CN117555123A - Automatic focusing method and device for electron microscope - Google Patents
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Abstract
The invention discloses an automatic focusing method and device of an electron microscope, which are characterized in that firstly, images are respectively collected at an initial focusing position of a lens and two other positions before and after the initial focusing position, evaluation values corresponding to the collected images are calculated, the three positions are corrected according to the evaluation values, and then a coarse focusing position is calculated according to a defocusing analysis method; then respectively acquiring images at the rough focus correcting position and the other two positions before and after the rough focus correcting position, calculating evaluation values corresponding to the acquired images, correcting the three positions according to the evaluation values, and calculating a fine focus correcting position according to a curve fitting method; and finally, the lens moves to the fine-focusing position to finish automatic focusing. Compared with the focusing search algorithm adopted in the traditional passive automatic focusing, the method has fewer steps required for completing automatic focusing, improves the image distinguishing capability when the defocus amount is large, and improves the focusing speed of an electron microscope.
Description
Technical Field
The present invention relates to digital image processing methods and apparatuses, and in particular, to an automatic focusing method and apparatus.
Background
With the rapid development of industrial automation, machine vision is gradually beginning to replace the traditional human eye in production and inspection. For example, endoscopes in some medical imaging systems have incorporated an auto-focus function. Focusing is the most central problem for machine vision systems, and if the focal length is not adjusted, the captured image is blurred and subsequent work is naturally undone. Under a microscope, the problem is more serious, and new requirements are also put on the adjustment accuracy due to the small depth of field of the objective lens. Therefore, how to focus accurately at high speed is an important issue in the field of machine vision, and autofocus techniques are key techniques to solve this problem.
In principle, autofocus can be classified into 2 types, passive and active. Active autofocus refers to some modification of the system hardware for autofocus functions, such as adding a laser ranging module directly, or modifying the system using defocus error measurement techniques, etc. The active focusing adjustment is troublesome, the cost is higher, but the efficiency is higher. Passive autofocus is based on digital image processing, which performs a series of analyses on the acquired image to help complete focusing, and can be categorized into in-focus depth and out-of-focus depth. Passive focusing is the most widely used focusing mode at present, and has the advantages of low cost, low power consumption, flexible and easy control of algorithm, relatively long focusing time and high requirement on image quality.
In passive autofocus, the depth of focus method is to collect a series of images near the focus, analyze each frame of images, and implement autofocus by changing the sharpness of the images, where the focus is an image sharpness evaluation function and a focus search algorithm. The image definition evaluation function has the characteristics of no bias, unimodal, high signal to noise ratio, small metering number, high sensitivity and the like, and is commonly used with gray gradient functions, frequency spectrum functions, entropy functions and the like. The focusing search algorithm has higher search speed and stronger anti-interference capability, and typical algorithms include a hill-climbing search method, a Fibonacci search method and a function approximation method. For example, the system moves along one direction in the area near the focus by using a hill climbing method, simultaneously analyzes the definition evaluation function of each image until the image passes through the peak value of the evaluation function, reduces the step length, changes the direction, continues to move to the peak value, and repeats the process until the accuracy requirement is met.
The most widely used search strategy is the hill climbing method or the improved three-step hill climbing method, and some methods use curve fitting methods, generally the least square method based on quadratic polynomials. They have the following drawbacks: the focusing process needs more than 10 images, generally more steps, long focusing time and poor instantaneity; the hill climbing method is easily affected by local extremum, even if the method such as the three-point method and the four-point method is adopted, the problem of sinking into the local extremum cannot be fundamentally solved, and the efficiency of the multi-point discrimination method is low.
The curve fitting method is easy to be influenced by external interference, the least square fitting polynomial function is easy to construct a function curve with more local extremum and easy to fail focusing, but for the condition that the peak value is in a small range, the focusing effect is good, and only a few points near the peak value are needed to fit a proper curve and obtain the positive focusing position.
The defocus analysis method is different from the focusing depth method, and only 2-3 frames of images are required to be acquired, and the autofocus is completed by directly analyzing the images to obtain defocus information. However, the method needs to establish a defocusing model of the optical system, and confirms the blurring condition of the defocusing image, so that depth information is obtained. Specifically, an ideal light source is assumed in an object space, an image point with energy as a unit value is formed in an ideal imaging state, a dispersed circle is formed on an image plane according to a defocusing fuzzy principle when defocusing, and when an evaluation function is calculated by using a gradient absolute value, the gray value of pixels in a uniform circle does not need to participate in calculation, and only the circumferential length of the dispersed circle and the energy of each pixel point need to be calculated. Evaluation value of circle of confusionWherein R is the radius of the circle of confusion. From this, it is found that the defocus amount is inversely proportional to the evaluation function value. The current defocus analysis method has lower accuracy than the focusing depth method due to the fact that the extracted image information quantity is less, and only the approximate range of the focusing position can be roughly estimated.
