Compensation device and method of digital holographic microscope

Abstract

The invention discloses a compensation device and method for a digital holographic microscope, relates to the technical field of optical imaging, and can dynamically adjust optical response according to wavefront distortion data measured in real time by using a deformable lens group and a real-time wavefront sensing technology. The adaptive optical element accurately controls the light wavefront, effectively expands the imaging depth, optimizes the focus control, and enhances the resolution. In addition, according to the compensation phase algorithm, the required compensation is calculated by introducing a regularization item, so that the adaptability of the algorithm to environment change is improved, and the robustness of the algorithm in the imaging process is enhanced. Reconstruction and post-processing, such as phase recovery and image sharpening, of the digital hologram are carried out through the constructed phase recovery algorithm, a high-quality imaging result can still be generated under the condition that noise is large or imaging conditions change, and it is further guaranteed that the algorithm can maintain high performance under various imaging conditions.

Description

Technical Field

[0001] The present invention relates to the field of optical imaging technology, and in particular to a compensation device and method for a digital holographic microscope. Background Art

[0002] Traditional microscopes are affected by optical distortions during the imaging process, such as spherical aberration and aberration, which reduce the clarity and accuracy of imaging. With the improvement of digital imaging technology and computer processing capabilities, digital holographic imaging technology has been explored to overcome these distortions. Digital holographic microscopes can provide higher resolution and richer information by using the characteristics of phase information preservation and restoration. Therefore, compensation methods have become one of the key technologies to solve the imaging distortion of digital holographic microscopes. In the development history of digital holographic microscopes, compensation methods have undergone continuous technological innovation and development. First, the application of adaptive optical elements has become one of the key technologies, and the adjustment of optical properties is achieved through deformable lenses or electronically adjusted liquid crystal optical elements, thereby eliminating optical distortion. The establishment of a real-time feedback system has become a development focus, using sensors or computer vision systems to monitor aberrations during imaging and correct them in real time. At the same time, digital image processing algorithms have been optimized, using mathematical models to reversely deduce distortions during imaging and correct images. With the continuous evolution and improvement of multimodal imaging technology, new development opportunities have been brought to the compensation methods of digital holographic microscopes. Corresponding compensation is made according to the characteristics of different imaging modalities, further improving the imaging quality and flexibility.

[0003] Although the above technical solutions have significant advantages in improving the imaging quality of digital holographic microscopes, there are still some technical defects: 1. The robustness of the algorithm needs to be improved. Different samples and imaging conditions may require different optimization parameters. It is a challenge to find a universally applicable, efficient and stable algorithm configuration. In addition, the iterative algorithm may not guarantee convergence, or converge to an incorrect solution under specific conditions. 2. Limitations in imaging depth and resolution. Although digital holographic microscopes can provide three-dimensional imaging capabilities, the imaging depth and resolution are still limited by optical parameters and imaging technology. Especially for deep or highly scattering samples, the imaging quality and resolution may be significantly reduced. 3. The physical limitations of the optical system. Although optical distortion compensation can be improved through software, the physical limitations of the optical system itself, such as diffraction limits and lens manufacturing defects, are still insurmountable obstacles. These physical limitations define the absolute upper limit of the system's optimal performance. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a compensation device and method for a digital holographic microscope.

[0005] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0006] A compensation method for a digital holographic microscope comprises the steps of system initialization and calibration, real-time wavefront inspection, calculation of compensation phase, adjustment of a deformable mirror, digital holographic image acquisition, digital holographic image reconstruction and post-processing, and performance evaluation and optimization.

[0007] As a preferred technical solution of the present invention, the step of calculating the compensating phase is based on the results of the real-time wavefront inspection and the system initialization calibration information, and a compensating phase algorithm is constructed to calculate the phase modulation that needs to be applied to the deformable mirror to eliminate the wavefront aberration and achieve clear digital holographic image reconstruction.

