CN113109936B - Microscope automatic focusing method and device based on image definition evaluation - Google Patents
Microscope automatic focusing method and device based on image definition evaluation Download PDFInfo
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Abstract
The invention provides a microscope automatic focusing method and a device based on image definition evaluation, wherein the method comprises the following steps: the method comprises the following steps: dividing an original graph into a plurality of image sub-blocks; step two: determining initial focusing evaluation values of the image sub-blocks by using an initial evaluation function, and determining the number of initial focusing windows according to the initial focusing evaluation values to focus; step three: and taking the sub-block corresponding to the initial focusing window as the evaluation basis of the evaluation function, and focusing by continuously replacing the evaluation function and continuously reducing the number of the focusing windows until the focusing is finished. The method provided by the invention dynamically selects the focusing window through a composite definition evaluation mode, improves the combination of the three-order hill climbing search algorithm, can avoid the defects of each definition evaluation function and exerts the respective advantages. The focusing precision is improved, and the focusing time is reduced.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an automatic microscope focusing method and device based on image definition evaluation.
Background
Optical microscopes play an important role as a precise optical instrument in the fields of biology, medicine, chemistry, and the like. Microscopic observation and monitoring of samples using microscopes that rely on manual focusing can easily lead to some degree of error. In recent years, with the continuous progress and improvement of the automatic control theory, the autofocus technology has been applied to the optical microscope system. The automatic focusing system can reduce the labor of operators and the subjective error caused by repeated adjustment, can replace complex focusing operation, and greatly helps the operators to improve the working efficiency. With the development of CCD, CMOS, computer control technology, Digital Signal Processing (DSP) chip technology, etc., the auto-focusing technology based on image processing has been rapidly developed. Therefore, there is a certain trend of development to apply the autofocus technology to the optical microscope focusing system.
Based on different focusing principles, the automatic focusing technologies can be mainly classified into two types, namely active focusing technologies and passive focusing technologies. A method of performing defocus amount detection and focusing by using auxiliary elements such as laser is called active auto-focusing; a focusing method based on digital image processing and by performing an out-of-focus evaluation on an imaged image is called a passive focusing technique. The passive focusing technique can be divided into a defocus depth method and a focus depth method. The defocus depth method is a method for obtaining depth information from a defocus image to complete auto-focusing. The method needs to obtain 2-3 images under different imaging parameters, requires to describe an imaging system by a mathematical model in advance, and then calculates the optimal focusing position according to the images obtained from a small number of imaging positions. The defocus depth method requires a small number of images, so that the processing speed is high, but the accuracy is low. The focusing depth method is a focusing mode established in the searching process, and evaluates the definition of images obtained from different focusing positions by selecting a proper evaluation function, wherein the maximum definition value corresponds to the best focusing position.
The passive auto-focusing algorithm based on image processing includes three aspects, namely, a sharpness evaluation function, a focusing window and a search strategy. The accurate focusing evaluation function can reflect the real defocusing degree of the system; the influence of background noise can be reduced and the calculation amount can be reduced by reasonably selecting the focusing window; the optimized search strategy can quickly find the best focus plane.
The definition evaluation functions commonly adopted at present mainly comprise a gray scale change evaluation function, a gray scale entropy function and a frequency domain function. The gray scale change evaluation function detects the image definition by utilizing that a clear image has a sharper boundary and a larger gray scale value change at the boundary. The gray scale change function mainly comprises a Tenenngrad function, a Brenner function, an image gray scale variance function and a gradient square function. The gray level entropy function utilizes the aroma information theory, and the maximum information quantity exists when the entropy is maximum. The more equal the probability of occurrence of all gray levels in the image, the more the maximum amount of information is contained. Therefore, the magnitude of the grayscale entropy of the image can also be used as the evaluation value of the image sharpness. Sharp images contain more information than blurred images, sharp images have sharper edges, while high frequency parts are mainly concentrated at the edges of the image. The method of extracting the high-frequency part in the image by using the Fourier transform of the frequency domain class function can also be used for judging the definition of the image.
However, the above several types of sharpness evaluation functions have characteristics, and the gray scale change evaluation function has a wide focusing range, good stability and low sensitivity; the gray level entropy function has longer calculation time and low sensitivity; the frequency domain class function can accurately judge the definition of the image, but the calculation time is long because Fourier transform needs to be operated.
The focusing window commonly used at present comprises a central window, an inverted T window and a non-uniform sampling window-taking method. The center window-taking method is to select the center area of the image as the focusing area, the size of the focusing area is usually 1/4 or 1/16 of the whole image, firstly, the coarse focusing is carried out in a small window mode, and then the fine focusing is carried out in a large window mode. The inverted T-shaped window is characterized in that when a subject of a scene shot by people in daily life is generally positioned at the middle lower part, an image can have a more aesthetic habit, and the window is taken at the middle lower part and the center of the image and is in an inverted T shape. The non-uniform sampling window method is characterized in that the original resolution ratio of a window in the central area of an image is kept unchanged, the non-uniform sampling is carried out on the peripheral area, and the resolution ratio is reduced in an exponential mode along with the increase of the radius.
