A large depth of field reconstruction method based on fourier ptychographic imaging

Abstract

The application discloses a large depth-of-field reconstruction method based on Fourier ptychographic imaging. The method uses the characteristic of Fourier ptychographic imaging technology that can fully utilize redundant information, establishes a layered reconstruction model, after high-resolution objects of each layer are accurately reconstructed, each layer of object information is synthesized, and finally, a reconstruction result with a large field of view, high resolution and large depth of field is obtained. Compared with the traditional Fourier ptychographic imaging technology, the reconstruction method significantly expands the depth-of-field range of system imaging, and is very suitable for pathological imaging of thick samples.

Description

Technical Field

[0001] This invention pertains to optical microscopy imaging, specifically a method for large depth-of-field reconstruction based on Fourier layered imaging. Background Technology

[0002] In the field of traditional microscopy, there has always been an irreconcilable contradiction: the contradiction between imaging field of view and imaging resolution. Low-powered microscopes can obtain images with a large field of view, but with low resolution; high-powered microscopes can obtain high-resolution images, but with a smaller field of view. Fourier layer imaging, a computational microscopy technique developed in recent years, integrates the concepts of phase retrieval and synthetic aperture. It iteratively maps light intensity information recorded in the spatial domain to a fixed mapping relationship in the frequency domain, ultimately achieving both a large field of view and high resolution imaging results. In a traditional Fourier layer imaging system, the sample is illuminated by plane waves at different angles and imaged through a low numerical aperture objective. The two-dimensional thin object, illuminated by plane waves from different angles, has its spectrum shifted to corresponding positions on the back focal plane of the objective. Therefore, some frequency components that would otherwise exceed the numerical aperture of the objective are shifted within the numerical aperture, thus enabling them to be transmitted to the imaging plane for imaging. Conversely, incident light at different angles can be equivalently represented as overlapping pupil functions (sub-apertures) at different positions in the spectrum. Each time the light passes through a different sub-aperture, the spectrum forms a stack in the frequency domain. Then, a series of low-resolution images captured by the camera are used to iterate in the frequency domain, updating the spectral information in the corresponding sub-apertures in turn. The overlapping of sub-apertures expands the frequency bandwidth and recovers high-frequency information that exceeds the spatial resolution limit of the objective lens. Ultimately, a large field-of-view, high-resolution image of the object's light intensity and phase is reconstructed simultaneously. This achieves a large field-of-view and high-resolution imaging result using a low numerical aperture and low magnification objective lens.

[0003] However, the traditional Fourier layered imaging model has a strong assumption: the object being measured is a two-dimensional, thin planar object. But in reality, the sample to be observed is often a three-dimensional object with thickness, and artifacts and other errors are very likely to occur when reconstructing such objects. To address the above problems, Tian Lei et al. combined the classic multi-slice model in CT and MRI with the FPM imaging method to construct a three-dimensional FPM reconstruction algorithm framework, achieving super-resolution reconstruction of three-dimensional samples (Tian L, Waller L. 3D intensity and phase imaging from light field measurements in an LED array microscope[J]. optica, 2015, 2(2): 104-111.). The multi-slice model models three-dimensional samples as a series of two-dimensional sample slices with specific spacing in the spatial domain, and connects the sample slices through the free propagation of light waves during the forward modeling and reverse recovery process. This algorithm can achieve high-resolution reconstruction results for each layer of two overlapping resolution plates. Zuo et al. proposed a Fourier ptychographic diffraction tomography technique, extending the traditional FPM model to three dimensions and performing phase retrieval and reconstruction in the three-dimensional Fourier domain (Zuo C, Sun J, Li J, et al. Wide-field high-resolution 3D microscopy with Fourier ptychographic diffraction tomography[J]. Optics and Lasers in Engineering, 2020, 128: 106003.). They derived the spectral support domain of the microscope system by using the illumination numerical aperture and objective lens numerical aperture, and obtained the specific structure of this support domain, thus realizing a Fourier ptychographic imaging method for three-dimensional objects. These methods often require a large amount of data acquisition and tomographic calculations to obtain high-precision three-dimensional reconstruction results. However, these techniques are difficult to apply in the pathological diagnosis industry because: on the one hand, doctors often do not need high-resolution three-dimensional information during diagnosis, but only need two-dimensional image information with a large depth of field; on the other hand, the above algorithms consume a lot of time during computation, limiting the practical application of these techniques. Summary of the Invention

[0004] The purpose of this invention is to propose a large depth-of-field reconstruction method based on Fourier layered imaging to solve the problems of artifacts and other errors generated when Fourier layered imaging reconstructs thicker objects, and to achieve high-speed, high-resolution, large field of view, and large depth-of-field pathological imaging.

