CN106846383B - High dynamic range image imaging method based on 3D digital microscopic imaging system - Google Patents

Abstract

The invention relates to a high dynamic range image imaging method based on a 3D digital microscopic imaging system. By generating a high dynamic range image of an object to be observed and acquiring an original high dynamic multi-focus sequence image of the sample to be observed, the phase matching method and Fu Lie transform performs image registration and movement at the superpixel level, and then segments the target object through the foreground and background segmentation method; performs quadtree decomposition on the segmented image, marks clear image blocks in the image sequence, and records The height information corresponding to each image; finally, the marked clear image blocks are fused into the three-dimensional shape of the logistics to be observed, and the generated three-dimensional shape is filtered by median filtering to eliminate the sampling frequency of the three-dimensional shape. The jaggies effect caused by the shortage makes the generated three-dimensional three-dimensional formation of the object to be observed smoother.

Description

High dynamic range image imaging method based on 3D digital microscope imaging system

technical field

The invention relates to the technical field of high-definition and high-precision microscopic imaging detection, in particular to a high dynamic range image imaging method based on a 3D digital microscopic imaging system.

Background technique

Multifocal 3D technology (Shape from Focus, SFF for short) is a commonly used 3D technology in the field of digital microscopy image processing. Because the multifocal 3D technology only needs to use the traditional monocular microscope to obtain the three-dimensional shape of the observed sample, it has received extensive attention from experts and scholars. Different from stereo vision technology that uses binocular lens to obtain depth information, multifocal 3D technology only detects clear areas in the image by moving and observing the distance from the object to the lens, so that the depth information of the object can be restored and reconstructed.

However, the main drawback of the multi-focal 3D technology is that when the observed sample has high reflection, due to the insufficient dynamic range of the acquired image, the image details in some areas are insufficient, and even there is no detail in the image in some areas. This greatly affects the accuracy of the reconstructed three-dimensional shape of the object. However, many scientific researches still mainly focus on the effect of focusing factor on the accuracy of 3D shape reconstruction of objects, but ignore the influence of the quality of the original image in terms of dynamic range on the results of 3D shape reconstruction of objects.

In order to overcome the influence of insufficient dynamic range in the obtained images, high dynamic range imaging techniques are proposed. A high dynamic range image (High-Dynamic Range, HDR for short) can be obtained by using a high dynamic range imaging technology. Through calibration, the images of the same scene with different exposure times are fused to obtain the 32-bit high dynamic range light spectrum of the scene. These 32-bit light spectral images can accurately and truly reflect the dynamic range in the scene, and then these 32-bit light spectral images are mapped to 8-bit ordinary images through local tone mapping, so that traditional display devices can display and save these 8 A normal image of bits. However, due to the high computational complexity of high dynamic range imaging technology, the current microscopic 3D reconstruction methods on the market still have limitations in implementing this technology.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide a high dynamic range image imaging method based on a 3D digital microscope imaging system for the above-mentioned prior art. The high dynamic range image imaging method can solve the defect that the existing image imaging method cannot capture high-resolution dynamic scenes, and can simultaneously accurately generate the three-dimensional shape of the object to be observed, thereby providing the observer with all-round 3D stereo vision enjoyment.

The technical solution adopted by the present invention to solve the above technical problems is: a high dynamic range image imaging method based on a 3D digital microscopic imaging system, which is characterized in that it includes the following steps:

Step 1, for the object to be observed on the microscope stage, by adjusting the height of the stage, and using the camera to obtain high dynamic multi-focus images of each layer from the bottom of the object to be observed to the top of the object to be observed, to obtain a three-dimensional image Original high dynamic multi-focus sequence images required for stereo imaging;

Step 2, using the phase matching method to register the obtained original high dynamic multi-focus sequence images, so that the spatial positions, zoom scales and image sizes of the image pairs connected before and after in the original high dynamic multi-focus sequence images correspond to the same, so that Obtain a registered high dynamic multi-focus sequence image;

Step 3, for the registered high dynamic multi-focus sequence images, the foreground and background segmentation method of background accumulation is used to extract the observation sample area that needs to generate three-dimensional stereo;

Step 4, using the quadtree segmentation method to segment the observed sample area, and detect the clear part in each image of the high dynamic multi-focus sequence image, and record the height information corresponding to each image;

In step 5, the clear parts of the detected images are fused to generate a three-dimensional shape of the object to be observed.