However, the focusing windows selected by the above methods are all fixed and not adaptive, and if the distribution of the targets is random or uneven, the accuracy of target focusing is affected. As information outside the window is completely lost. Furthermore, with these conventional methods, the position of the target in the image must be determined before the autofocus process is applied, which results in an increase in calculation time. Another disadvantage of these conventional approaches is that the sharpness score depends largely on whether the target is in the selected focus window. The difference between the sharpness evaluation value and the actual situation is larger as soon as the actual imaging target is not at the center position of the window or the selected window.
The search algorithm adopted at present is a hill climbing search method, an image definition evaluation function curve under an ideal condition has unimodal and symmetrical properties, can be approximately expressed into a parabolic shape, and corresponds to an optimal imaging position, namely a positive focal position when an extreme value is reached. In general, in the process of approaching the clearest image, the evaluation function value is monotonously increased; after the optimum imaging position is crossed, the evaluation function value is monotonically decreased. From this characteristic, the principle of the hill-climbing search method is: when the focusing search is started, the image is moved to the next position in an arbitrary direction according to the search step length, the search direction is determined by comparing the function evaluation values of the images acquired at the two positions, and the optimal imaging position is determined, and the schematic diagram is shown in fig. 1.
The hill climbing searching method includes the steps of assuming a searching direction and setting a larger searching step length when searching is started, driving a lens to move in an equal step length mode, collecting an image at the position and calculating an image definition evaluation value when the lens moves for one step, comparing evaluation values of the images at the front position and the rear position, changing the searching direction when an evaluation function value of a newly obtained image is smaller than that of the previous image, correspondingly reducing the searching step length, repeatedly searching again in a small step length mode, correspondingly compensating after the searching is finished, continuously changing the direction and the searching step length, and finally determining the optimal imaging position, namely a positive focal position.
However, the disadvantages of the hill-climbing search method are: firstly, the method is easily affected by local extremum, and misjudgment may be generated due to "false peak", resulting in focusing error. Secondly, the problem of too long searching time caused by improper step length is solved.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a microscope automatic focusing method and device based on image definition evaluation, which have high focusing speed and high precision.
An automatic microscope focusing method based on image definition evaluation comprises the following steps:
the method comprises the following steps: dividing an original graph into a plurality of image sub-blocks;
step two: determining initial focusing evaluation values of the image sub-blocks by using an initial evaluation function, and determining the number of initial focusing windows according to the initial focusing evaluation values to focus;
step three: and taking the sub-block corresponding to the initial focusing window as the evaluation basis of the evaluation function, and focusing by continuously replacing the evaluation function and continuously reducing the number of the focusing windows until the focusing is finished.
Further, the method for microscope auto-focusing based on image sharpness evaluation as described above, the second step includes the following steps:
step 21: determining the focus evaluation values of the image sub-blocks by using an initial evaluation function, and selecting the image sub-blocks with the evaluation values larger than a first preset threshold value as initial focus windows according to the focus evaluation values;
step 22: determining a search step length, controlling the motor to operate by one step, calculating the sum of front and rear focusing evaluation values of the initial focusing sub block, and determining the increasing direction as the search direction of the motor;
step 23: controlling a motor to run along the searching direction, and comparing the magnitude relation of focusing evaluation values of the front state and the rear state of each initial focusing sub block;
step 24: when the sum of the focus evaluation values of the initial focus sub-blocks is decreased, the step 25 is carried out; when the sum of the focus evaluation values of the initial focus sub-blocks is not decreased, returning to the step 23;
step 25: calculating whether the current step length is equal to or less than a second preset threshold, and if the current step length is equal to or less than the second preset threshold, finishing focusing; and if the threshold value is larger than the preset threshold value, entering the third step.
Further, according to the microscope auto-focusing method based on image sharpness evaluation as described above, the initial evaluation function is an image gray variance function.