[0005] The technical solution to achieve the objective of this invention is as follows: a large depth-of-field reconstruction method based on Fourier layered imaging, comprising the following steps:

[0006] Step 1, Acquisition of raw intensity images: Using a ring-shaped LED board as the illumination source for the microscope, the three RGB beads of each LED are lit in sequence. The light emitted by the beads after illuminating the sample is regarded as planar monochromatic light. By synchronously triggering the camera to scan the bead array, the corresponding low-resolution bright field image of each bead is acquired.

[0007] Step 2: Determine the low-frequency cutoff frequency based on the numerical aperture of the microscope objective and the wavelength of the incident light, and calculate the spatial frequency of the incident light corresponding to each LED bead based on its spatial coordinates.

[0008] Step 3: Add up all the captured low-resolution bright-field images and take the average, then perform linear interpolation to enlarge and obtain the initial solution of the high-resolution image, and divide the initial solution into several layers of complex amplitude objects;

[0009] Step 4: Set the defocus value corresponding to the complex amplitude objects of different layers. For each complex amplitude object, first transform each layer of complex amplitude it represents to the frequency domain through Fourier transform. Then multiply the frequency domain by the low-pass filter function corresponding to the tilt angle and the transfer function corresponding to the defocus amount to obtain the updated sub-aperture spectrum of each layer. Then transform the updated sub-aperture spectrum of each layer back to the spatial domain through inverse Fourier transform to obtain the complex amplitude object corresponding to each sub-aperture at different defocus positions in each layer.

[0010] Step 5: Based on the distribution of light intensity of each layer of complex amplitude objects corresponding to the same sub-aperture, set the update coefficient for each sub-aperture in each layer.

[0011] Step 6, iterative reconstruction: Fourier layer imaging technology is used to transfer the complex amplitude object corresponding to each sub-aperture to the frequency domain in each layer, and the synthetic aperture is calculated one by one in the frequency domain according to the update coefficients obtained in step 5. When the cost function of each layer is less than the set threshold, the iteration stops and the high-resolution result of each layer is obtained; otherwise, return to step 4.

[0012] Step 7: Combine the high-resolution results obtained from different converged layers to obtain the final large depth-of-field image.

[0013] Preferably, the specific formula for determining the low-frequency cutoff frequency based on the numerical aperture of the microscope objective and the wavelength of the incident light is as follows:

[0014]

[0015] In the formula, This is the low-frequency cutoff frequency. The wavelength of the incident light, Numerical aperture of a microscope objective.

[0016] Preferably, the specific method for calculating the spatial frequency of the incident light corresponding to each LED bead based on its spatial coordinate position is as follows:

[0017] The spatial frequency of the incident light corresponding to each LED bead is calculated based on its spatial coordinates. Where n represents the LED number, when The images captured at that time For bright field images; when The images captured at that time For dark field images, when At this time, the illumination is considered to be matched illumination, and the illumination aperture is... , This is the low-frequency cutoff frequency.

[0018] Preferably, the initial solution of the high-resolution image Specifically:

[0019]

[0020] In the formula, P{} represents bilinear interpolation upsampling. Low-resolution bright-field images captured, where m is the total number of images captured.

[0021] Preferably, in step three, the initial solution is... Divided into several layers of complex amplitude objects Each layer is the initial solution for the corresponding layer, and the calculation formula is as follows;

[0022]

[0023] In the formula, The number of floors.

[0024] Preferably, in step four, each sub-aperture at different defocus positions corresponds to a complex amplitude object. Specifically:

[0025]

[0026]

[0027] Where n represents the LED number, For the number of floors, Represents the Fourier transform operation. Represents the inverse Fourier transform operation. This indicates a spectrum shift operation, the amount of which is determined by the angle of the tilted light corresponding to each LED. This is a low-pass filter function. , For spatial frequency, For wave number, This is the low-frequency cutoff frequency. The wavelength of the incident light.

