CN110286464B - Automatic focusing method based on area criterion - Google Patents
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Abstract
The invention discloses an automatic focusing method based on an area criterion, which comprises the following steps: (1) acquiring a holographic image of multi-focal-length plankton; (2) carrying out image segmentation on the hologram, and extracting a sub-hologram of each plankton; (3) and sequentially applying an automatic focusing method based on an area criterion to the sub-holograms to obtain the focal length of each plankton. According to the invention, the accuracy of plankton focal length detection is effectively improved according to the principle that the focused image has a smaller area than the blurred image, and the method is simple in calculation and strong in practicability.
Description
Technical Field
The invention relates to the field of digital holographic microscopic imaging of multi-focal-length plankton, in particular to an automatic focusing method based on an area criterion.
Background
The Digital Holographic Microscopy (DHM) imaging technology can record light wave field information of the whole monitoring area in a plane Holographic image, and reconstruct images of planktons at different depths in the monitoring area through a numerical reconstruction algorithm, but only when the reconstruction distance is equal to the depth (focal length) of the planktons, the planktons in the reconstructed image can form a clear image, otherwise, a blurred image is formed. In order to find the focal distance of plankton in the holographic image, researchers have proposed many auto-focusing methods, which are mostly based on the sharpness criterion of the image, and include two aspects: one is that sharp images have larger gradients than blurred images, including the laplacian gradient method (LAP), the border gradient method (CG); the second is that sharp images have more high frequency components than blurred images, including Fourier Transform (FT), Wavelet transform (Wavelet), and Discrete Cosine Transform (DCT).
The automatic focusing methods have good effect on the traditional optical microscope image or the holographic image of only one target, but cannot accurately find the focal length of each plankton in the holographic image of the multi-focal-length plankton. The main reasons are: 1. in a traditional optical microscope image, the whole image becomes clear or fuzzy simultaneously along with the lifting of an objective lens, and an extreme point is easy to obtain when an automatic focusing method is applied. In the multi-focal-distance plankton holographic image, along with the change of the reconstruction distance, some planktons become clear, and some planktons become fuzzy, so that the contribution of the clear planktons to the calculation result is counteracted by the fuzzy planktons, and the extreme point does not exist or is not obvious; 2. coherent noise exists in the holographic image, a large amount of high-frequency components are carried, and huge interference is generated on a calculation result; 3. the size of plankton is mostly between tens of microns to hundreds of microns, and is small relative to the size of the whole holographic image, and the influence on the calculation result is weak. Therefore, an automatic focusing method suitable for multi-focus plankton holographic images is needed, which can eliminate the mutual interference between planktons and the influence of coherent noise, and accurately find the focal length of each plankton.
Disclosure of Invention
The invention provides an automatic focusing method based on an area criterion, which effectively improves the accuracy of plankton focal length detection according to the principle that a focused image has a smaller area than a blurred image, and has simple calculation and strong practicability.
An area criterion based autofocus method comprising the steps of:
(1) acquiring a holographic image of multi-focal-length plankton;
(2) carrying out image segmentation on the holographic image in the step (1), obtaining the boundary and pixel coordinates of each plankton, and extracting a sub-holographic image of each plankton;
(3) sequentially applying an automatic focusing algorithm based on an area criterion to the sub-hologram image of each plankton in the step (2) to obtain the optimal reconstruction distance (focal length) of each plankton, wherein the specific process is as follows:
3-1, sequentially taking out a reconstruction distance in a reconstruction distance interval according to a given step length, and reconstructing a reconstruction image corresponding to the sub-hologram of the plankton at the distance by using an angular spectrum reconstruction algorithm;
3-2, eliminating background noise in the reconstructed image of each plankton through a preset gray threshold, and binarizing the image;
3-3, filtering scattered small connected regions in the binary image in the 3-2 by a preset area threshold, and only reserving one large plankton connected region;
3-4, counting the area of the plankton connected region reserved in 3-3 as the value of the reconstruction distance in 3-1 by the automatic focusing method;
the method specifically comprises the following steps: and comparing the areas of the maximum plankton communicating regions obtained at the reconstruction distances, and taking the reconstruction distance corresponding to the minimum area as the focal length.