Further, the process of using the camera to obtain the high dynamic range image of each layer in the step 1 includes:

(a) calibrating the corresponding curve of the camera; (b) acquiring images for different exposure values in the same scene; (c) using the corresponding curve of the calibrated camera to generate a 32-bit light spectrogram of the scene; (d) ) using local tone mapping to map the 32-bit light spectrogram to an 8-bit ordinary image, and save the ordinary image in a format that can be displayed and stored by a computer.

Further, in step 1, the acquisition process of the original high dynamic multi-focus sequence image includes: first, by moving the height of the stage, changing the distance between the object to be observed and the objective lens of the microscope, so as to achieve different monocular microscopes. Sequence of focal plane images; secondly, record the requirements for the height information of each focal plane image; thirdly, perform focus detection on each focal plane image, and record the pixel with the largest focus definition in each focal plane image points for subsequent 3D shape reconstruction.

Specifically, in step 2, the process of registering the obtained original high dynamic multi-focus sequence images by the phase matching method includes:

First, in the original high dynamic poly-sequence image, for every two consecutive image pairs, each image in the image pair is converted into a grayscale image, thereby obtaining a grayscale image pair;

Secondly, the phase information of each frequency band is extracted from the converted grayscale image pair by using a complex band-pass filter;

Thirdly, utilizing the extracted phase information, realize the movement of the grayscale image pair on the superpixel level through Fourier transform, to ensure the consistency of the positions of the two images connected before and after;

Finally, for each set of image pairs in the original HDR image sequence, the process is repeated until the scale and displacement of all images in the HDR image sequence remain consistent.

Specifically, in the step 4, the process of using the quadtree segmentation method to divide the observed sample area includes:

First, the original high dynamic multi-focus sequence image is input into the quadtree as a layer of the quadtree root;

Secondly, set the image decomposition conditions, and process according to whether the images of each layer in the quadtree meet the decomposition conditions:

If the image decomposition condition is satisfied for a layer of images, the image of this layer is decomposed by quadratic decomposition and input to the next layer of the quadtree; and so on, until the minimum image blocks obtained by decomposing the image sequence are not satisfied The described image decomposition conditions, then end the quadtree decomposition process; wherein, the set image decomposition conditions are:

For the decomposed image blocks of each layer in the image sequence in the quadtree, calculate the maximum difference value MDFM of the focus factor and the difference value SMDG of the gradient respectively.

_MDFM =FMmax- _FMmin ;

Among them, FM _max represents the maximum value of focal length measurement, FM _min represents the minimum value of focal length measurement; grad _max (x, y) represents the maximum gradient value, and grad _min (x, y) represents the minimum gradient value;

For a layer of image blocks in the quadtree, if MDFM≥0.98×SMDG is satisfied, and there is a fully focused image block in the image sequence of this layer, the image block of this layer will not continue to be decomposed downward; Blocks will continue to be decomposed until all images in the quadtree have been decomposed into sub-image blocks that cannot be decomposed.

Specifically, the acquisition process of the maximum value FM _max of the focal length measurement and the minimum value FM _min of the focal length measurement is:

First, calculate the gradient matrix of each pixel in a layer of images at the root of the quadtree. The calculation formula is:

GM _i =gradient(I _i ), i=1,2,...,n;

Wherein, I _i is the i-th original high dynamic multi-focus image, GM _i is the gradient matrix corresponding to I _i ; n is the total number of images in the original high dynamic multi-focus sequence image;

Second, find the largest gradient matrix and the smallest gradient matrix among all gradient matrices at each point of the image in this layer, the formula is as follows:

GM _max =max(GM _i (x,y)), i=1,2,...,n;

GM _min =min(GM _i (x,y)), i=1,2,...,n;

Again, calculate the sum of the gradient matrices of all points in the image of this layer. The calculation formula is as follows:

FM _i = Σ _x Σ _y grad _i (x, y), i = 1, 2, ..., n;

Finally, find the maximum and minimum values of the sum of the above gradient matrices respectively. The calculation formula is as follows:

FM _max =max{FM _i },i=1,2,...,n;

FM _min =min{FM _i }, i=1,2,...,n.