[0008] As a preferred technical solution of the present invention, the construction process of the compensation phase algorithm is as follows:

[0009] (1) Assume W(x, y) as the wavefront error data obtained from the wavefront sensor. It is necessary to find a compensation phase plane Φ(x, y) to correct the distortion in the optical system by adjusting the compensation phase, so that the wavefront is closer to the ideal wavefront and the total wavefront error is minimized. Because it is necessary not only to minimize the error but also to ensure the stability and smoothness of the solution, it is necessary to add a certain regularization term;

[0010] (2) The error function is F(x,y,Φ)=W(x,y)+Φ(x,y)

[0011] Wherein, F(x,y,Φ) represents the wavefront value obtained after the compensation phase Φ(x,y), that is, the sum of the wavefront sensor data W(x,y) and the applied compensation phase Φ(x,y);

[0012] (3) Define optimization goals:

[0013] J(Φ)=∫∫[F(x,y,Φ)-W_ideal(x,y)]^2dxdy+λ∫∫R(Φ)dxdy

[0014] Wherein, J(Φ) is the optimization objective function, which is used to describe the performance index of compensating the phase Φ, that is, minimizing the wavefront error and ensuring the smoothness and stability of the solution; W_ideal(x,y) is the ideal wavefront value, which represents the value of the wavefront under the ideal condition without error; λ is the regularization parameter, which is used to balance the importance of minimizing the error and the regularization term; R(Φ) is the regularization term, which is used to ensure the smoothness or other specific properties of the solution;

[0015] (4) Regularization method:

[0016]

[0017] Where Φ represents the compensation phase surface, which is the unknown function that needs to be solved, and usually represents the optical path or phase distribution in the optical system;

[0018] Represents the gradient of the phase surface Φ, which is used to describe the change of the phase field in space; It is the square of the modulus of the gradient, which indicates the square of the gradient size or rate of change, and is used to measure the intensity or frequency component of the phase change;

[0019] (5) Gradient Expression and Solution

[0020] By taking the derivative of J(Φ), we get the gradient expression about Φ. By setting this gradient to 0, we can find Φ(x,y) that minimizes J(Φ), that is:

[0021] (6) Use iterative method to solve: gradually approach the optimal solution through gradient descent method.

[0022] As a preferred technical solution of the present invention, the digital holographic image reconstruction and post-processing steps utilize a digital holographic reconstruction algorithm to perform mathematical processing on the collected hologram to obtain the image of the object, and use digital image processing technology to further improve the imaging quality; by designing a computational optical imaging framework, image information is further extracted and enhanced from the corrected image to overcome the remaining optical imaging limitations.

[0023] As a preferred technical solution of the present invention, the digital holographic reconstruction algorithm extracts and fuses feature information from images acquired at multiple focal lengths through a lightweight deep neural network architecture design, and adopts a multi-scale processing strategy to allow the network to process information at different scales, thereby enhancing the model's ability to restore image details.

[0024] As a preferred technical solution of the present invention, the computational optical imaging framework includes a phase recovery algorithm and a deep learning model for image reconstruction and quality optimization. After wavefront correction, the phase recovery algorithm can further improve the resolution, restore high-frequency details lost due to optical limitations, and optimize image contrast.

[0025] As a preferred technical solution of the present invention, the construction process of the phase recovery algorithm is:

[0026] (1) Solving the regularized inverse problem

[0027] The wavefront corrected image is set to Ax=y, where A is a linear operator representing the imaging system, x is the ideal image, and y is the observed image. Regularization techniques are used to stabilize the solution:

[0028] argmin_x||Ax-y||2 2 +λ||Lx||2 2

[0029] Among them, ||Ax-y||2 2 is the data fidelity term, L is the regularization matrix, λ is the regularization parameter, λ||Lx||2 2 is the regularization term;

[0030] (2) A deep convolutional neural network is designed, including convolutional layers, activation layers, batch normalization layers, and final reconstruction layers. Each convolutional layer learns different feature representations, from low-level features to high-level features. The difference between the reconstructed image and the ideal image is minimized and evaluated using the mean square error. The quality of the reconstructed image is evaluated during real-time imaging. The regularization parameter λ and network parameters are dynamically adjusted based on the evaluation results. The expectation maximization method is used to gradually optimize the parameters to achieve the optimal image quality.