In the multi-focal-length plankton holographic image in the step (1), the change of each plankton is not consistent with the change of the reconstruction distance, some plankton becomes clear, and some plankton becomes fuzzy. Therefore, their influence on the calculation results of the autofocus method is also inconsistent. In order to eliminate the mutual interference among the plankton and the influence of the coherent noise on the calculation result, the holographic image needs to be divided, and a sub-holographic image containing only a single plankton is extracted. The focus of each plankton is obtained one by applying the automatic focusing method to only one sub-holographic image each time. In the step (2), the extraction method of the sub-hologram image only containing a single plankton is as follows:
2-1, carrying out non-local mean (NLM) filtering on the holographic image in the step 1, and filtering out coherent noise in the holographic image;
2-2, setting a reasonable gray threshold value for the holographic image subjected to filtering processing in the step 2-1, separating plankton from the background, and filtering the background;
2-3, carrying out binarization on the holographic image with the background filtered in the step 2-2 to obtain a binary image;
2-4, setting reasonable upper limit and lower limit of area, filtering out a connected region with the area smaller than the lower limit and a connected region with the area larger than the upper limit in the binary image in the step 2-3, and filtering out partial impurities in the seawater;
2-5, calculating the long axis and the short axis of the connected region in the binary image after the impurities are filtered in the step 2-4, removing the connected region with the ratio of the long axis to the short axis being less than 2, and filtering non-plankton particles in the seawater;
2-6, performing expansion and hole filling operations on the binary image subjected to particle filtering in the step 2-5;
2-7, solving the boundary of the connected region (plankton) of the binary image subjected to expansion and hole filling in the step 2-6, and drawing the boundary of the plankton in the step (1);
2-8, marking the connected regions of the binary image which is subjected to expansion and hole filling in the step 2-6, and storing each connected regionThe pixel coordinates of the region (plankton), denoted as rnAnd cnWherein the subscript n represents the nth plankton, rnAnd cnThe line coordinates and the column coordinates respectively correspond to the nth plankton, and are column vectors, and the length of the vectors is equal to the number of pixels forming the plankton;
2-9, solving the length (a) and the width (b) of the equivalent minimum circumscribed rectangle of each connected region (plankton) of the binary image subjected to expansion and hole filling in the step 2-6, and the vertex coordinate (r) at the upper left corner0,c0)；
2-10, obtaining a sub-hologram image corresponding to each plankton through the formula (1),
Bn(rn-r0+1,cn-c0+1)＝A(rn,cn) (1)
wherein, BnRepresents a sub-hologram corresponding to the nth plankton, BnA represents the hologram image in step (1).
The invention has the beneficial effects that:
the automatic focusing method eliminates the influence of mutual interference and coherent noise between plankton by extracting the sub-hologram image of each plankton. The principle that a clear image has a smaller area than a blurred image is utilized, and an area criterion is introduced, so that the accuracy of plankton focal length detection is improved.
Drawings
Fig. 1 is a flowchart of an auto-focusing method based on an area criterion.
FIG. 2 is a digital holographic microscopic imaging experiment platform;
FIG. 3 is a holographic image of multi-focal plankton.
FIG. 4 shows the results of several auto-focusing methods for focus detection of the whole holographic image.
Fig. 5 is a holographic image of multi-focal plankton after NLM filtering.
Fig. 6 is a holographic image of multi-focal plankton after background filtering.
FIG. 7 shows the result of the holographic image binarization of the multi-focal-length plankton.
Fig. 8 is a binary image after removing impurities.
Fig. 9 is a binary image from which non-plankton particles are removed.
Fig. 10 is a binary image after expansion and hole filling.
FIG. 11 is a boundary of multi-focal plankton.
FIG. 12 is the equivalent minimum bounding rectangle of plankton and its top left corner vertex.
Fig. 13 is a sub-hologram image of each extracted plankton.