Specifically, the process of fusing the clear parts of each image in the step 5 includes: taking all the clear parts obtained as clear sub-image blocks, recording their height information respectively, and fusing all the clear sub-image blocks into a three-dimensional stereo image of a complete observation sample.

Improved, the step 5 further includes: filtering the generated three-dimensional three-dimensional shape using a median filtering method to eliminate the aliasing effect of the three-dimensional three-dimensional shape caused by insufficient sampling frequency, thereby making the generated three-dimensional three-dimensional shape more smooth.

Compared with the prior art, the advantages of the present invention are:

First, the high dynamic range image imaging method provided by the present invention adopts high dynamic range imaging, three-dimensional stereo imaging and multi-depth-of-field image fusion technology, acquires image sequences of the same scene with different exposure times at the same time, and generates a 32-bit light spectrum of the scene Then the 32-bit light spectrogram is mapped to an 8-bit ordinary image using local tone mapping, and saved into a high dynamic range video format that can be displayed and stored by a computer. Using tone mapping technology, high dynamic range video is displayed and transmitted in real time. Micro video, which is convenient for the observer to dynamically watch the object to be observed in real time;

Secondly, due to the high computational complexity involved in the application of high dynamic range video technology, the present invention adopts methods such as phase matching and quadtree segmentation, which can process video signals in real time and generate real-time microscopic video display, thereby reducing computational complexity. Spend;

Thirdly, the high dynamic range image imaging method of the present invention can observe the object to be observed in real time and high definition, which overcomes the defect that the current image imaging technology cannot see the reflective and non-reflective areas at the same time for high-contrast samples;

Finally, the high dynamic range image imaging method of the present invention can obtain a fully focused image composed of images with different focal points; in the process of processing the images with different focal points, the depth of the midpoint of the image is automatically obtained, so as to restore the image on the surface of the image. The three-dimensional coordinates of the point provide a strong auxiliary guarantee for the quality inspection of emerging materials.

Description of drawings

1 is a schematic flowchart of a high dynamic range image imaging method based on a 3D digital microscope imaging system in Embodiment 1 of the present invention;

2 is a schematic diagram of a 3D digital microscope imaging system in Embodiment 1 of the present invention;

Fig. 3 is the original high dynamic multi-focus sequence image corresponding to the metal screw in the first embodiment;

4 is a comparison diagram of a high dynamic range image of a metal screw obtained in Embodiment 1 and an ordinary automatic exposure image; wherein, the left column is a corresponding high dynamic range image, and the right column is a corresponding ordinary automatic exposure image;

5 is a schematic diagram of extracting a foreground image in Embodiment 1;

6a is a 3D stereoscopic image without image texture mapping generated by using a high dynamic range image in the first embodiment;

6b is a 3D stereoscopic image with image texture mapping generated by using a high dynamic range image in Embodiment 1;

6c is a 3D stereoscopic image without image texture mapping generated by using the original automatic exposure image in the first embodiment;

6d is a 3D stereoscopic image with image texture mapping generated by using the original automatic exposure image in the first embodiment;

6e is a ground truth map of a 3D stereoscopic image without image texture mapping in the first embodiment;

Figure 6f is a ground truth map of a 3D stereoscopic image with image texture mapping in the first embodiment;

7a is a 3D stereoscopic image without image texture mapping generated by using a high dynamic range image in the second embodiment;

7b is a 3D stereoscopic image with image texture mapping generated by using a high dynamic range image in the second embodiment;

Figure 7c is a 3D stereoscopic image without image texture mapping generated by using the original automatic exposure image in the second embodiment;

7d is a 3D stereo image with image texture mapping generated by using the original automatic exposure image in the second embodiment;

7e is a ground truth map of a 3D stereoscopic image without image texture mapping in the second embodiment;

Fig. 7f is the ground truth map of the 3D stereo image with image texture mapping in the second embodiment;

8 is a comparison diagram of the square root error of the method for generating a 3D three-dimensional shape using a high dynamic range image and without using a high dynamic range image in the second embodiment;