[0031] A device for implementing a digital holographic microscope compensation method is used to implement the digital holographic microscope compensation method, comprising an adaptive optical system, a light source device, a microscopic imaging device, and a computer control device.

[0032] As a preferred technical solution of the present invention, the adaptive optical system includes a deformable mirror group, which is provided with an optical device with adjustable focal length including a variable focus objective lens and a liquid crystal variable focus lens group. The position and number of focal planes are determined according to the depth range of the imaging target and the imaging resolution requirements, and a sufficient image sequence is obtained by designing the sampling interval to cover the entire depth area of ​​interest.

[0033] As a preferred technical solution of the present invention, the adaptive optical system includes a wavefront sensor and a wavefront corrector. The wavefront sensor is used to detect the wavefront distortion caused by medium inhomogeneity in real time, and the deformable mirror is used to dynamically correct these distortions. The adaptive optical system is calibrated before imaging to determine the optimal wavefront correction parameters and the initial computational imaging configuration. The adaptive optical system continuously monitors and corrects the wavefront distortion of the light wave, reduces the influence of medium inhomogeneity on the imaging quality, and improves the propagation of the light beam in the optical system from a physical level.

[0034] The beneficial effect of adopting the above technical solution is that the present invention combines the advantages of multi-focal length imaging technology and deep learning, and effectively improves the depth and resolution of imaging through efficient feature extraction and fusion and multi-scale processing. By using a deformable mirror group and real-time wavefront sensing technology, the compensation device can dynamically adjust the optical response according to the wavefront distortion data measured in real time. The adaptive optical element precisely controls the light wavefront, effectively expands the imaging depth and optimizes the focus control, thereby enhancing the resolution. In addition, the compensating phase algorithm not only improves the adaptability of the algorithm to environmental changes, but also enhances the robustness of the algorithm during the imaging process by introducing a regularization term to calculate the required compensation. The reconstruction and post-processing of the digital hologram, such as phase recovery and image sharpening, are performed by the constructed phase recovery algorithm, which can still produce high-quality imaging results in the case of large noise or changing imaging conditions, further ensuring that the algorithm can maintain high performance under various imaging conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of an execution device of the present invention. DETAILED DESCRIPTION

[0036] The following examples describe the present invention in detail. Each device used in the present invention is a conventional commercial product and can be directly obtained by purchasing from the market. In the description of the following embodiments, specific details such as specific system structures and technologies are proposed for illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices, circuits and methods is omitted to prevent unnecessary details from hindering the description of the present application.

[0037] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the items listed in association and all possible combinations, and includes these combinations. As used in the present specification and the appended claims, the term "if" can be interpreted as "when..." or "once" or "in response to determination" or "in response to detection" according to the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to determination" or "once [described condition or event] is detected" or "in response to detection [described condition or event]" according to the context. In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0038] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0039] Example 1

[0040] A system for realizing a digital holographic microscope compensation method comprises an adaptive optical system, a light source device, a microscopic imaging device, and a computer control device.

[0041] As the core of the adaptive optical system, the deformable mirror group is used to adjust the shape of the light wavefront in real time to compensate for phase distortion. The deformable mirror group is equipped with an optical device with adjustable focal length, including a variable focus objective lens and a liquid crystal variable focus lens group. According to the depth range of the imaging target and the imaging resolution requirements, the position and number of focal planes are determined, and sufficient image sequences are obtained by designing the sampling interval to cover the entire depth area of ​​interest. According to the image acquisition plan, the various parts of the acquisition system are synchronized to ensure that each image is captured at the correct focal length. During the capture process, any optical deviation caused by environmental changes such as temperature and humidity is monitored and corrected in real time. The acquired image sequence is sorted and necessary preprocessing is performed, such as denoising, brightness correction and normalization. A prepared multi-focal image data set is provided for subsequent image fusion and analysis.