Fig. 14 shows the results of applying the area-based autofocus method to the 6 th plankton, (a), (b), (c) reconstructed images of the sub-hologram of the 6 th plankton at reconstruction distances of 8.600mm,8.620mm, and 8.664mm, respectively, and d), (e), (f) binary images of (a), (b), and (c), respectively, and (g) a focus measurement curve.
Fig. 15 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the sub-hologram image of the 1 st plankton.
Fig. 16 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the sub-hologram image of the 2 nd plankton.
Fig. 17 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the 3 rd plankton sub-hologram.
Fig. 18 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the sub-hologram image of the 4 th plankton.
Fig. 19 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the sub-hologram image of the 5 th plankton.
Fig. 20 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the sub-hologram image of the 6 th plankton.
Fig. 21 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the sub-hologram image of the 7 th plankton.
Fig. 22 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the 8 th plankton sub-hologram.
Fig. 23 is a comparison of the results of the area criteria based auto-focusing method and several common auto-focusing methods applied to the sub-hologram image of the 9 th plankton.
Detailed Description
The present invention will be described in detail with reference to the following examples and drawings, but the present invention is not limited thereto.
In the embodiment, the holographic image of the multi-focal-distance plankton is obtained by shooting through a digital holographic microscopic imaging system set up in a laboratory, all algorithms are written in the environment of Matlab2018a, and the hardware conditions of the operation of the algorithms are a core i5 processor, a master frequency of 2.6GHz and a memory of 8 GB.
The flowchart of the auto-focusing method based on the area criterion according to the embodiment is shown in fig. 1, and includes the following steps:
(1) the holographic image of the multi-focal-distance plankton is obtained by shooting through a digital holographic microscopic imaging system built in a laboratory, the digital holographic microscopic imaging system built in the laboratory is shown in figure 2, and the holographic image of the multi-focal-distance plankton is obtained by shooting is shown in figure 3.
Because the holographic image of the multi-focus plankton is affected by the mutual interference and coherent noise among the planktons when the focal length is detected, the focal length of each plankton is difficult to accurately find. Fig. 4 is a calculation result of focus detection performed on the whole hologram image by several automatic focusing methods, and it is difficult to distinguish the minimum value corresponding to each plankton in the result.
(2) Carrying out image segmentation on the holographic image of the multi-focus plankton in the step (1), and extracting the sub-holographic image of each plankton, wherein the specific method comprises the following steps:
2-1, performing NLM filtering on the holographic image of the multi-focus plankton in the step (1), setting the weight to be 50, the size of a search window to be 21, the size of a filtering window to be 9, and obtaining a filtering result as shown in FIG. 5;
2-2, performing threshold segmentation on the holographic image filtered in the step 2-1, filtering a background, wherein the threshold is set to be 0.6, and the holographic image after the background is filtered is shown in fig. 6;
2-3, carrying out binarization on the holographic image with the background filtered in the step 2-2 to obtain a binary image as shown in figure 7;
2-4, setting the lower limit of the area to be 900 and the upper limit of the area to be 2400 in the binary image obtained in the step 2-3, filtering a connected region with the area smaller than the lower limit and the area larger than the upper limit, removing impurities, and obtaining a result after removing the impurities as shown in fig. 8;
2-5, calculating the long axis and the short axis of the connected region in the binary image after the impurities are filtered in the step 2-4, removing the connected region with the ratio of the long axis to the short axis being less than 2, and filtering non-plankton particles in the seawater, wherein the result is shown in fig. 9;
2-6, performing expansion and hole filling operations on the binary image subjected to particle filtering in the step 2-5, and obtaining a result shown in fig. 10;
2-7, solving the boundary of the connected region (plankton) of the binary image subjected to expansion and hole filling in the step 2-6 by utilizing a bwbuildings function of Matlab, wherein FIG. 11 is the boundary of multi-focal-length plankton;
2-8, labeling connected regions of the binary image subjected to expansion and hole filling in the step 2-7 by using a Matlab's bwleael function, and storing a pixel coordinate label r of each connected region (plankton)nAnd cnWherein the subscript n represents the nth plankton, rnAnd cnThe line coordinates and the column coordinates respectively correspond to the nth plankton, and are column vectors, and the length of the vectors is equal to the number of pixels forming the plankton;
2-9, solving the length (a) and the width (b) of the equivalent minimum circumscribed rectangle of each connected region (plankton) of the binary image subjected to expansion and hole filling in the step 2-7 and coordinates (r) of the top left vertex of the connected region by utilizing a regionprops function of Matlab0,c0) As shown in fig. 12;
2-10, obtaining a sub-hologram image corresponding to each plankton through the formula (1),
Bn(rn-r0+1,cn-c0+1)＝A(rn,cn) (1)
wherein, BnRepresents a sub-hologram corresponding to the nth plankton, BnA represents the hologram image in step (1), and fig. 13 is a sub-hologram image of each extracted plankton.