9a is a 3D stereo image without image texture mapping generated by using a high dynamic range image in the third embodiment;

9b is a 3D stereoscopic image with image texture mapping generated by using a high dynamic range image in the third embodiment;

Figure 9c is a 3D stereo image without image texture mapping generated by using the original automatic exposure image in the third embodiment;

9d is a 3D stereoscopic image with image texture mapping generated by using the original automatic exposure image in the third embodiment;

Figure 9e is a ground truth map of a 3D stereoscopic image without image texture mapping in the third embodiment;

Figure 9f is a ground truth map of a 3D stereoscopic image with image texture mapping in the third embodiment;

FIG. 10 is a comparison diagram of the square root error corresponding to the 3D stereoscopic image generated by the method of generating a 3D stereoscopic shape from a high dynamic range image and the method of generating a 3D stereoscopic shape from an original automatic exposure image.

Detailed ways

The present invention will be further described in detail below with reference to the embodiments of the accompanying drawings.

Example 1

As shown in Figure 2, the 3D digital microscopic imaging system used in the first embodiment includes a traditional optical microscope, an automatic stage that can move in any direction of the X-axis, Y-axis and Z-axis, a CMOS camera and computer. Wherein, the object to be observed in the first embodiment is a metal screw, and the metal screw is placed on the automatic stage. Referring to Fig. 1, the high dynamic range image imaging method based on the 3D digital microscope imaging system in the first embodiment includes the following steps:

Step 1: For the object to be observed on the microscope stage, that is, the metal screw, adjust the height of the stage to make the CMOS camera focus on each level of the object to be observed, that is, on each level of the metal screw, And use the camera to obtain the high dynamic multi-focus image of each layer from the bottom of the metal screw to the top of the metal screw to obtain the original high-dynamic multi-focus sequence image required for 3D stereo imaging; the original high-dynamic multi-focus sequence image for metal screws Referring to Fig. 3; wherein, the process of acquiring the high dynamic range multi-focus image of the object to be observed includes two processes of high dynamic range image acquisition and multi-focus image acquisition; specifically, acquiring the high dynamic range image of each layer of the object to be observed The process includes:

(a) calibrating the corresponding curve of the camera; (b) obtaining images for different exposure values in the same scene; (c) using the corresponding curve of the calibrated camera to generate a 32-bit light spectrogram of the scene; (d) using the local tone Map, map the 32-bit light spectrogram to an 8-bit normal image, and save the normal image to a format that the computer can display and store. The high dynamic range image of each layer corresponding to the metal screw is shown in the left column in Figure 4;

Step 2, using the phase matching method to register the obtained original high dynamic multi-focus sequence images, so that the spatial positions, zoom scales and image sizes of the image pairs connected before and after in the original high dynamic multi-focus sequence images correspond to the same, so as to obtain matching images. The calibrated high dynamic multi-focus sequence image; wherein, the process of registering the obtained original high dynamic multi-focus sequence image by the phase matching method includes:

First of all, in the original high dynamic poly-sequence image, for every two consecutive image pairs, each image in the image pair is converted into a grayscale image, thereby obtaining a grayscale image pair;

Secondly, the phase information of each frequency band is extracted from the converted grayscale image pair by using a complex band-pass filter;

Thirdly, using the extracted phase information, the movement of the gray-scale image pair on the superpixel level is realized by Fourier transform, so as to ensure the consistency of the positions of the two images connected before and after;

Finally, for each set of image pairs in the original HDR image sequence, the process is repeated until the scale and displacement of all images in the HDR image sequence remain consistent.