In summary, the passive focusing needs to perform algorithm analysis on the actual image, and the microscope generally uses an area-array camera to acquire the image, so that the image information is more, the algorithm processing takes longer time, and the overall focusing takes longer time.
Disclosure of Invention
The invention aims to: aiming at the prior art, an automatic focusing method and an automatic focusing device for an electron microscope are provided, an out-of-focus analysis method and a curve fitting method are combined, an evaluation function algorithm of a gray gradient value with a high N value is combined with the out-of-focus analysis method to calculate the rough position of the positive focus, and a gray gradient value algorithm with a low N value is combined with the curve fitting method to calculate the accurate position of the positive focus.
The technical scheme is as follows: an automatic focusing method of an electron microscope comprises the following steps:
step 1: respectively acquiring images at an initial position of focusing of the lens and two other positions before and after the initial position, calculating evaluation values corresponding to the acquired images, correcting the three positions of focusing of the lens according to the evaluation values, and then calculating a coarse focusing position according to a defocusing analysis method;
step 2: respectively acquiring images at the rough focus correcting position and the other two positions before and after the rough focus correcting position, calculating evaluation values corresponding to the acquired images, correcting the three focusing positions of the lens according to the evaluation values, and then calculating the fine focus correcting position according to a function fitting method;
step 3: and the lens moves to the fine-focusing position to complete automatic focusing.
Further, in the step 1, the initial position of the lens is set as X1, the other two front and rear positions are respectively x2=x1/2, x3= (M-X1)/2, and M is the focus range mostA large position; setting the evaluation values corresponding to the images acquired by the positions X1, X2 and X3 as Y1, Y2 and Y3 respectively, correcting the three focusing positions of the lens according to the evaluation values comprises the following steps: if Y2<Y1<Y3 is as followsInstead of the position of X1, X2, X3 furthest from X4; if Y2>Y1>Y3 is as followsInstead of the position of X1, X2, X3 furthest from X4,Lrepresenting the number of sampling positions of the stepping motor in the focusing range of the focusing system; then, the image is re-acquired at the position X4, and the evaluation value Y4 is calculated correspondingly.
Further, in the step 1, for an image of size m×nf (x,y) M is the width of the image, n is the height of the image,xa value representing the pixel in the width direction,x∈[0,m]，ya value representing the pixel in the height direction,y∈[0,n]the calculating of the evaluation value of the image includes the steps of: for pixel (x, y), constructing a pixel block A with size of N×N downwards to the right, calculating gray average value of the pixel block AH (x,y) The method comprises the steps of carrying out a first treatment on the surface of the Selecting a pixel block B with the size of N multiplied by N and 3 pixels at the right side of the pixel block A, and calculating the gray average value of the pixel block BR (x,y) The method comprises the steps of carrying out a first treatment on the surface of the Gradient values for pixel block aT (x,y) =(R- (x,y) -H (x,y) ) 2 The method comprises the steps of carrying out a first treatment on the surface of the Calculating gradient values of pixel blocks taking the first to (m-8) -th pixels as starting points in the first to (n-2) -th rows of the image one by one; evaluation value of final whole image。
Further, in the step 1, the calculating the coarse defocus position according to the defocus analysis method includes the following steps: calculating a fitting function according to the corrected three-point position Xi and the corresponding evaluation value YiParameters of (a)x f Taking the coarse focus position X5 asx f Wherein a and b are functional coefficients.x f
Further, in the step 2, the coarse-focus position X5 and the other two positions before and after the position X5 are respectively，/>The method comprises the steps of carrying out a first treatment on the surface of the Setting evaluation values corresponding to images acquired by the positions X5, X6 and X7 as Y5, Y6 and Y7 respectively, correcting three focusing positions of the lens according to the evaluation values, wherein the method comprises the following steps: if Y6<Y5<Y7 is->Instead of the position of X5, X6, X7 furthest from X8; if Y7<Y5<Y6 is usedInstead of the position of X5, X6, X7 furthest from X8; then, the image is collected again at the position X8, and an evaluation value Y8 is obtained through corresponding calculation; and judging whether the new three evaluation values are monotonous again, if so, repeating the step to continue the correction, and cycling the correction process until the obtained three evaluation values meet the non-monotonous condition.