(3) The area criterion-based automatic focusing method is respectively applied to the sub-hologram images of each plankton extracted in the step (2), and the specific process is as follows:
3-1, sequentially taking out a reconstruction distance in a reconstruction distance interval according to a given step length, and reconstructing a reconstruction image corresponding to the sub-hologram of the plankton at the distance by using an angular spectrum reconstruction algorithm;
the results of the auto-focus method do not show strict monotonicity as the offset (defocus) between the reconstruction distance and the plankton focus increases over the entire depth range (0-15 mm). Therefore, when determining the reconstruction distance interval of each plankton, the global minimum point is first searched by a large step size (10um), and then a certain distance is extended to both sides by a small step size (1um) near the minimum point as the reconstruction distance interval of the plankton. For example, for the 6 th plankton, (8,600-8.670mm) is searched for the global minimum at 8.62mm, and (8,600-8.670mm) is taken as the reconstruction distance interval, and the auto-focusing method sequentially takes out one reconstruction distance at the interval by 1um as the step length for calculation, and (a), (b), and (c) in fig. 14 are respectively the reconstruction images of the 6 th plankton at 8.600mm,8.620mm, and 8.670 mm.
3-2, setting a gray threshold value of 0.4 in the reconstructed image of each plankton, filtering background noise, and binarizing the image;
3-3, filtering scattered small connected regions in the binary image in the 3-2 by a preset area threshold, and only reserving one large plankton connected region, wherein (d), (e) and (f) in fig. 14 are binary images of (a), (b) and (c) in fig. 14 respectively, and the scattered small connected regions are filtered;
3-4, the area of the plankton connected region retained in 3-3 is counted as the value of the auto-focusing method at the reconstruction distance in 3-1, for example, for the 6 th plankton, the areas at the reconstruction distances 8.600mm,8.620mm, 8.670mm are 828, 805, 831, respectively, and (g) in fig. 14 is the focal length detection result in the reconstruction distance section.
FIGS. 15-23 show the results of the focus detection of each plankton extracted by the method of the present invention and several autofocus methods, respectively, and all curves were fitted using the smoothspline in Matlab for convenient analysis. As can be seen from the figure, the method can detect the focal distance of each plankton, and other methods cannot identify the focal distance of part of plankton. Table 1 shows the focal length (in mm) of each plankton detected by various auto-focusing methods, where 'Area' indicates the method of the present invention and the symbol '-' indicates that the method cannot detect the focal length of the corresponding plankton.
TABLE 1
To better contrast the various autofocus algorithms, accuracy and resolution criteria were introduced for evaluation. Accuracy (AM) is defined as:
AM＝|z+e-z-e| (2)
z in the formula (2)+eAnd Z－eThe uncertainty e is set to 2% for the reconstruction distances on both sides of the corresponding focal length when the autofocus calculation result rises to e times the minimum value, respectively.
The Resolution (RM) reflects the global distribution of the calculation results, given by equation (2),
in the formula (3), z represents a reconstruction distance, zfRepresenting the focal length, F (z) representing the calculation result of the auto-focusing method at z, | F | | calculation of z | | L calculation of2Is represented by2And (4) norm.