Step 3, for the registered high-dynamic multi-focus sequence images, the foreground and background segmentation method of background accumulation is used to extract the observation sample area that needs to generate a three-dimensional three-dimensional image; as shown in Figure 5, that is, using the difference between frames, the corresponding metal screws are divided. The background is extracted, and then threshold segmentation is performed to obtain the foreground image;

Step 4, adopting the quadtree segmentation method to segment the observed sample area, and detecting the clear part in each image of the high dynamic multi-focus sequence image, and recording the height information corresponding to each image; wherein,

The quadtree segmentation method in the first embodiment is described as follows:

First, the original high dynamic multi-focus sequence image is input into the quadtree as a layer of the quadtree root;

Secondly, set the image decomposition conditions, and process according to whether the images of each layer in the quadtree meet the decomposition conditions:

If the image decomposition condition is satisfied for a layer of images, then the image of this layer is decomposed by quad, and input to the next layer of the quad tree; and so on, until the minimum image block obtained by the decomposed image sequence does not satisfy the image decomposition condition, then end the quadtree decomposition process; wherein, the image decomposition conditions are described as follows:

For the decomposed image blocks of each layer in the image sequence in the quadtree, calculate the maximum difference value MDFM of the focus factor and the difference value SMDG of the gradient respectively.

_MDFM =FMmax- _FMmin ;

Among them, FM _max represents the maximum value of focal length measurement, FM _min represents the minimum value of focal length measurement; grad _max (x, y) represents the maximum gradient value, and grad _min (x, y) represents the minimum gradient value; the maximum value for focal length measurement The calculation of FM _max and the minimum value FM _min of focal length measurement is:

First, calculate the gradient matrix of each pixel in a layer of images at the root of the quadtree. The calculation formula is:

GM _i =gradient(I _i ),i=1,2,...,n;

Wherein, I _i is the i-th original high dynamic multi-focus image, GM _i is the gradient matrix corresponding to I _i ; n is the total number of images in the original high dynamic multi-focus sequence image;

Second, find the largest gradient matrix and the smallest gradient matrix among all gradient matrices at each point of the image in this layer, the formula is as follows:

GM _max =max(GM _i (x,y)), i=1,2,...,n;

GM _min =min(GM _i (x,y)), i=1,2,...,n;

Again, calculate the sum of the gradient matrices of all points in the image of this layer. The calculation formula is as follows:

FM _i =Σ _x Σ _y grad _i (x,y),i=1,2,...,n;

Finally, find the maximum and minimum values of the sum of the above gradient matrices respectively. The calculation formula is as follows:

FM _max =max{FM _i },i=1,2,...,n; FM _min =min{FM _i },i=1,2,...,n.

The process of detecting the clear parts in each image of the high dynamic multi-focus sequence image is described as follows:

For each image block sequence in the quadtree, find an image block with the largest gradient matrix in the image block sequence, and record the position and height information of the image block with the largest gradient matrix in the image sequence;

i＝1,2,…,n；其中，fm_i(x,y)表示图像序列中第i张图像的梯度矩阵。

i=1,2,...,n; where, fm _i (x, y) represents the gradient matrix of the ith image in the image sequence.

Step 5: Fusing the clear parts of the detected images to generate a three-dimensional shape of the object to be observed, that is, a three-dimensional three-dimensional image of the metal screw; wherein, the three-dimensional image corresponding to the metal screw is set to be marked as Z :

Z(x, y)= _zi (x, y), where _zi (x, y) represents the ith clear image block in the image sequence.

In order to compare the traditional method of using the original automatic exposure image to generate a 3D three-dimensional shape with the method of using a high dynamic range image to generate a 3D three-dimensional shape in the present invention, the present embodiment 1 shows that the metal screws are generated by using the above two 3D three-dimensional shape methods respectively. The comparison diagram corresponding to the stereoscopic image is shown in FIG. 6 for details. In the present invention, the use of the original automatic exposure image technology is denoted as Normal SFF, and the use of the high dynamic range image technology is denoted as HDR-SFF. in:

In order to compare the accuracy of the above two 3D three-dimensional shape generation methods, the first embodiment of the present invention introduces the square root error to measure the difference between the two 3D three-dimensional shape generation methods and the true value under the same conditions:

Among them, G _T (i, j) represents the true value, and Z (i, j) represents the Normal SFF or HDR-SFF value.

Table 1 shows the square root error of the two 3D stereo shape generation methods using 22 different focusing factors. By comparing the results in Table 1, it can be seen that for the same focus factor, the square root error value of the focus factor corresponding to the 3D stereo shape generated by the high dynamic range image is smaller than the focus corresponding to the 3D stereo shape generated without the high dynamic range image. Factor square root error value. The results in Table 1 show that the 3D volumetric shapes generated using the high dynamic range images of the present invention are more accurate than the 3D volumetric shapes generated without using the high dynamic range images.