Further, the method for automatically focusing a microscope based on image sharpness evaluation as described above, the third step includes:
step 31: replacing the initial evaluation function as a second evaluation function, and continuing to advance one step along the current search direction according to the current step length;
step 32: evaluating the image subblocks corresponding to the initial focusing window again by using a second evaluation function to obtain the focusing evaluation value of each initial focusing subblock;
step 33: comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub-block;
step 34: if the initial focus sub-blocks with increased focus evaluation values are more than the initial focus sub-blocks with decreased focus evaluation values, the evaluation function is replaced with the initial evaluation function, and the step 23 is returned; if the initial focus sub-block with increased focus evaluation value is less than the initial focus sub-block with decreased focus evaluation value, go to step 35;
step 35: discarding the focusing sub-block with the focusing evaluation value smaller than the third preset threshold in the step 32, reducing the search step length, and reversing the search direction;
step 36: controlling a motor to continue searching along the current direction until the sum of the focus evaluation values of the current focus sub-blocks is decreased;
step 37: changing the second evaluation function into a third evaluation function, and continuing to advance one step along the current search direction according to the current step length;
step 38: evaluating the image subblocks corresponding to the current focusing window again by using a third evaluation function to obtain the focusing evaluation value of each current focusing subblock;
step 39: comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each focus sub-block;
step 40: if the focus sub-blocks with increased focus evaluation values are more than the focus sub-blocks with decreased focus evaluation values, the evaluation function is changed into a second evaluation function, and the step 36 is returned; if the initial focus sub-block with increased focus evaluation value is less than the initial focus sub-block with decreased focus evaluation value, go to step 41;
step 41: further reducing the search step length to be equal to or less than a second preset threshold, reducing the number of the current focusing sub-blocks again, and reversing the search direction again;
step 42: and controlling the motor to continuously search along the current direction, evaluating the image subblock corresponding to the current focusing window by using a third evaluation function to obtain the focusing evaluation value of each current focusing subblock, and finishing focusing when the sum of the focusing evaluation values of the current focusing subblocks is decreased progressively because the searching step length is equal to or less than a second preset threshold value.
Further, in the method for automatically focusing a microscope based on image sharpness evaluation as described above, the second evaluation function is a Tenengrad function; the third evaluation function is a frequency domain class function.
An automatic focusing device for a microscope based on image definition evaluation comprises:
the dividing module is used for dividing the original graph into a plurality of image sub-blocks;
the evaluation module is used for determining initial focusing evaluation values of the image sub-blocks by using an initial evaluation function and determining the number of initial focusing windows according to the initial focusing evaluation values so as to carry out focusing;
and the focusing module is used for focusing by continuously replacing the evaluation function and continuously reducing the number of the focusing windows according to the sub-block corresponding to the initial focusing window as the basis of evaluation of the evaluation function until the focusing is finished.
Further, the microscope auto-focusing apparatus based on image sharpness evaluation as described above, the evaluation module includes:
the initial evaluation unit is used for determining the focus evaluation values of the image sub-blocks by using an initial evaluation function, and selecting the image sub-blocks with the evaluation values larger than a first preset threshold value as an initial focus window according to the focus evaluation values;
the searching unit is used for determining a searching step length, controlling the motor to operate by one step, calculating the sum of front and rear focusing evaluation values of the initial focusing sub-block, and determining the increasing direction as the searching direction of the motor;
the first comparison unit is used for controlling the motor to run along the search direction and comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub block;
a selection unit configured to perform an operation of the second comparison unit when a sum of the focus evaluation values of the initial focus sub-blocks decreases; when the sum of the focusing evaluation values of the initial focusing sub-blocks is not decreased, continuing searching according to the current step length;
the second comparison unit is used for calculating whether the current step length is equal to or less than a second preset threshold value or not, and if the current step length is equal to or less than the second preset threshold value, focusing is finished; and if the current value is greater than the preset threshold value, executing the operation of the focusing module.
Further, in the automatic focusing device for microscope based on image sharpness evaluation as described above, the initial evaluation function is an image gray variance function.
Further, the microscope auto-focusing apparatus based on image sharpness evaluation as described above, the focusing module includes:
the replacing unit is used for replacing the initial evaluation function into a second evaluation function and continuing to advance one step along the current searching direction according to the current step length;
the second evaluation unit is used for evaluating the image sub-blocks corresponding to the initial focusing windows again by using a second evaluation function to acquire the focusing evaluation values of the current initial focusing sub-blocks;
the third comparison unit is used for comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub-block;
a second selection unit, configured to, if the initial focus sub-blocks with increased focus evaluation values are more than the initial focus sub-blocks with decreased focus evaluation values, change the evaluation function to the initial evaluation function, and continue searching according to the current step length; executing the operation of the focus reducing unit if the initial focus sub-block with the increased focus evaluation value is less than the initial focus sub-block with the decreased focus evaluation value;
a focus reducing unit for discarding the focus sub-block whose focus evaluation value evaluated by the second evaluating unit is smaller than a third preset threshold, reducing the search step length, and reversing the search direction;
the second searching unit is used for controlling the motor to continue searching along the current direction until the sum of the focus evaluation values of the current focus sub-blocks is decreased;
the second replacing unit is used for replacing the second evaluation function into a third evaluation function and continuing to advance one step along the current searching direction according to the current step length;
the third evaluation unit is used for evaluating the image sub-blocks corresponding to the current focusing window again by using a third evaluation function to acquire the focusing evaluation values of the current focusing sub-blocks;
the fourth comparison unit is used for comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each focus sub-block;
a third selection unit, configured to, if the focus sub-blocks with increased focus evaluation values are more than the focus sub-blocks with decreased focus evaluation values, change the evaluation function to a second evaluation function, and continue to perform search according to the current step length; executing the operation of the second focus reducing unit if the initial focus sub-block in which the focus evaluation value increases is less than the initial focus sub-block in which the focus evaluation value decreases;
the second focusing reduction unit is used for further reducing the search step length to be equal to or smaller than a second preset threshold, reducing the number of the current focusing sub-blocks again and reversing the search direction again;
and the focusing unit is used for controlling the motor to continuously search along the current direction, evaluating the image subblocks corresponding to the current focusing window by using a third evaluation function to obtain the focusing evaluation values of the current focusing subblocks, and finishing focusing when the sum of the focusing evaluation values of the current focusing subblocks is decreased progressively because the searching step length is equal to or less than a second preset threshold value.