[0042] The adaptive optical system also includes a wavefront sensor and a wavefront corrector. The wavefront sensor is used to detect the wavefront distortion caused by medium inhomogeneity in real time, and the deformable mirror is used to dynamically correct these distortions. The adaptive optical system is calibrated before imaging to determine the optimal wavefront correction parameters and the initial computational imaging configuration. The adaptive optical system continuously monitors and corrects the wavefront distortion of the light wave, reduces the impact of medium inhomogeneity on imaging quality, and improves the propagation of the light beam in the optical system from a physical level.

[0043] The light source device includes a laser light source and a control unit thereof, which provide stable coherent light illumination.

[0044] The microscopic imaging device includes an objective lens, a CCD or CMOS camera, etc., which is used to collect holographic images.

[0045] The computer control unit controls the operation of the entire system, including the adjustment of the adaptive optical elements, image acquisition, and the execution of digital image processing.

[0046] Example 2

[0047] A compensation method for a digital holographic microscope comprises the following steps:

[0048] 1. System initialization and calibration: Before imaging begins, perform system initialization and calibration to set light source intensity, camera parameters, etc. to ensure optimal imaging conditions.

[0049] 2. Real-time wavefront inspection: During the imaging process, the wavefront sensor monitors the wavefront morphology of light passing through the sample in real time and captures its distortion.

[0050] 3. Calculate the compensation phase: Based on the data provided by the wavefront sensor, the compensation phase that needs to be applied is calculated by designing a compensation phase algorithm.

[0051] 4. Adjust the deformable mirror: According to the calculated compensation phase, dynamically adjust the shape or phase distribution of the deformable mirror to compensate for phase distortion in real time.

[0052] 5. Digital holographic image acquisition: During the compensation process, the CCD or CMOS camera acquires the holographic image formed by the compensated light wavefront.

[0053] 6. Digital holographic image reconstruction and post-processing: Construct a phase recovery algorithm to perform mathematical processing on the collected hologram to obtain the image of the object, and use digital image processing techniques such as denoising, sharpening, and contrast enhancement to further improve the imaging quality.

[0054] 7. Performance evaluation and optimization: Use software to evaluate the quality of imaging results. If necessary, adjust system parameters or optimize processing algorithms based on the evaluation results, and execute them repeatedly until the expected imaging effect is achieved.

[0055] By using adaptive optical elements to compensate for phase distortion in real time, it can adapt to sample and environmental changes and improve imaging stability. Combining high-precision optical compensation and advanced digital image processing, it can significantly improve imaging resolution and quality.

[0056] Example 3

[0057] The step of calculating the compensation phase is to calculate the phase modulation that needs to be applied to the deformable mirror based on the results of the real-time wavefront inspection and the system initialization calibration information by constructing a compensation phase algorithm to eliminate the aberration of the wavefront and achieve clear digital holographic image reconstruction. The construction process of the compensation phase algorithm is as follows:

[0058] 1. Wavefront error identification and compensation objectives:

[0059] In practical applications, the wavefront sensor measures the errors in front of the wavefront, which come from the optical system itself or the external environment. By analyzing the wavefront data, we can understand the performance of the optical system and the distortion that needs to be compensated. After determining the wavefront error, we need to set a clear compensation goal, that is, to eliminate or minimize these errors through compensating phase adjustment so that the light wavefront is closer to the ideal state. When formulating compensation measures, in addition to pursuing error minimization, the stability and smoothness of the solution must also be considered. Such comprehensive considerations can ensure that the compensation results we obtain are effective and reliable.

[0060] 2. Construct the error function:

[0061] The construction of the error function is a key step, which needs to comprehensively consider the wavefront error information provided by the wavefront sensor data and the compensation phase we expect to apply to evaluate the effects of different compensation strategies. By defining an appropriate error function, we can quantify the degree of wavefront distortion and the effect of compensation measures, thereby providing guidance and basis for the subsequent optimization process. The design of this error function needs to take into account the accuracy of the wavefront error data and the feasibility of the implementation of the compensation strategy to ensure that the final compensation effect is effective.