[0028] Preferably, an update coefficient is set based on the light intensity distribution of the complex amplitude object corresponding to each sub-aperture in each layer. ;

[0029]

[0030] In the formula, n represents the number of the LED bead. For the number of floors, The total number of images captured. For complex amplitude objects with different sub-apertures at different defocus positions, This indicates the conjugate operation. This represents absolute value operations.

[0031] Preferably, the specific method for iterative reconstruction in step six is ​​as follows:

[0032] Fourier layer imaging technology is used to update the complex amplitude object corresponding to each sub-aperture in each layer. At the same time, multiply the exponent by the update coefficient from step five. ;

[0033]

[0034] in, For each layer and each sub-aperture, the updated complex amplitude object is represented, where n represents the LED number. To update the coefficients, P{} represents bilinear interpolation upsampling. The image is a low-resolution bright-field image. The image represents the complex amplitude object before the update, and m represents the total number of images captured. Number of floors;

[0035] The updated complex amplitude object for each layer and each sub-aperture Fourier transform to the frequency domain, and update the complex amplitude object using the updated sub-aperture spectrum. The portion corresponding to the sub-aperture;

[0036]

[0037] in, This represents the updated complex amplitude object. Represents the Fourier transform operation. Represents the inverse Fourier transform operation. This indicates a spectrum shift operation, the amount of which is determined by the angle of the tilted light corresponding to each LED. This is a low-pass filter function. , For spatial frequency, For wave number, This is the low-frequency cutoff frequency. The wavelength of the incident light. To update the step size.

[0038] Finally, the cost function COST is calculated;

[0039]

[0040] This indicates the conjugate operation. This represents the absolute value operation. Let be the cost function.

[0041] The iteration stops when the cost function of each layer is less than a set threshold, and the high-resolution result of each layer is obtained; otherwise, the complex amplitude object from step six is ​​retained. Replace the complex amplitude object in step five with it. Repeat step five to perform a new round of complex amplitude for each layer and each sub-aperture. The calculation and iteration.

[0042] Preferably, in step seven, the high-resolution results obtained from convergence at different layers are combined to obtain the final large depth-of-field image. Specifically:

[0043]

[0044] in, For the updated complex amplitude results of each layer, This indicates the conjugate operation.

[0045] Compared with the prior art, the present invention has the following significant advantages: (1) Compared with the traditional Fourier layered imaging technology, the present invention has a larger depth imaging range and solves the problem of artifacts and other errors generated when reconstructing thick objects in the traditional algorithm. (2) Compared with other imaging algorithms that obtain three-dimensional information of objects, the present invention has a smaller amount of data acquisition, faster reconstruction speed, and can achieve high-speed high-throughput reconstruction; at the same time, it synthesizes the object information within a certain depth range into a two-dimensional image, which is convenient for observation and judgment during pathological diagnosis.

[0046] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0047] Figure 1This is a schematic diagram of an actual device that uses a single LED in a ring-shaped lighting system to illuminate an object.

[0048] Figure 2 This is a flowchart illustrating the iterative reconstruction method of the present invention.

[0049] Figure 3 This invention presents a large depth-of-field reconstruction result for a thick HPV cell sample. Figure 3 (a) shows the reconstruction result obtained using traditional Fourier layered imaging. Figure 3 (b) in the figure represents the reconstruction result obtained by the large depth-of-field reconstruction method proposed in this invention. Detailed Implementation

[0050] like Figure 1 , 2 As shown, a large depth-of-field reconstruction method based on Fourier layered imaging has the following specific steps:

[0051] Step 1, Original Intensity Image Acquisition: Using a ring-shaped LED board as the microscope's illumination source, the three RGB LEDs of each LED element are lit sequentially. The light emitted by the LEDs after illuminating the sample can be considered to have a wavelength of... The light source is a monochromatic plane light (i=1,2,3). The center of the ring-shaped light plate is on the optical axis of the microscope objective system. After the incident light illuminates the sample, it passes through the microscope objective system. By synchronously triggering the camera in conjunction with the scanning of the light array, the corresponding low-resolution bright-field image of each light bead is acquired. , where n is the LED number of the LED panel.