Further, in the automatic focusing device for a microscope based on image sharpness evaluation as described above, the second evaluation function is a Tenengrad function; the third evaluation function is a frequency domain class function.
Has the advantages that:
1. the method provided by the invention combines various image definition evaluation functions and utilizes proper functions in stages, thereby avoiding the defects of each evaluation function and exerting respective advantages.
2. The image is divided into sub-blocks and focused by a method of dynamically selecting a focusing window, and the area with obvious boundary in the image can be selected as the area of the focusing window by dynamically selecting the focusing window, so that useless information in the image is removed, the focusing accuracy is improved, the calculated amount is reduced, and the focusing speed is also improved.
3. The focusing time is reduced by reducing the focus sub-block during focusing.
4. By switching the definition function and comparing the front and back states of each focusing sub-block and counting, the phenomenon of 'false peak' can be effectively avoided, the step length can be flexibly adjusted, and the focusing speed is improved.
5. In order to further enhance the focusing accuracy, the frequency domain function used in the last step, in which the focus sub-block is already few, may only keep one to two blocks, so the required calculation amount is much shorter than that of the whole calculation. And at the moment, the focus point is already approached, the number of steps required to be calculated is small, and the calculation time is greatly shortened compared with the whole course of using a frequency domain function.
Drawings
FIG. 1 is a schematic diagram of a hill-climbing search algorithm in the prior art;
FIG. 2 is a schematic diagram of image slitting;
FIG. 3 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described clearly and completely below, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an automatic microscope focusing method based on image definition evaluation. The method adopts a mode of combining an image gray variance function, a Tenengrad function and a frequency domain function as a definition evaluation mode; and a method for dynamically selecting a focusing window is provided; and a third-order hill climbing search algorithm is provided by combining the definition evaluation mode and the dynamic focusing window improved hill climbing search method.
1. Image definition evaluation method
The method adopts a mode of combining an image gray variance function, a Tenengrad function and a frequency domain function as a definition evaluation mode:
(1) image gray variance function:
a sharp image has a larger difference in gray scale than a blurred image, which indicates that the variance of the image is larger, and thus the variance function is generally used to represent the gradient information of the image. The variance function can be expressed as:
where u represents the average grayscale value of the image, f (x, y) represents the grayscale value at the image coordinate point (x, y), and takes the form:
where M × N is the pixel size of the image.
(2) Tenengrad function
The Tenengrad function extracts gradient values in the horizontal direction and the vertical direction, respectively, using Sobel operators, and represents the merit function by the sum of squares of the gradients. The gradient S (x, y) is above a threshold T, i.e.:
in the formula:
for a digital image:
Gx(x,y)＝f(x-1,y+1)+2f(x,y+1)+f(x+1,y+1)-f(x-1,y-1)-2f(x,y-1)-f(x+1,y-1)
Gy(x,y)＝f(x-1,y-1)+2f(x-1,y)+f(x-1,y+1)-f(x+1,y-1)-2f(x+1,y)-f(x+1,y+1)
(3) frequency domain function
The focusing evaluation function based on the frequency domain realizes the definition evaluation of the focusing image by carrying out frequency domain transformation on the image, and the method is based on Fourier transformation and analyzes the image definition characteristic from the frequency domain. In the frequency domain, the high frequency components correspond to the edge details of the image, and the more the high frequency components, the richer the representation details and the sharper the edge. A commonly used frequency domain sharpness evaluation method is a fourier transform method:
where P (u, v) is the power spectrum of the original image at the point (u, v) frequency domain space, and the calculation process is as follows:
two-dimensional discrete fourier transform:
let R (u, v) and I (u, v) be the real and imaginary parts of F (u, v), i.e.:
|F(u,v)|＝[R2(u,v)+I2(u,v)]1/2
the power spectrum P (u, v) is:
P(u,v)＝|F(u,v)|2＝R2(u,v)+I2(u,v)
2. focus window selection
The invention provides a dynamic focusing window selection mode based on the image definition evaluation function. First, the whole block image is divided into m × n blocks, as shown in fig. 2:
at different stages, the focus evaluation value of each sub-block is calculated by using evaluation functions,
f (x, y) is a focus evaluation function. E denotes a focus evaluation value of the image sub-block. And in the initial state, evaluating all the sub-blocks by using an image gray variance function, and selecting the sub-block with a larger focusing evaluation value as an initial focusing window. After the evaluation function is switched every time, each subblock is evaluated on the basis of the original focusing window subblock, the subblocks with smaller focusing evaluation values are omitted, and a plurality of subblocks with larger focusing evaluation values are selected as windows for focusing next time.