[0062] 3. Optimization goal setting:

[0063] When setting the optimization goal, we need to consider the balance between minimizing the wavefront error and maintaining the smoothness of the solution. This requires us to set a suitable optimization objective function to clearly guide the optimization process of the compensation phase. The setting of the optimization goal should take into account the actual compensation needs and system performance requirements to ensure that the final compensation result can effectively reduce the wavefront error while maintaining the stability and smoothness of the solution. By setting clear optimization goals, we can effectively guide the implementation of the compensation phase algorithm, making the compensation process more targeted and efficient.

[0064] 4. Use appropriate regularization methods:

[0065] The choice of regularization method is crucial to ensure the smoothness and stability of the compensation results. By adopting appropriate regularization methods, the variation of the compensation phase can be effectively controlled to avoid introducing too many high-frequency components or local extreme values, thereby ensuring that the solution has good stability and reliability in practical applications. Choosing a suitable regularization method can minimize the wavefront error while maintaining the smoothness and practicality of the compensation solution, providing better image reconstruction effects for the application of digital holographic microscopy.

[0066] 5. Solution and optimization process:

[0067] In the process of solving and optimizing, it is necessary to gradually approach the optimal solution through repeated iterations. By evaluating the current wavefront state and the applied compensation phase adjustment in real time, and gradually adjusting the compensation strategy, the best compensation effect can be achieved. Through continuous iterative calculation and optimization, we can gradually adjust the compensation phase so that the wavefront error is effectively reduced and the stability and effectiveness of the entire compensation process are guaranteed.

[0068] 6. Iterative solution and adjustment:

[0069] By continuously iterating and adjusting, we can gradually improve the performance and effect of the compensation phase algorithm. In each iteration, we evaluate the performance of the solution based on the current wavefront state and the applied compensation strategy, and make corresponding adjustments accordingly. By continuously iterating and adjusting until a satisfactory compensation effect is achieved, we can effectively improve the quality of digital holographic microscope images and achieve clearer and more accurate image reconstruction.

[0070] Example 4

[0071] The digital holographic image reconstruction and post-processing steps use a digital holographic reconstruction algorithm to mathematically process the collected hologram to obtain the image of the object, and use digital image processing technology to further improve the imaging quality; by designing a computational optical imaging framework, further extract and enhance image information from the corrected image to overcome the remaining optical imaging limitations. The computational optical imaging framework includes a phase recovery algorithm and a deep learning model for image reconstruction and quality optimization. After wavefront correction, the phase recovery algorithm can further improve resolution, restore high-frequency details lost due to optical limitations, and optimize image contrast.

[0072] 1. Overview of digital holographic image reconstruction process:

[0073] First, the collected holograms are mathematically processed and the digital holographic reconstruction algorithm is used to reconstruct the image of the object. The original hologram data is processed by the algorithm to obtain a preliminary image of the object. The quality of these images is further enhanced by using advanced digital image processing technology to improve the accuracy and usability of the imaging results.

[0074] The digital holographic reconstruction algorithm uses a lightweight deep neural network architecture design to extract and fuse feature information from images acquired at multiple focal lengths. It adopts a multi-scale processing strategy, allowing the network to process information at different scales, thereby enhancing the model's ability to restore image details.

[0075] First, we use upsampling neural network and downsampling neural network to sample the image. Then fill the downsampled pattern into the upsampled pattern. When filling, convolve the downsampled pattern with the upsampled pattern at the corresponding position. Then extract features from the obtained filled image at different focal lengths, map the extracted features to the multidimensional space coordinate system, calculate the support domain of the data extreme points in the multidimensional space coordinate system, the support domain is the set of feature vectors that are collinear with the data extreme points, reconstruct the image by inverse mapping the data in the multidimensional space coordinate system, and then perform pooling on the non-support domain image.

[0076] 2. Application of computational optical imaging framework:

[0077] After completing the basic reconstruction, a computational optical imaging framework is designed and applied, which can extract and enhance more image information from the reconstructed and corrected images. It uses phase recovery algorithms and deep learning models to not only further optimize the image quality and improve the resolution, but also restore some image details lost due to optical limitations, such as high-frequency details, and optimize the image contrast.