Further, in the step 2, for an image of size m×nf (x,y) M is the width of the image, n is the height of the image,xa value representing the pixel in the width direction,x∈[0,m]，ya value representing the pixel in the height direction,y∈[0,n]the calculating of the evaluation value of the image includes the steps of: pixel [ ]x,y) Gradient values of (2)G(x,y)=f(x+2,y)-f(x,y) Evaluation value of image。
Further, in the step 2, the calculating the fine alignment focus position according to the function fitting method includes the following steps: according toFinally calculating a fitting function from the corrected three-point positions Xi and the corresponding evaluation values YiParameters of (a)x’ f Taking the fine focus position X9 asx’ f Wherein c and d are function coefficients.
An electron microscope autofocus device comprising: a memory for storing executable instructions; and the processor is used for running the executable instructions stored in the memory to realize the automatic focusing method of the electron microscope.
The beneficial effects are that: the focusing depth method is a focusing mode based on searching, a series of images with different definition are selected in the focusing process, and the evaluation value is calculated by using a definition evaluation function, so that the focusing process is guided. In the automatic focusing method of the electron microscope, the average gray level of the pixel blocks with the size of N multiplied by N is used for calculating the gradient value in the coarse adjustment process, so that the average value of the gradient value of the whole image is calculated and obtained, and the average value is used as the evaluation value of the image. The image evaluation values of the initial position and the other two positions in the vicinity thereof are calculated by setting a high N value, which is favorable to cover a larger teaching range but has low sensitivity. If the image evaluation values form a monotone relationship, and the image evaluation values do not contain peak positions, the potential focusing positions are difficult to reasonably fit, so that a fourth point correction point taking position is introduced, the point taking range of the fitting curve can contain the potential peak positions, and the fitting result is more accurate. And calculating the coarse alignment focus position according to the corrected data and the defocusing analysis method. Then, setting a low N value to calculate image evaluation values of the coarse focus position and other two positions in a small step range nearby, and improving the sensitivity by adopting the low N value. And calculating the fine-tuning focal position according to the function fitting method by using the corrected three points. Compared with the focusing search algorithm adopted in the traditional passive automatic focusing, the method has less steps required for completing the automatic focusing, improves the image distinguishing capability when the defocus amount is large, and improves the focusing speed of the electron microscope.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of a coarse tuning step of the defocus analysis of the present invention;
FIG. 3 is a flow chart of a coarse position correction algorithm of the present invention;
FIG. 4 is a flow chart of the fine tuning steps of the curve fitting method of the present invention;
FIG. 5 is a flow chart of a fine position correction algorithm of the present invention.
Description of the embodiments
The invention is further explained below with reference to the drawings.
As shown in fig. 1, an automatic focusing method of an electron microscope includes the following steps:
step 1: respectively acquiring images at an initial position of focusing of the lens and two other positions before and after the initial position, calculating evaluation values corresponding to the acquired images, correcting the three positions of focusing of the lens according to the evaluation values, and then calculating a coarse focusing position according to a defocusing analysis method.
As shown in fig. 2, the coarse adjustment process of step 1 includes the following specific steps:
step 1.1: setting the calculated N value of the image definition evaluation function as 3, wherein the calculated N value represents that the window size obtained by function operation is N multiplied by N, namely, the average gray scale of the pixel block with the size of N multiplied by N is used for calculating the gradient value.
Step 1.2: the evaluation value Y1 of the image acquired at the initial position X1 is calculated, and the calculation process is as follows: for an image of size m×nf(x,y) M is the width of the image, n is the height of the image,xa value representing the pixel in the width direction,x∈[0,m]，ya value representing the pixel in the height direction,y∈[0,n]constructing a first pixel block with the size of 3 multiplied by 3 from the first pixel (1, 1) at the upper left corner of the image to the lower right, and calculating the gray average value of the pixel blockH (1,1) ：
Selecting a pixel block with a size of 3×3 as a reference pixel block, which is 3 pixels apart from the right side of the first pixel blockThat is, the reference pixel block starts with the pixel (7, 1), and the gray average value of the reference pixel block is calculatedR (1,1) ：
Gradient value of the first pixel blockT (1,1) The method comprises the following steps:
in the same way, the gray average value of a second pixel block starting from a second pixel (2, 1) to the right of the first pixel (1, 1) is calculatedH (2,1) Then calculate the gray average value of the control pixel block with the size of 3×3, which is 3 pixels apart on the right side of the second pixel blockR (2,1) Further calculate the gradient value of the second pixel blockT (2,1) The method comprises the steps of carrying out a first treatment on the surface of the Repeating the process, sequentially calculating gradient values corresponding to 3×3 pixel blocks starting from each pixel in the first row until the gradient values corresponding to the pixel blocks starting from the pixel point (m-8, 1)H m(-8,1) Until that is reached; then, according to the calculation process of the first row, gradient values of the pixel blocks with each pixel point in the second row to the (n-2) th row as a starting point are calculated.