3. Search algorithm and focusing process
The invention provides a three-order hill-climbing search algorithm by combining the image definition composite evaluation mode and the dynamic focusing window improved hill-climbing search method. The focus search process is shown in fig. 3.
The method comprises the steps of preprocessing an image, dividing the image into a plurality of blocks, selecting an image gray variance function as an initial definition evaluation function, calculating a focus evaluation value of each sub-block, selecting a plurality of sub-blocks with larger focus evaluation values as focus windows, initially determining a larger search step length, and determining the direction in which the sum of the focus evaluation values of the sub-blocks is increased as the search direction after controlling a motor to rotate forwards and backwards. The motor is then controlled to run in the search direction, with the sum of the focus evaluation values for each sub-block increasing incrementally. When the sum of the focus evaluation values of the respective sub-blocks decreases, that is, an inflection point appears, it is considered that the peak top is reached. There are two cases at this time: (1) the merit function curve reaches near a "false peak", i.e. falls near a local maximum of the merit function, rather than reaching a global maximum of the focus position. (2) The merit function curve reaches near the true peak, i.e. near the global maximum of the focus position. How to decide on these two cases?
Switching an image evaluation function into a Tenengrad function with higher precision, calculating the image definition focus evaluation value of each current subblock, continuing to move forward one step along the current search direction according to the current step length, calculating the image definition focus evaluation value of each subblock, and comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each subblock.
If the sub-blocks with increased focusing evaluation values are more than the sub-blocks with decreased focusing evaluation values, the sub-blocks reach the vicinity of a pseudo peak, namely the vicinity of a local maximum value, at the moment, the evaluation function is switched to an image gray variance function with lower precision, and the motor is operated to continue searching along the current searching direction according to the current searching step length.
If the sub-blocks with increased focus evaluation values are less than the sub-blocks with decreased focus evaluation values, the sub-blocks with increased focus evaluation values reach the vicinity of the true peak, namely the vicinity of the global maximum value, and are already close to the focus, at this time, in order to reduce the calculation amount and shorten the calculation time, a few focus sub-blocks with smaller focus evaluation values are omitted, the search step length is reduced, the search direction is reversed, and the search is continued.
When the evaluation function curve falls into a local extreme value, the evaluation function curve with higher image definition is switched to, so that the local maximum value of the original evaluation function curve can be avoided, and meanwhile, the size relation of the sub-blocks is counted by comparing the size relation of the front state and the rear state of each sub-block, so that the possibility that the local extreme value appears in the focusing evaluation value sum at the same position on a new evaluation function curve is further avoided.
Through the process, after the crest is further approached in the second time of the searching process, the image definition evaluation function is switched into a frequency domain function, and the searching step length is changed into the minimum step length; reducing the number of focusing windows again to reduce the calculated amount and improve the focusing speed; and the search direction is reversed again to continue the search. And when the peak is approached for the third time, the peak is very close to the focusing plane, and the searching step length is equal to the minimum step length according to the judgment condition, so that focusing is finished.
The process of the invention is described in further detail below:
the invention provides an automatic microscope focusing method based on image definition evaluation, which comprises the following steps of:
the method comprises the following steps: dividing an original graph into a plurality of image sub-blocks;
step two: determining initial focusing evaluation values of the image sub-blocks by using an initial evaluation function, and determining the number of initial focusing windows according to the initial focusing evaluation values to focus;
step three: and taking the sub-block corresponding to the initial focusing window as the evaluation basis of the evaluation function, and focusing by continuously replacing the evaluation function and continuously reducing the number of the focusing windows until the focusing is finished.
Specifically, the second step includes the following steps:
step 21: determining the focus evaluation values of the image sub-blocks by using an initial evaluation function, and selecting the image sub-blocks with the evaluation values larger than a first preset threshold value as initial focus windows according to the focus evaluation values;
step 22: determining a search step length, controlling the motor to operate by one step, calculating the sum of front and rear focusing evaluation values of the initial focusing sub block, and determining the increasing direction as the search direction of the motor;
step 23: controlling a motor to run along the searching direction, and comparing the magnitude relation of focusing evaluation values of the front state and the rear state of each initial focusing sub block;
step 24: when the sum of the focus evaluation values of the initial focus sub-blocks is decreased, the step 25 is carried out; when the sum of the focus evaluation values of the initial focus sub-blocks is not decreased, returning to the step 23;
step 25: calculating whether the current step length is equal to or less than a second preset threshold, and if the current step length is equal to or less than the second preset threshold, finishing focusing; and if the threshold value is larger than the preset threshold value, entering the third step.
The initial evaluation function is an image gray variance function.