Table 1

Embodiment 2

In the second embodiment, a bank card made of plastic material is used as the object to be observed, and the bank card has a lowercase English letter "d". Wherein, the steps of generating the three-dimensional three-dimensional image of the bank card are the same as the steps of generating the three-dimensional three-dimensional image of the metal screw in the first embodiment, and will not be repeated here.

In the second embodiment, in order to verify the accuracy and robustness of the high dynamic range image imaging method in the present invention, the second embodiment provides the corresponding high dynamic range image generated by the bank card, see FIG. 7a to FIG. 7 for details. 7f. FIG. 8 is a comparison diagram of the square root error of a method for generating a 3D three-dimensional shape by using a high dynamic range image and without using a high dynamic range image for the bank card in the second embodiment.

It can be seen from Figure 8 that for the same focus factor, the square root error value of the focus factor corresponding to the 3D stereo shape generated by the high dynamic range image is smaller than the square root error value of the focus factor corresponding to the 3D stereo shape generated without the high dynamic range image. . It can be seen that the 3D stereoscopic shape generated using the high dynamic range image in the present invention is more accurate than the 3D stereoscopic shape generated without using the high dynamic range image.

Embodiment 3

In the third embodiment, a metal chip is used as the object to be observed. Wherein, the steps for generating the three-dimensional stereoscopic image of the metal chip are the same as the steps for generating the three-dimensional stereoscopic image of the metal screw in the first embodiment, and are not repeated here.

FIG. 10 is a comparison diagram of the square root error corresponding to the 3D stereoscopic image generated by the method of generating a 3D stereoscopic shape from a high dynamic range image and the method of generating a 3D stereoscopic shape from an original automatic exposure image.

It can be seen from Figure 10 that for the same focus factor, the square root error value of the focus factor corresponding to the 3D stereo shape generated by the high dynamic range image is smaller than the square root error value of the focus factor corresponding to the 3D stereo shape generated without the high dynamic range image. . It can be seen that the 3D stereoscopic shape generated using the high dynamic range image in the present invention is more accurate than the 3D stereoscopic shape generated without using the high dynamic range image.

Claims (7)

1. the high dynamic range image imaging method based on 3D digital microscopic imaging system, is characterized in that, comprises the steps: Step 1, for the object to be observed on the microscope stage, by adjusting the height of the stage, and using the camera to obtain high dynamic multi-focus images of each layer from the bottom of the object to be observed to the top of the object to be observed, to obtain a three-dimensional image The original high dynamic range multi-focus sequence image required for stereo imaging; wherein the process of using the camera to obtain the high dynamic range image of each slice includes: (a) calibrating the corresponding curve of the camera; (b) acquiring images for different exposure values in the same scene; (c) using the corresponding curve of the calibrated camera to generate a 32-bit light spectrogram of the scene; (d) ) using local tone mapping to map the 32-bit light spectrogram to an 8-bit common image, and save the common image as a format that a computer can display and store; Step 2, using the phase matching method to register the obtained original high dynamic multi-focus sequence images, so that the spatial positions, zoom scales and image sizes of the image pairs connected before and after in the original high dynamic multi-focus sequence images correspond to the same, so that Obtain a registered high dynamic multi-focus sequence image; Step 3, for the registered high dynamic multi-focus sequence images, the foreground and background segmentation method of background accumulation is used to extract the observation sample area that needs to generate three-dimensional stereo; Step 4, using the quadtree segmentation method to segment the observed sample area, and detect the clear part in each image of the high dynamic multi-focus sequence image, and record the height information corresponding to each image; In step 5, the clear parts of the detected images are fused to generate a three-dimensional shape of the object to be observed. 2. The high dynamic range image imaging method according to claim 1, wherein in step 1, the process of obtaining the original high dynamic multi-focus sequence image comprises: In step 1, first, by moving the height of the stage, the distance between the object to be observed and the objective lens of the microscope is changed to realize the sequence of images of different focal planes of the monocular microscope; secondly, the height information of each focal plane image is recorded. Requirements; again, perform focus detection on each focal plane image, and record the pixel points with the maximum focal resolution in each focal plane image for subsequent three-dimensional shape reconstruction. 3. The high dynamic range image imaging method according to claim 1, wherein in step 2, the process of registering the obtained original high dynamic range multi-focus sequence images by the phase matching method comprises: First, in the original high dynamic poly-sequence image, for every two consecutive image pairs, each image in the image pair is converted into a grayscale image, thereby obtaining a grayscale image pair; Secondly, the phase information of each frequency band is extracted from the converted grayscale image pair by using a complex band-pass filter; Thirdly, utilizing the extracted phase information, realize the movement of the grayscale image pair on the superpixel level through Fourier transform, to ensure the consistency of the positions of the two images connected before and after; Finally, for each set of image pairs in the original HDR image sequence, the process is repeated until the scale and displacement of all images in the HDR image sequence remain consistent. 4. The high dynamic range image imaging method according to claim 1, wherein the process of adopting the quadtree segmentation method to divide the observed sample area in the step 4 comprises: First, the original high dynamic multi-focus sequence image is input into the quadtree as a layer of the quadtree root; Secondly, set the image decomposition conditions, and process according to whether the images of each layer in the quadtree meet the decomposition conditions: If the image decomposition condition is satisfied for a layer of images, the image of this layer is decomposed by quadratic decomposition and input to the next layer of the quadtree; and so on, until the minimum image blocks obtained by decomposing the image sequence are not satisfied The described image decomposition conditions, then end the quadtree decomposition process; wherein, the set image decomposition conditions are: For the decomposed image blocks of each layer in the image sequence in the quadtree, calculate the maximum difference value MDFM of the focus factor and the difference value SMDG of the gradient respectively. _MDFM =FMmax- _FMmin ;