[0052] This invention is based on a ring-shaped LED light panel 4 for illumination. Compared to traditional bright-field microscopes, the traditional light source needs to be replaced with a ring-shaped LED array. The LED array is placed below the object 3, while the other parts of the microscope, such as the microscope objective and camera acquisition module, remain consistent with traditional bright-field microscopes. Here, f is the focal length of the objective lens 2, typically around 10mm, and the center of the ring-shaped illumination source is on the optical axis of the system. The LED light panel illuminates the object, and the object information is ultimately imaged onto the camera 1 through the objective lens. The LED array contains several LED beads, each composed of red, green, and blue LEDs. Typical wavelengths are: red 620nm, green 550nm, and blue 460nm. The distance between each LED bead and the center point is 2cm, 4cm, 6cm… The LED array can be purchased directly from the market. Table 1 shows a parameter diagram of a commercially available ring-shaped LED light panel. The light panel contains five concentric rings of LED beads, with the number of beads ranging from 8, 16, 24, 32, to 48 from the inside out. Each LED bead has a brightness of over 1000 cd / m2.

[0053] Table 1:

[0054] project parameter LED unit wavelength Red 635nm, Green 550nm, Blue 420nm Number of rings 5 Ring LED radius 2cm, 4cm, 6cm, 8cm, 10cm Number of ring-shaped LED beads 8，16，24，32，48 LED unit brightness <![CDATA[1000cd / m 2 ]]> power supply 5V Current Maximum 3A (full brightness)

[0055] Each LED in an LED array can be lit individually. Control methods include, but are not limited to, microcontroller and ARM technologies.

[0056] Step two: Determine the low-frequency cutoff frequency based on the numerical aperture of the microscope objective and the wavelength of the incident light. Calculate the spatial frequency of the incident light corresponding to each LED bead based on its spatial coordinates. The specific process is as follows:

[0057] According to the numerical aperture of the microscope objective and the wavelength of the incident light Determine the low-frequency cutoff frequency The calculation formula is:

[0058]

[0059] The spatial frequency of the incident light corresponding to each LED bead is calculated based on its spatial coordinates. , where n represents the LED number. When The images captured at that time For bright field images; when The images captured at that time For dark field images, when At this time, the lighting is considered to be matched lighting, and the lighting aperture is... The algorithm requires that the illumination be exactly matched illumination. Since each LED on the ring-shaped light panel is equidistant from the center point, and the center point is on the optical axis, when the spatial position of one LED satisfies the matched illumination condition, all LEDs satisfy the matched illumination condition.

[0060] Step 3, Image Initialization: Initialize all captured low-resolution bright-field images The sums are then averaged, and linear interpolation is performed to upscale the image to obtain an initial solution for the high-resolution image. The initial solution calculation formula is as follows:

[0061]

[0062] Where P{} represents bilinear interpolation upsampling. The initial solution... Divided into several layers of complex amplitude , Let be the number of layers. Each layer is considered as the initial solution for the corresponding layer, and the calculation formula is as follows;

[0063]

[0064] Step 4, Layer Settings: Set the defocus value for different layers of objects to... For each layer of objects First, transform it to the frequency domain using a Fourier transform, and then multiply it in the frequency domain by the low-pass filter function corresponding to the tilt angle and the defocusing amount. The corresponding transfer function is then used to transform the spectrum back into the spatial domain via an inverse Fourier transform, yielding complex amplitude objects with different sub-apertures at different defocus positions. ;

[0065]

[0066]

[0067] Where n represents the LED number, For the number of floors, Represents the Fourier transform operation. Represents the inverse Fourier transform operation. This indicates a spectrum shift operation, the amount of which is determined by the angle of the tilted light corresponding to each LED. This is a low-pass filter function. , For spatial frequency, For wave number, This is the low-frequency cutoff frequency. The wavelength of the incident light.

[0068] Step 5: Calculate the update coefficient based on the light intensity distribution of the complex amplitude object corresponding to each sub-aperture in each layer. ;

[0069]

[0070] In the formula, n represents the number of the LED bead. For the number of floors, The total number of images captured. For complex amplitude objects with different sub-apertures at different defocus positions, This indicates the conjugate operation. This represents absolute value operations.