The third step comprises:
step 31: replacing the initial evaluation function as a second evaluation function, and continuing to advance one step along the current search direction according to the current step length;
step 32: evaluating the image subblocks corresponding to the initial focusing window again by using a second evaluation function to obtain the focusing evaluation value of each initial focusing subblock;
step 33: comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub-block;
step 34: if the initial focus sub-blocks with increased focus evaluation values are more than the initial focus sub-blocks with decreased focus evaluation values, the evaluation function is replaced with the initial evaluation function, and the step 23 is returned; if the initial focus sub-block with increased focus evaluation value is less than the initial focus sub-block with decreased focus evaluation value, go to step 35;
step 35: discarding the focusing sub-block with the focusing evaluation value smaller than the third preset threshold in the step 32, reducing the search step length, and reversing the search direction;
step 36: controlling a motor to continue searching along the current direction until the sum of the focus evaluation values of the current focus sub-blocks is decreased;
step 37: changing the second evaluation function into a third evaluation function, and continuing to advance one step along the current search direction according to the current step length;
step 38: evaluating the image subblocks corresponding to the current focusing window again by using a third evaluation function to obtain the focusing evaluation value of each current focusing subblock;
step 39: comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each focus sub-block;
step 40: if the focus sub-blocks with increased focus evaluation values are more than the focus sub-blocks with decreased focus evaluation values, the evaluation function is changed into a second evaluation function, and the step 36 is returned; if the initial focus sub-block with increased focus evaluation value is less than the initial focus sub-block with decreased focus evaluation value, go to step 41;
step 41: further reducing the search step length to be equal to or less than a second preset threshold, reducing the number of the current focusing sub-blocks again, and reversing the search direction again;
step 42: and controlling the motor to continuously search along the current direction, evaluating the image subblock corresponding to the current focusing window by using a third evaluation function to obtain the focusing evaluation value of each current focusing subblock, and finishing focusing when the sum of the focusing evaluation values of the current focusing subblocks is decreased progressively because the searching step length is equal to or less than a second preset threshold value.
The second evaluation function is a Tenengrad function; the third evaluation function is a frequency domain class function.
The invention also provides an automatic focusing device of a microscope based on image definition evaluation, which comprises:
the dividing module is used for dividing the original graph into a plurality of image sub-blocks;
the evaluation module is used for determining initial focusing evaluation values of the image sub-blocks by using an initial evaluation function and determining the number of initial focusing windows according to the initial focusing evaluation values so as to carry out focusing;
and the focusing module is used for focusing by continuously replacing the evaluation function and continuously reducing the number of the focusing windows according to the sub-block corresponding to the initial focusing window as the basis of evaluation of the evaluation function until the focusing is finished.
The evaluation module comprises:
the initial evaluation unit is used for determining the focus evaluation values of the image sub-blocks by using an initial evaluation function, and selecting the image sub-blocks with the evaluation values larger than a first preset threshold value as an initial focus window according to the focus evaluation values;
the searching unit is used for determining a searching step length, controlling the motor to operate by one step, calculating the sum of front and rear focusing evaluation values of the initial focusing sub-block, and determining the increasing direction as the searching direction of the motor;
the first comparison unit is used for controlling the motor to run along the search direction and comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub block;
a selection unit configured to perform an operation of the second comparison unit when a sum of the focus evaluation values of the initial focus sub-blocks decreases; when the sum of the focusing evaluation values of the initial focusing sub-blocks is not decreased, continuing searching according to the current step length;
the second comparison unit is used for calculating whether the current step length is equal to or less than a second preset threshold value or not, and if the current step length is equal to or less than the second preset threshold value, focusing is finished; and if the current value is greater than the preset threshold value, executing the operation of the focusing module.
Wherein the initial evaluation function is an image gray variance function.
The focusing module includes:
the replacing unit is used for replacing the initial evaluation function into a second evaluation function and continuing to advance one step along the current searching direction according to the current step length;
the second evaluation unit is used for evaluating the image sub-blocks corresponding to the initial focusing windows again by using a second evaluation function to acquire the focusing evaluation values of the current initial focusing sub-blocks;
the third comparison unit is used for comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub-block;
a second selection unit, configured to, if the initial focus sub-blocks with increased focus evaluation values are more than the initial focus sub-blocks with decreased focus evaluation values, change the evaluation function to the initial evaluation function, and continue searching according to the current step length; executing the operation of the focus reducing unit if the initial focus sub-block with the increased focus evaluation value is less than the initial focus sub-block with the decreased focus evaluation value;
a focus reducing unit for discarding the focus sub-block whose focus evaluation value evaluated by the second evaluating unit is smaller than a third preset threshold, reducing the search step length, and reversing the search direction;
the second searching unit is used for controlling the motor to continue searching along the current direction until the sum of the focus evaluation values of the current focus sub-blocks is decreased;
the second replacing unit is used for replacing the second evaluation function into a third evaluation function and continuing to advance one step along the current searching direction according to the current step length;
the third evaluation unit is used for evaluating the image sub-blocks corresponding to the current focusing window again by using a third evaluation function to acquire the focusing evaluation values of the current focusing sub-blocks;
the fourth comparison unit is used for comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each focus sub-block;
a third selection unit, configured to, if the focus sub-blocks with increased focus evaluation values are more than the focus sub-blocks with decreased focus evaluation values, change the evaluation function to a second evaluation function, and continue to perform search according to the current step length; executing the operation of the second focus reducing unit if the initial focus sub-block in which the focus evaluation value increases is less than the initial focus sub-block in which the focus evaluation value decreases;
the second focusing reduction unit is used for further reducing the search step length to be equal to or smaller than a second preset threshold, reducing the number of the current focusing sub-blocks again and reversing the search direction again;
and the focusing unit is used for controlling the motor to continuously search along the current direction, evaluating the image subblocks corresponding to the current focusing window by using a third evaluation function to obtain the focusing evaluation values of the current focusing subblocks, and finishing focusing when the sum of the focusing evaluation values of the current focusing subblocks is decreased progressively because the searching step length is equal to or less than a second preset threshold value.