[0078] 3. Implementation of phase recovery algorithm:

[0079] The key to phase retrieval algorithms is dealing with the limitations and complexities of the imaging system. The relationship between the wavefront corrected image and the observed image is first identified and defined, and the problem is stabilized using regularization techniques. This process uses an approach that balances data fidelity and regularization to ensure a stable and high-quality solution by minimizing the difference between the ideal and reconstructed images.

[0080] The wavefront corrected image is set to Ax=y, where A is a linear operator representing the imaging system, x is the ideal image, and y is the observed image. Regularization techniques are used to stabilize the solution:

[0081] argmin_x||Ax-y||2 2 +λ||Lx||2 2

[0082] Among them, ||Ax-y||2 2 is the data fidelity term, L is the regularization matrix, λ is the regularization parameter, λ||Lx||2 2 is the regularization term.

[0083] 4. Integration of deep learning models:

[0084] Design a deep convolutional neural network that learns different feature representations of images, from basic edge information to more complex texture and shape features, through different levels of convolutional layers, activation layers, and batch normalization layers. Minimize the error between the reconstructed image and the ideal image, and evaluate it by the mean square error. In the actual imaging process, the quality of the reconstructed image is evaluated in real time, and the regularization parameters and network parameters are dynamically adjusted accordingly. The expectation maximization strategy is used to gradually optimize the parameters in order to maximize the image quality.

[0085] Since the characteristic details of the image are enhanced during the image reconstruction process and the support domain is used to provide a reference system for image optimization, the convergence speed of the deep convolutional neural network can be accelerated during image optimization, thereby improving the image optimization speed.

[0086] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone computers, floor-standing stand-alone computers, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS network, World Wide Internet, etc.

[0088] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0089] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0090] Each functional unit in each embodiment of the present invention may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and may also instruct the relevant hardware to complete it through a computer program. The computer program may be stored in a computer-readable storage medium, and the computer program may implement the steps of the above-mentioned method embodiments when executed by the processor. . Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0091] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A compensation method for a digital holographic microscope, characterized in that: The steps include system initialization and calibration, real-time wavefront inspection, calculation of compensation phase, adjustment of deformable mirror, digital holographic image acquisition, digital holographic image reconstruction and post-processing, performance evaluation and optimization.

2. The compensation method of a digital holographic microscope according to claim 1, characterized in that: The step of calculating the compensating phase calculates the phase modulation that needs to be applied to the deformable mirror based on the result of the real-time wavefront inspection and the system initialization calibration information by constructing a compensating phase algorithm to eliminate the wavefront aberration and achieve clear digital holographic image reconstruction.

3. The compensation method of a digital holographic microscope according to claim 2, characterized in that: The construction process of the compensation phase algorithm is as follows: (1) Assume that W(x, y) is the wavefront error data obtained from the wavefront sensor. It is necessary to find a compensation phase plane Φ(x, y) to correct the distortion in the optical system by adjusting the compensation phase, so that the wavefront is closer to the ideal wavefront and the total wavefront error is minimized. (2) The error function is F(x,y,Φ)=W(x,y)+Φ(x,y) Wherein, F(x,y,Φ) represents the wavefront value obtained after the compensation phase Φ(x,y), that is, the sum of the wavefront sensor data W(x,y) and the applied compensation phase Φ(x,y); (3) Define optimization goals: J(Φ)=∫∫[F(x,y,Φ)-W_ideal(x,y)] 2 dxdy+λ∫∫R(Φ)dxdy Wherein, J(Φ) is the optimization objective function, which is used to describe the performance index of compensating the phase Φ, that is, minimizing the wavefront error and ensuring the smoothness and stability of the solution; W_ideal(x,y) is the ideal wavefront value, which represents the value of the wavefront under the ideal condition without error; λ is the regularization parameter, which is used to balance the importance of minimizing the error and the regularization term; R(Φ) is the regularization term, which is used to ensure the smoothness or other specific properties of the solution; (4) Regularization method: Where Φ represents the compensation phase surface, which is the unknown function that needs to be solved, and usually represents the optical path or phase distribution in the optical system; Represents the gradient of the phase surface Φ, which is used to describe the change of the phase field in space; It is the square of the modulus of the gradient, which indicates the square of the gradient size or rate of change, and is used to measure the intensity or frequency component of the phase change; (5) Gradient Expression and Solution By taking the derivative of J(Φ), we get the gradient expression about Φ. By setting this gradient to 0, we can find Φ(x,y) that minimizes J(Φ), that is: (6) Use iterative method to solve: gradually approach the optimal solution through gradient descent method.

