisition module is used to acquire the large offset spot array and the reference spot array of the Hartmann wavefront sensor;

[0085] The demodulation module is used to demodulate large offset light spots to obtain the wrapping slope information;

[0086] The unfolding module is used to unfold the slope information of the package using an unwrapping algorithm;

[0087] The reconstruction module is used to reconstruct the expanded slope using a modified Fourier transform reconstruction algorithm, in order to expand the dynamic range of the Hartmann wavefront sensor and realize the quantitative detection of the phase of the wavefront to be measured.

[0088] According to embodiments of this application, this application also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions.

[0089] Figure 6 This is a schematic diagram of an electronic device used to implement the Shaker-Hartmann wavefront sensor large dynamic range extension method according to embodiments of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0090] like Figure 6 As shown, the electronic device includes one or more processors 501, a memory 502, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6Take a processor 501 as an example.

[0091] The memory 502 is the non-transient computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor to cause the at least one processor to execute the large dynamic range extension method for the Shakhartmann wavefront sensor provided in this application. The non-transient computer-readable storage medium of this application stores computer instructions for causing a computer to execute the large dynamic range extension method for the Shakhartmann wavefront sensor provided in this application.

[0092] The memory 502, as a non-transient computer-readable storage medium, can be used to store non-transient software programs, non-transient computer-executable programs, and units, such as the program instructions / units corresponding to the Shaker-Hartmann wavefront sensor large dynamic range extension method in the embodiments of this application. The processor 501 executes various server functions and data processing by running the non-transient software programs, instructions, and units stored in the memory 502, thereby implementing the Shaker-Hartmann wavefront sensor large dynamic range extension method in the above method embodiments.

[0093] The memory 502 may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created by the use of the electronic device implementing the Shaker-Hartmann wavefront sensor large dynamic range extension method provided in the embodiments of this application. Furthermore, the memory 502 may include high-speed random access memory and may also include non-transient memory, such as at least one disk storage device, flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 502 may optionally include memory remotely located relative to the processor 501. These remote memories can be connected via a network to the electronic device implementing the Shaker-Hartmann wavefront sensor large dynamic range extension method provided in the embodiments of this application. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0094] The electronic device for the Shaker-Hartmann wavefront sensor large dynamic range extension method may further include: an input device 503 and an output device 504. The processor 501, memory 502, input device 503, and output device 504 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0095] Input device 503 can receive input digital or character information, as well as key signal inputs related to user settings and function control of the electronic device implementing the Shaker-Hartmann wavefront sensor large dynamic range extension method provided in this application embodiment. Examples of input devices include touchscreens, keypads, mice, trackpads, touchpads, joysticks, one or more mouse buttons, trackballs, and joysticks. Output device 504 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The display device may include, but is not limited to, LCD (liquid crystal display), LED (light-emitting diode) displays, and plasma displays. In some embodiments, the display device may be a touchscreen.

[0096] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, ASICs (Application-Specific Integrated Circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] These computational programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, PLD (programmable logic device)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0099] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0100] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0101] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0102] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor, characterized in that, include: Acquire the large offset beam pattern array and reference beam pattern array of the Hartmann wavefront sensor, including: A large-offset beam pattern array and a reference beam pattern array were acquired using a Hartmann wavefront sensor. The offset beam pattern and the reference beam pattern were then calculated. x shaft and y The axis offset depends on the magnitude of the offset and the focal length. f Relationship, obtain slope distribution: in, i and j These represent the rows and columns of a two-dimensional sub-aperture array, respectively. This represents the offset along the y-axis. f The focal length of the microlens. express y The slope distribution along the axial direction is demodulated for each spot to obtain the slope distribution of all sub-apertures; Demodulating the large offset spot yields the wrapping slope information, including: When obtaining the slope distribution at each location Then, calculate the slope of the package: Where P represents the sub-aperture spacing, when the offset magnitude satisfy The slope within the detection window will not exhibit a wrap-around phenomenon; the slope range is between (-π, π), and the magnitude of the slope will be determined by... The ratio determines the value; When the offset satisfies When the light spot deviates outside the sub-aperture range, the slope corresponding to the detection window will be wrapped with respect to π, denoted as . ; The slope information of the package is expanded using an unwrapping algorithm; the slope unwrapping is represented as: in, This is represented as an unpacking algorithm. y The slope information after unwrapping in the direction is used This indicates, including: A least-squares unwrapping algorithm based on discrete cosine transform is used to expand the wrapping slope. First, the continuous slope and the wrapping slope are obtained separately. x and y The slope difference between adjacent slopes in the direction is calculated and the difference operation is performed. The loss function is constructed by the least squares algorithm and its first-order partial derivative is obtained. The discrete Poisson equation is obtained by rearranging it. After setting the corresponding boundary conditions, the discrete cosine transform and inverse discrete cosine transform are performed to obtain a continuous slope distribution. The slope unwrapping algorithm expands the slope wrapped in the range of (-π, π) to obtain a continuous slope distribution. The unfolded slope is reconstructed using a wavefront restoration algorithm to extend the dynamic range of the Hartmann wavefront sensor and achieve quantitative detection of the phase of the wavefront under test. The reconstruction of the unfolded slope using the wavefront restoration algorithm includes: First, respectively x and y The slope after directional expansion is subjected to Fourier transform, and the two are linearly combined in the frequency domain. Then, an inverse Fourier transform is performed on it, and the real part of the complex function is taken to obtain the wavefront phase information. The reconstructed wavefront phase is multiplied by the system calibration factor to obtain the accurate reconstructed phase, thus realizing quantitative phase detection.

2. The method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor according to claim 1, characterized in that, For sub-aperture spots that exceed the corresponding detection window, the direct demodulation method or the Fourier transform method is used to demodulate large offset spots.

3. The method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor according to claim 1, characterized in that, A Fourier transform is performed on the large offset spot array, which will present a multi-level spectrum in the frequency domain. All non-zero level spectra are moved to the origin and the spectra are superimposed. A filter window is set and the remaining spectra are filtered out. Finally, an inverse Fourier transform is performed to demodulate and obtain the wrapping slope, which is wrapped in the range of (-π, π).

4. The method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor according to claim 1, characterized in that, The wavefront reconstruction algorithms include modified Fourier transform reconstruction algorithms, deep learning-based wavefront reconstruction algorithms, pattern methods, or region methods.

5. The method for extending the large dynamic range of a Shaker-Hartmann wavefront sensor according to claim 1, characterized in that, right x and y After unwrapping the directional wrapping slope, we obtain the following: x and y Expansion slope in direction and The phase of the wavefront under test is reconstructed using a Fourier transform reconstruction algorithm. This algorithm is then calibrated and corrected. The corrected Fourier transform reconstruction algorithm is expressed as follows: in, , , This represents the operation of taking the real part of a complex function. and These represent the Fourier transform and the inverse Fourier transform, respectively. To calibrate the correction factor for the system, This indicates the reconstructed wavefront phase.

6. A large dynamic range extension system for a Shakhartmann wavefront sensor, based on the large dynamic range extension method for a Shakhartmann wavefront sensor according to any one of claims 1 to 5, characterized in that, include: The acquisition module is used to acquire the large offset spot array and the reference spot array of the Hartmann wavefront sensor; The demodulation module is used to demodulate large offset light spots to obtain the wrapping slope information; The unfolding module is used to unfold the slope information of the package using an unwrapping algorithm; The reconstruction module is used to reconstruct the expanded slope using a modified Fourier transform reconstruction algorithm, in order to expand the dynamic range of the Hartmann wavefront sensor and realize the quantitative detection of the phase of the wavefront to be measured.

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