Wherein the second evaluation function is a Tenengrad function; the third evaluation function is a frequency domain class function.
In conclusion, the method provided by the invention dynamically selects the focusing window through a composite definition evaluation mode, improves the combination of the three-order hill climbing search algorithm, can avoid the defects of each definition evaluation function and exerts the respective advantages. The area with obvious boundary in the image can be selected as the area of the focusing window by dynamically selecting the focusing window, thereby removing useless information in the image, providing the accuracy of the method, reducing the calculated amount and improving the focusing speed. The improved three-order hill climbing search method utilizes the switching among three definition evaluation functions and the calculation and statistics of each image subblock to effectively avoid the phenomenon of false peaks, flexibly adjust the step length and improve the focusing speed.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. An automatic microscope focusing method based on image definition evaluation is characterized by comprising the following steps:
the method comprises the following steps: dividing an original graph into a plurality of image sub-blocks;
step two: determining initial focusing evaluation values of the image sub-blocks by using an initial evaluation function, and determining the number of initial focusing windows according to the initial focusing evaluation values to focus;
step three: focusing by taking the sub-blocks corresponding to the initial focusing window as the basis for evaluating the evaluation function and continuously replacing the evaluation function and continuously reducing the number of the focusing windows until the focusing is finished;
the second step comprises the following steps:
step 21: determining the focus evaluation values of the image sub-blocks by using an initial evaluation function, and selecting the image sub-blocks with the evaluation values larger than a first preset threshold value as initial focus windows according to the focus evaluation values;
step 22: determining a search step length, controlling the motor to operate by one step, calculating the sum of front and rear focusing evaluation values of the initial focusing sub block, and determining the increasing direction as the search direction of the motor;
step 23: controlling a motor to run along the searching direction, and comparing the magnitude relation of focusing evaluation values of the front state and the rear state of each initial focusing sub block;
step 24: when the sum of the focus evaluation values of the initial focus sub-blocks is decreased, the step 25 is carried out; when the sum of the focus evaluation values of the initial focus sub-blocks is not decreased, returning to the step 23;
step 25: calculating whether the current step length is equal to or less than a second preset threshold, and if the current step length is equal to or less than the second preset threshold, finishing focusing; if the threshold value is larger than the preset threshold value, entering a third step;
the third step comprises:
step 31: replacing the initial evaluation function as a second evaluation function, and continuing to advance one step along the current search direction according to the current step length;
step 32: evaluating the image subblocks corresponding to the initial focusing window again by using a second evaluation function to obtain the focusing evaluation value of each initial focusing subblock;
step 33: comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub-block;
step 34: if the initial focus sub-blocks with increased focus evaluation values are more than the initial focus sub-blocks with decreased focus evaluation values, the evaluation function is replaced with the initial evaluation function, and the step 23 is returned; if the initial focus sub-block with increased focus evaluation value is less than the initial focus sub-block with decreased focus evaluation value, go to step 35;
step 35: discarding the focusing sub-block with the focusing evaluation value smaller than the third preset threshold in the step 32, reducing the search step length, and reversing the search direction;
step 36: controlling a motor to continue searching along the current direction until the sum of the focus evaluation values of the current focus sub-blocks is decreased;
step 37: changing the second evaluation function into a third evaluation function, and continuing to advance one step along the current search direction according to the current step length;
step 38: evaluating the image subblocks corresponding to the current focusing window again by using a third evaluation function to obtain the focusing evaluation value of each current focusing subblock;
step 39: comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each focus sub-block;
step 40: if the focus sub-blocks with increased focus evaluation values are more than the focus sub-blocks with decreased focus evaluation values, the evaluation function is changed into a second evaluation function, and the step 36 is returned; if the initial focus sub-block with increased focus evaluation value is less than the initial focus sub-block with decreased focus evaluation value, go to step 41;
step 41: further reducing the search step length to be equal to or less than a second preset threshold, reducing the number of the current focusing sub-blocks again, and reversing the search direction again;
step 42: and controlling the motor to continuously search along the current direction, evaluating the image subblock corresponding to the current focusing window by using a third evaluation function to obtain the focusing evaluation value of each current focusing subblock, and finishing focusing when the sum of the focusing evaluation values of the current focusing subblocks is decreased progressively because the searching step length is equal to or less than a second preset threshold value.