4. The compensation method of a digital holographic microscope according to claim 1, characterized in that: The digital holographic image reconstruction and post-processing steps utilize a digital holographic reconstruction algorithm to perform mathematical processing on the collected hologram to obtain an image of the object, and use digital image processing technology to further improve the imaging quality; by designing a computational optical imaging framework, image information is further extracted and enhanced from the corrected image to overcome the remaining optical imaging limitations.

5. The compensation method of a digital holographic microscope according to claim 4, characterized in that: The digital holographic reconstruction algorithm uses a lightweight deep neural network architecture design to extract and fuse feature information from images acquired at multiple focal lengths, and adopts a multi-scale processing strategy to allow the network to process information at different scales, thereby enhancing the model's ability to restore image details.

6. The compensation method of a digital holographic microscope according to claim 4, characterized in that: The computational optical imaging framework performs image reconstruction and quality optimization through a phase recovery algorithm. After wavefront correction, the phase recovery algorithm can further improve the resolution, restore high-frequency details lost due to optical limitations, and optimize image contrast.

7. The compensation method of a digital holographic microscope according to claim 6, characterized in that: The construction process of the phase recovery algorithm is: (1) Solving the regularized inverse problem The wavefront corrected image is set to Ax=y, where A is a linear operator representing the imaging system, x is the ideal image, and y is the observed image. Regularization techniques are used to stabilize the solution: argmin_x||Ax-y||2 2 +λ||Lx||2 2 Among them, ||Ax-y||2 2 is the data fidelity term, L is the regularization matrix, λ is the regularization parameter, λ||Lx||2 2 is the regularization term; (2) A deep convolutional neural network is designed, including convolutional layers, activation layers, batch normalization layers, and final reconstruction layers. Each convolutional layer learns different feature representations, from low-level features to high-level features. The difference between the reconstructed image and the ideal image is minimized and evaluated using the mean square error. The quality of the reconstructed image is evaluated during real-time imaging. The regularization parameter λ and network parameters are dynamically adjusted based on the evaluation results. The expectation maximization method is used to gradually optimize the parameters to achieve the optimal image quality.

8. A device for implementing a digital holographic microscope compensation method, used to implement the method of claim 1, characterized in that: It includes an adaptive optical system, a light source device, a microscopic imaging device, and a computer control device.

9. The device for implementing the digital holographic microscope compensation method according to claim 8, characterized in that: The adaptive optical system includes a deformable mirror group, which is provided with an optical device with adjustable focal length including a variable focus objective lens and a liquid crystal variable focus lens group. The position and number of focal planes are determined according to the depth range of the imaging target and the imaging resolution requirements, and sufficient image sequences are obtained by designing the sampling interval to cover the entire depth area of ​​interest.

10. The device for implementing the digital holographic microscope compensation method according to claim 8, characterized in that: The adaptive optical system includes a wavefront sensor and a wavefront corrector. The wavefront sensor is used to detect the wavefront distortion caused by medium inhomogeneity in real time, and the deformable mirror is used to dynamically correct these distortions. The adaptive optical system is calibrated before imaging to determine the optimal wavefront correction parameters and the initial computational imaging configuration. The adaptive optical system continuously monitors and corrects the wavefront distortion of the light wave, reduces the influence of medium inhomogeneity on the imaging quality, and improves the propagation of the light beam in the optical system from a physical level.

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