[0071] Step Six: Iterative Reconstruction. Using Fourier layered imaging technology, the complex amplitude object corresponding to each sub-aperture in each layer is transformed into the frequency domain. The synthetic aperture is then calculated sequentially in the frequency domain using the update coefficients obtained in Step Five. Iteration stops when the cost function of each layer is less than a set threshold, yielding a high-resolution result for each layer. Specifically, Fourier layered imaging technology is used to update the complex amplitude object corresponding to each sub-aperture in each layer. At the same time, multiply the exponent by the update coefficient from step five. ;

[0072]

[0073] in, For each layer and each sub-aperture, the updated complex amplitude object is represented, where n represents the LED number. To update the coefficients, P{} represents bilinear interpolation upsampling. The image is a low-resolution bright-field image. The image represents the complex amplitude object before the update, and m represents the total number of images captured. Number of floors;

[0074] The updated complex amplitude object for each layer and each sub-aperture Fourier transform to the frequency domain, and update the complex amplitude object using the updated sub-aperture spectrum. The portion corresponding to the sub-aperture;

[0075]

[0076] in, Represents the Fourier transform operation. Represents the inverse Fourier transform operation. This indicates a spectrum shift operation, the amount of which is determined by the angle of the tilted light corresponding to each LED. This is a low-pass filter function. , For spatial frequency, For wave number, This is the low-frequency cutoff frequency. The wavelength of the incident light. To update the step size.

[0077] Finally, the cost function COST is calculated;

[0078]

[0079] This indicates the conjugate operation. This represents the absolute value operation. Let be the cost function.

[0080] The iteration stops when the cost function of each layer is less than a set threshold, and the high-resolution result of each layer is obtained; otherwise, the complex amplitude object from step six is ​​retained. Replace the complex amplitude object in step five with it. Repeat step five to perform a new round of complex amplitude for each layer and each sub-aperture. The calculation and iteration.

[0081] Step 7: Combine the high-resolution results obtained from different converged layers to obtain the final large depth-of-field image. Specifically:

[0082]

[0083] in, For the updated complex amplitude results of each layer, This indicates the conjugate operation.

[0084] Figure 3 The reconstruction results of a thick HPV cell sample are shown. The system was imaged using a 10x objective lens (NA=0.4). Figure 3 (a) Reconstruction results using conventional Fourier transform imaging. Due to the shallow depth of field of a 10x scope, when imaging thick samples such as HPV cells, the focus is on the inside of the cell nucleus, and the cytoplasm below the cell nucleus cannot be seen due to defocus. Figure 3 (b) shows the large depth-of-field reconstruction result obtained using this algorithm. It can be seen that, while ensuring that the cell nucleus is clearly focused, the morphology of the cytoplasm in the lower layer can also be clearly seen, which is convenient for doctors to diagnose the cell state.

Claims

1. A method for large depth-of-field reconstruction based on Fourier layered imaging, characterized in that, The steps are as follows: Step 1, Acquisition of raw intensity images: Using a ring-shaped LED board as the illumination source for the microscope, the three RGB beads of each LED are lit in sequence. The light emitted by the beads after illuminating the sample is regarded as planar monochromatic light. By synchronously triggering the camera to scan the bead array, the corresponding low-resolution bright field image of each bead is acquired. Step 2: Determine the low-frequency cutoff frequency based on the numerical aperture of the microscope objective and the wavelength of the incident light, and calculate the spatial frequency of the incident light corresponding to each LED bead based on its spatial coordinates. Step 3: Add up all the captured low-resolution bright-field images and take the average, then perform linear interpolation to enlarge and obtain the initial solution of the high-resolution image, and divide the initial solution into several layers of complex amplitude objects; Step 4: Set the defocus value corresponding to the complex amplitude objects of different layers. For each complex amplitude object, first transform each layer of complex amplitude it represents to the frequency domain through Fourier transform. Then multiply the frequency domain by the low-pass filter function corresponding to the tilt angle and the transfer function corresponding to the defocus amount to obtain the updated sub-aperture spectrum of each layer. Then transform the updated sub-aperture spectrum of each layer back to the spatial domain through inverse Fourier transform to obtain the complex amplitude object corresponding to each sub-aperture at different defocus positions in each layer. Step 5: Based on the distribution of light intensity of each layer of complex amplitude objects corresponding to the same sub-aperture, set the update coefficient for each sub-aperture in each layer. Step Six: Iterative Reconstruction. Using Fourier layered imaging technology, the complex amplitude object corresponding to each sub-aperture in each layer is transferred to the frequency domain. The synthetic aperture is then calculated one by one in the frequency domain using the update coefficients obtained in Step Five. The iteration stops when the cost function of each layer is less than a set threshold, obtaining the high-resolution result for each layer; otherwise, the process returns to Step Four. The specific method is as follows: Fourier layer imaging technology is used to update the complex amplitude object corresponding to each sub-aperture in each layer. At the same time, multiply the exponent by the update coefficient from step five. ; in, For each layer and each sub-aperture, the updated complex amplitude object is represented, where n represents the LED number. To update the coefficients, P{} represents bilinear interpolation upsampling. The image is a low-resolution bright-field image. The image represents the complex amplitude object before the update, and m represents the total number of images captured. Number of floors; The updated complex amplitude object for each layer and each sub-aperture Fourier transform to the frequency domain, and update the complex amplitude object using the updated sub-aperture spectrum. The portion corresponding to the sub-aperture; in, This represents the updated complex amplitude object. Represents the Fourier transform operation. Represents the inverse Fourier transform operation. This indicates a spectrum shift operation, the amount of which is determined by the angle of the tilted light corresponding to each LED. This is a low-pass filter function. , For spatial frequency, For wave number, This is the low-frequency cutoff frequency. The wavelength of the incident light; To update the step size, These are the defocus values ​​corresponding to objects with different complex amplitude layers; Calculate the cost function COST, which is specifically: This indicates the conjugate operation. This represents the absolute value operation. Let P{} be the cost function, and P{} be the bilinear interpolation upsampling function. The iteration stops when the cost function of each layer is less than a set threshold, and the high-resolution result of each layer is obtained; otherwise, the complex amplitude object from step six is ​​retained. Replace the complex amplitude object in step four with it. Return to step four and perform a new round of complex amplitude for each layer and each sub-aperture. Calculation and iteration; Step 7: Combine the high-resolution results obtained from different converged layers to obtain the final large depth-of-field image.