2. A method for microscope auto-focusing based on image sharpness evaluation according to claim 1, wherein the initial evaluation function is an image grey-scale variance function.
3. A method of auto-focusing a microscope based on image sharpness evaluation according to claim 1, characterized in that the second merit function is a Tenengrad function; the third evaluation function is a frequency domain class function.
4. An automatic focusing device for a microscope based on image definition evaluation, comprising:
the dividing module is used for dividing the original graph into a plurality of image sub-blocks;
the evaluation module is used for determining initial focusing evaluation values of the image sub-blocks by using an initial evaluation function and determining the number of initial focusing windows according to the initial focusing evaluation values so as to carry out focusing;
the focusing module is used for focusing by continuously replacing the evaluation function and continuously reducing the number of the focusing windows according to the subblock corresponding to the initial focusing window as the basis of evaluation of the evaluation function until the focusing is finished;
the evaluation module comprises:
the initial evaluation unit is used for determining the focus evaluation values of the image sub-blocks by using an initial evaluation function, and selecting the image sub-blocks with the evaluation values larger than a first preset threshold value as an initial focus window according to the focus evaluation values;
the searching unit is used for determining a searching step length, controlling the motor to operate by one step, calculating the sum of front and rear focusing evaluation values of the initial focusing sub-block, and determining the increasing direction as the searching direction of the motor;
the first comparison unit is used for controlling the motor to run along the search direction and comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub block;
a selection unit configured to perform an operation of the second comparison unit when a sum of the focus evaluation values of the initial focus sub-blocks decreases; when the sum of the focusing evaluation values of the initial focusing sub-blocks is not decreased, continuing searching according to the current step length;
the second comparison unit is used for calculating whether the current step length is equal to or less than a second preset threshold value or not, and if the current step length is equal to or less than the second preset threshold value, focusing is finished; if the current value is greater than the preset threshold value, executing the operation of a focusing module;
the focusing module includes:
the replacing unit is used for replacing the initial evaluation function into a second evaluation function and continuing to advance one step along the current searching direction according to the current step length;
the second evaluation unit is used for evaluating the image sub-blocks corresponding to the initial focusing windows again by using a second evaluation function to acquire the focusing evaluation values of the current initial focusing sub-blocks;
the third comparison unit is used for comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each initial focus sub-block;
a second selection unit, configured to, if the initial focus sub-blocks with increased focus evaluation values are more than the initial focus sub-blocks with decreased focus evaluation values, change the evaluation function to the initial evaluation function, and continue searching according to the current step length; executing the operation of the focus reducing unit if the initial focus sub-block with the increased focus evaluation value is less than the initial focus sub-block with the decreased focus evaluation value;
a focus reducing unit for discarding the focus sub-block whose focus evaluation value evaluated by the second evaluating unit is smaller than a third preset threshold, reducing the search step length, and reversing the search direction;
the second searching unit is used for controlling the motor to continue searching along the current direction until the sum of the focus evaluation values of the current focus sub-blocks is decreased;
the second replacing unit is used for replacing the second evaluation function into a third evaluation function and continuing to advance one step along the current searching direction according to the current step length;
the third evaluation unit is used for evaluating the image sub-blocks corresponding to the current focusing window again by using a third evaluation function to acquire the focusing evaluation values of the current focusing sub-blocks;
the fourth comparison unit is used for comparing the magnitude relation of the focus evaluation values of the front state and the rear state of each focus sub-block;
a third selection unit, configured to, if the focus sub-blocks with increased focus evaluation values are more than the focus sub-blocks with decreased focus evaluation values, change the evaluation function to a second evaluation function, and continue to perform search according to the current step length; executing the operation of the second focus reducing unit if the initial focus sub-block in which the focus evaluation value increases is less than the initial focus sub-block in which the focus evaluation value decreases;
the second focusing reduction unit is used for further reducing the search step length to be equal to or smaller than a second preset threshold, reducing the number of the current focusing sub-blocks again and reversing the search direction again;
and the focusing unit is used for controlling the motor to continuously search along the current direction, evaluating the image subblocks corresponding to the current focusing window by using a third evaluation function to obtain the focusing evaluation values of the current focusing subblocks, and finishing focusing when the sum of the focusing evaluation values of the current focusing subblocks is decreased progressively because the searching step length is equal to or less than a second preset threshold value.
5. An image sharpness evaluation based microscope auto-focus apparatus according to claim 4, wherein the initial evaluation function is an image grey scale variance function.
6. An image sharpness evaluation based auto-focus apparatus for a microscope according to claim 4, wherein the second evaluation function is a Tenengrad function; the third evaluation function is a frequency domain class function.
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