Among them, FM _max represents the maximum value of focal length measurement, FM _min represents the minimum value of focal length measurement; grad _max (x, y) represents the maximum gradient value, and grad _min (x, y) represents the minimum gradient value; For a layer of image blocks in the quadtree, if MDFM≥0.98×SMDG is satisfied, and there is a fully focused image block in the image sequence of this layer, the image block of this layer will not continue to be decomposed downward; Blocks will continue to be decomposed until all images in the quadtree have been decomposed into sub-image blocks that cannot be decomposed. 5. The high dynamic range image imaging method according to claim 4, wherein the acquisition process of the maximum value FM _max of the focal length measurement and the minimum value FM _min of the focal length measurement is: First, calculate the gradient matrix of each pixel in a layer of images at the root of the quadtree. The calculation formula is: GM _i =gradient(I _i ),i=1,2,...,n; Wherein, I _i is the i-th original high dynamic multi-focus image, GM _i is the gradient matrix corresponding to I _i ; n is the total number of images in the original high dynamic multi-focus sequence image; Second, find the largest gradient matrix and the smallest gradient matrix among all gradient matrices at each point of the image in this layer, the formula is as follows: GM _max =max(GM _i (x,y)), i=1,2,...,n; GM _min =min(GM _i (x,y)), i=1,2,...,n; Again, calculate the sum of the gradient matrices of all points in the image of this layer. The calculation formula is as follows: FM _i =∑ _x ∑ _y grad _i (x,y),i=1,2,...,n; Finally, find the maximum and minimum values of the sum of the above gradient matrices respectively. The calculation formula is as follows: FM _max =max{FM _i },i=1,2,...,n; FM _min =min{FM _i }, i=1,2,...,n. 6 . The high dynamic range image imaging method according to claim 5 , wherein the process of fusing the clear parts of each image in the step 5 comprises: taking all the clear parts obtained as clear sub-image blocks. 7 . , respectively record its height information, and fuse all the clear sub-image blocks into a complete 3D stereo image of the observed sample. 7. The high dynamic range image imaging method according to claim 1, wherein the step 5 further comprises: using a median filtering method to filter the generated three-dimensional three-dimensional shape, so as to eliminate the sampling frequency of the three-dimensional three-dimensional shape due to the sampling frequency. The aliasing effect caused by the shortage makes the generated three-dimensional solid form smoother.

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