The final evaluation value Y1 of the entire image is:
step 1.3: taking X2 = X1/2, i.e. the lens focus position is moved to X2, the evaluation value Y2 of the image acquired at this position is calculated, and the calculation process is the same as the calculation process of the evaluation value Y1 of the image acquired at position X1.
Step 1.4: taking X3= (M-X1)/2, wherein M is the maximum position of the focusing range, moving the lens to the X3 position, and calculating the evaluation value Y3 of the image acquired at the position, wherein the calculation process is the same as that of the evaluation value Y1 of the image acquired at the position X1.
Step 1.5: if the three evaluation values calculated in the steps 1.2 to 1.4 form a monotone condition, carrying out point selection position correction on the three points X1, X2 and X3; if the monotone case is not constituted, no position correction is made.
As shown in FIG. 3, if Y2<Y1<Y3 is as followsInstead of the position of X1, X2, X3 furthest from X4; if Y2>Y1>Y3 is->Instead of the position of X1, X2, X3 furthest from X4,Land the number of the sampling positions of the stepping motor in the focusing range of the focusing system is represented. Then, the image is collected again at the position X4, and the evaluation value Y4 is obtained by corresponding calculation, and the calculation process is the same as that of the evaluation value Y1 of the image collected at the position X1.
Step 1.6: calculating a fitting function according to the corrected three-point position Xi and the corresponding evaluation value YiParameters of (a)x f Taking the coarse focus position X5 asx f Wherein a and b are functional coefficients.
Step 2: images are respectively acquired at the rough focus adjustment position X5 and the other two positions before and after the position X5, evaluation values corresponding to the acquired images are calculated, three focusing positions of the lens are corrected according to the evaluation values, and then the fine focus adjustment position is calculated according to a function fitting method.
As shown in fig. 4, the fine tuning process of step 2 includes the following specific steps:
step 2.1: the calculated N value of the sharpness evaluation function of the image is set to 1.
Step 2.2: calculating an evaluation value Y5 of an image acquired by the position X5, wherein the calculation process comprises the following steps: for an image of size m×nf (x,y) Arbitrary pixel [ ]x,y) Gradient values of (2)G(x,y)=f(x+2,y)-f(x,y) Evaluation of the imageValue of。
Step 2.3: order theThe lens is moved to the position X6, and the evaluation value Y6 of the image acquired at this position is calculated, the calculation process being the same as the calculation process of the evaluation value Y5 of the image acquired at the position X5.
Step 2.4: order theThe lens is moved to the position X7, and the evaluation value Y7 of the image acquired at this position is calculated, the calculation process being the same as the calculation process of the evaluation value Y5 of the image acquired at the position X5.
Step 2.5: if the three evaluation values calculated in the steps 2.2 to 2.4 form a monotone condition, performing point selection position correction on three points X5, X6 and X7; if no monotonic condition is constituted, no correction is made.
As shown in FIG. 5, if Y6<Y5<Y7 is usedInstead of the position of X5, X6, X7 furthest from X8. If Y7<Y5<Y6 is->Instead of the position of X5, X6, X7 furthest from X8. Then, the image is re-collected at the position X8, and the evaluation value Y8 is correspondingly calculated, and the calculation process is the same as that of the evaluation value Y5 of the image collected at the position X5. And judging whether the new three evaluation values are monotonous again, if so, repeating the step to correct again, and cycling the process until the non-monotonous result is met.
Step 2.6: calculating a fitting function according to the final corrected three-point position Xi and the corresponding evaluation value YiParameters of (a)x’ f Taking the fine focus position X9 asx’ f Wherein c and d are function coefficients.
Step 3: and the lens moves to the fine-focusing position to complete automatic focusing.