The accuracy and resolution of the various autofocus methods are shown in tables 2 and 3 (in mm), respectively:
TABLE 2
TABLE 3
As can be seen from the data analysis in tables 2 and 3, the average accuracy of the method of the invention is 0.010mm, which is 83%, 10%, 29%, 23% and 23% smaller than that of the LAP, FT, CG, Wavelet and DCT methods, respectively, and the detection accuracy is improved. Furthermore, some of the results of the method of the present invention are also superior to other methods in terms of resolution.
The above description is only exemplary of the preferred embodiments of the present invention, and is not intended to limit the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An area criterion based auto-focusing method, comprising the steps of:
(1) acquiring a holographic image of multi-focal-length plankton;
(2) carrying out image segmentation on the holographic image, and extracting a sub-holographic image of each plankton;
the specific method of the step (2) is as follows:
2-1, filtering and background filtering the holographic image to obtain a background-filtered holographic image;
2-2, carrying out binarization on the background-filtered holographic image to obtain a binary image;
2-3, setting an area interval and comparing a long axis and a short axis of the communication area, and filtering the communication area;
2-4, performing expansion and hole filling operation on the filtered binary image to obtain the boundary of a communication region of the binary image;
2-5, marking connected regions of the expanded and hole-filled binary image, and storing the pixel coordinate (r) of each connected regionn,cn)；
2-6, calculating the length a and the width b of the equivalent minimum bounding rectangle of each connected region and the vertex coordinate (r) at the upper left corner0,c0)；
2-7, obtaining a sub-hologram image corresponding to each plankton by the formula (1),
Bn(rn-r0+1,cn-c0+1)＝A(rn,cn) (1)
wherein, BnRepresents a sub-hologram corresponding to the nth plankton, BnA represents said holographic image, a x b;
(3) sequentially applying an automatic focusing method based on an area criterion to the sub-holographic image to obtain the focal length of each plankton; the specific process is as follows:
3-1, sequentially taking reconstruction distances in the reconstruction distance interval according to the step length, and reconstructing a reconstruction image corresponding to the sub-hologram image of the plankton at each reconstruction distance;
3-2, binarizing the reconstructed image at each reconstruction distance to obtain a corresponding binary image;
3-3, filtering scattered small connected regions in the binary image through a preset area threshold value, and reserving a maximum plankton connected region;
and 3-4, comparing the areas of the maximum plankton communicating regions obtained at the reconstruction distances, and taking the reconstruction distance corresponding to the minimum area as the focal length.
2. The area-based criterion automatic focusing method of claim 1, wherein in step 2-1, the holographic image is subjected to non-local mean filtering to filter out coherent noise in the holographic image; and setting a gray threshold value, separating plankton from the background in the holographic image, and filtering the background.
3. The area-based criterion automatic focusing method of claim 1, wherein in step 2-3, an upper limit and a lower limit of the area interval are set, connected regions with an area smaller than the lower limit and connected regions with an area larger than the upper limit in the binary image are filtered, and part of impurities in the seawater are filtered.
4. The area-based criterion automatic focusing method of claim 1, wherein in step 2-3, the major axis and the minor axis of the connected region in the binary image after filtering impurities are calculated, the connected region with the ratio of the major axis to the minor axis being less than 2 is removed, and non-plankton particles in seawater are filtered.
5. The area-based criterion automatic focusing method as claimed in claim 1, wherein in step 3-1, when determining the reconstruction distance interval of each plankton, the global minimum point is first searched by a large step size, and then extended to both sides by a small step size around the minimum point as the reconstruction distance interval of the plankton.
6. The area-based criterion automatic focusing method of claim 5, wherein within the reconstruction distance interval, a reconstruction distance is taken out in steps of 1um for calculation.
7. The area criterion-based autofocus method of claim 1, wherein in step 3-2, a gray threshold is set in the reconstructed image of each plankton, background noise is filtered out, and the image is binarized to obtain a corresponding binary image.
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