2. The large depth-of-field reconstruction method based on Fourier layered imaging according to claim 1, characterized in that, The specific formula for determining the low-frequency cutoff frequency based on the numerical aperture of the microscope objective and the wavelength of the incident light is as follows: In the formula, This is the low-frequency cutoff frequency. The wavelength of the incident light, Numerical aperture of a microscope objective.

3. The large depth-of-field reconstruction method based on Fourier layered imaging according to claim 1, characterized in that, The specific method for calculating the spatial frequency of the incident light corresponding to each LED bead based on its spatial coordinate position is as follows: The spatial frequency of the incident light corresponding to each LED bead is calculated based on its spatial coordinates. Where n represents the LED number, when The images captured at that time For bright field images; when The images captured at that time For dark field images, when At this time, the illumination is considered to be matched illumination, and the illumination aperture is... , This is the low-frequency cutoff frequency.

4. The large depth-of-field reconstruction method based on Fourier layered imaging according to claim 1, characterized in that, High-resolution image initial solution Specifically: In the formula, P{} represents bilinear interpolation upsampling. Low-resolution bright-field images captured, where m is the total number of images captured.

5. The large depth-of-field reconstruction method based on Fourier layered imaging according to claim 1, characterized in that, In step three, the initial solution will be... Divided into several layers of complex amplitude objects Each layer is the initial solution for the corresponding layer, and the calculation formula is as follows; In the formula, The number of floors.

6. The large depth-of-field reconstruction method based on Fourier layered imaging according to claim 5, characterized in that, In step four, the complex amplitude object corresponding to each sub-aperture at different defocus positions Specifically: Where n represents the LED number, For the number of floors, Represents the Fourier transform operation. Represents the inverse Fourier transform operation. This indicates a spectrum shift operation, the amount of which is determined by the angle of the tilted light corresponding to each LED. This is a low-pass filter function. , For spatial frequency, For wave number, This is the low-frequency cutoff frequency. The wavelength of the incident light.

7. The large depth-of-field reconstruction method based on Fourier layered imaging according to claim 1, characterized in that, Based on the light intensity distribution of the complex amplitude object corresponding to each sub-aperture in each layer, an update coefficient is set. ; In the formula, n represents the number of the LED bead. For the number of floors, The total number of images captured. For complex amplitude objects with different sub-apertures at different defocus positions, This indicates the conjugate operation. This represents absolute value operations.

8. The large depth-of-field reconstruction method based on Fourier layered imaging according to claim 1, characterized in that, In step seven, the high-resolution results obtained from convergence at different layers are combined to obtain the final image with a large depth of field. Specifically: in, For the updated complex amplitude results of each layer, This indicates the conjugate operation.

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