An electron microscope autofocus device comprising: a memory for storing executable instructions; and the processor is used for running the executable instructions stored in the memory to realize the automatic focusing method of the electron microscope.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (8)
1. An automatic focusing method of an electron microscope is characterized by comprising the following steps:
step 1: respectively acquiring images at an initial position of focusing of the lens and two other positions before and after the initial position, calculating evaluation values corresponding to the acquired images, correcting the three positions of focusing of the lens according to the evaluation values, and then calculating a coarse focusing position according to a defocusing analysis method;
step 2: respectively acquiring images at the rough focus correcting position and the other two positions before and after the rough focus correcting position, calculating evaluation values corresponding to the acquired images, correcting the three focusing positions of the lens according to the evaluation values, and then calculating the fine focus correcting position according to a function fitting method;
step 3: and the lens moves to the fine-focusing position to complete automatic focusing.
2. The method according to claim 1, wherein in the step 1, an initial position of focusing the lens is set as X1, two other positions of front and rear are respectively x2=x1/2, x3= (M-X1)/2, and M is a position of a maximum focusing range; setting the evaluation values corresponding to the images acquired by the positions X1, X2 and X3 as Y1, Y2 and Y3 respectively, and focusing the lens according to the evaluation valuesThe position correction includes the steps of: if Y2<Y1<Y3 is as followsInstead of the position of X1, X2, X3 furthest from X4; if Y2>Y1>Y3 is->Instead of the position of X1, X2, X3 furthest from X4,Lrepresenting the number of sampling positions of the stepping motor in the focusing range of the focusing system; then, the image is re-acquired at the position X4, and the evaluation value Y4 is calculated correspondingly.
3. The method according to claim 1 or 2, wherein in the step 1, for an image of size m×nf (x,y) M is the width of the image, n is the height of the image,xa value representing the pixel in the width direction,x∈[0,m]，ya value representing the pixel in the height direction,y∈[0,n]the calculating of the evaluation value of the image includes the steps of: for pixel (x, y), constructing a pixel block A with size of N×N downwards to the right, calculating gray average value of the pixel block AH (x,y) The method comprises the steps of carrying out a first treatment on the surface of the Selecting a pixel block B with the size of N multiplied by N and 3 pixels at the right side of the pixel block A, and calculating the gray average value of the pixel block BR (x,y) The method comprises the steps of carrying out a first treatment on the surface of the Gradient values for pixel block aT (x,y) =(R- (x,y) -H (x,y) ) 2 The method comprises the steps of carrying out a first treatment on the surface of the Calculating gradient values of pixel blocks taking the first to (m-8) -th pixels as starting points in the first to (n-2) -th rows of the image one by one; evaluation value of final whole image。
4. The method of auto-focusing an electron microscope according to claim 2, wherein in the step 1, calculating the coarse-focus position according to the defocus analysis method comprises the steps of: calculating a fitting function according to the corrected three-point position Xi and the corresponding evaluation value YiNumber of digitsParameters of (a)x f Taking the coarse focus position X5 asx f Wherein a and b are functional coefficients.
5. The method according to claim 4, wherein in the step 2, the coarse focus position X5 and the other two positions before and after the coarse focus position X5 are respectively，/>The method comprises the steps of carrying out a first treatment on the surface of the Setting evaluation values corresponding to images acquired by the positions X5, X6 and X7 as Y5, Y6 and Y7 respectively, correcting three focusing positions of the lens according to the evaluation values, wherein the method comprises the following steps: if Y6<Y5<Y7 is->Instead of the position of X5, X6, X7 furthest from X8; if Y7<Y5<Y6 is->Instead of the position of X5, X6, X7 furthest from X8; then, the image is collected again at the position X8, and an evaluation value Y8 is obtained through corresponding calculation; and judging whether the new three evaluation values are monotonous again, if so, repeating the step to continue the correction, and cycling the correction process until the obtained three evaluation values meet the non-monotonous condition.
6. The method according to claim 5, wherein in the step 2, for an image of size m×nf (x,y) M is the width of the image, n is the height of the image,xa value representing the pixel in the width direction,x∈[0,m]，ya value representing the pixel in the height direction,y∈[0,n]the calculating of the evaluation value of the image includes the steps of: pixel [ ]x,y) Gradient values of (2)G(x,y)=f(x+2,y)-f(x,y) Evaluation value of image。
7. The method according to claim 5 or 6, wherein in the step 2, the calculating the fine focus position according to the function fitting method comprises the steps of: calculating a fitting function according to the final corrected three-point position Xi and the corresponding evaluation value YiParameters of (a)x’ f Taking the fine focus position X9 asx’ f Wherein c and d are function coefficients.
8. An electron microscope autofocus device comprising: a memory for storing executable instructions; a processor for executing the executable instructions stored in the memory to implement the electron microscope auto-focusing method according to any one of claims 1-7.
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