CN106488122A - A kind of dynamic auto focusing algorithm based on improved sobel method - Google Patents
A kind of dynamic auto focusing algorithm based on improved sobel method Download PDFInfo
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Abstract
The invention provides a kind of dynamic auto focusing algorithm based on improved sobel method, it is characterised in that:Process is realized in actual focusing includes step：A) system original position is located at over focus, is scanned for big step-length toward electric current augment direction, and record corresponding evaluation function value in liquid zoom lens driving current range；B) according to big step length searching process in step a, if declining twice continuously occurs in the Image Definition value of sampled point, scanned for middle step-length with big step-length output current rightabout, and calculate corresponding evaluation function value in real time；C) according to step length searching process in step b, if once declining occurs in the Image Definition value of sampled point, scanned for small step length with middle step-length output current rightabout, and calculate each sampled point evaluation function value in real time；If in the little step length searching that near focal point is carried out, there is once decline and be considered as declining previous sampled point for positive Jiao position, while whole search procedure terminates.
Description
Technical field
This patent is related to a kind of eight weighted directions Sobel operator and Step-varied back propagation search strategy, belongs to automatic focusing
Technical field.
Background technology
In bionical visual field, automatic focusing algorithm is always its key technology.With pass based on telemetry, as detection method etc.
System automatic focusing method is compared, and the dynamic focusing method based on image procossing has intellectuality, low-power consumption, Highgrade integration, takes volume
Little and inexpensive the advantages of, and the fast development with digital image processing techniques and super large-scale integration, based on figure
As the automatic focusing for processing has become the Main way of focus technique future development.Automatic focusing algorithm core based on image procossing
The heart is sharpness evaluation function and extremum search strategy, and the former is that the definition to piece image is calculated；And the latter is then
Be current focus state to be judged with sharpness evaluation function, finally realize the control to focus adjusting mechanism.True epigraph is clear
Clear degree evaluation function plays critical effect, image definition discrimination technology in the technology of auto based on image procossing
The attention of Chinese scholars is caused in recent years, more used in which is time domain contrast evaluation function, conventional
Tenengrad function, Brenner function, variance function, energy function and Laplacian function etc.；The frequency spectrum of also frequency domain
Evaluation function；The entropy evaluation function in information theory field；Wavelet transformation evaluation function；Evaluation function and god based on dct transform
Through assessing network function.Any of the above evaluation function has various pluses and minuses, such as：Entropy evaluation function is easily by external environment
Impact, sensitivity are low, easily cause the erroneous judgement of focus；The sensitivity of frequency spectrum evaluation function is high, but computationally intensive, it is difficult to meet in real time
Property require；Dct transform function is distinguished the image that has focused and is not focused by the gray-value variation situation of analysis of the image
Image, to environmental stability require higher.
It is true that more used in Image Definition is time domain contrast evaluation function, and contrast
The key point of evaluation function is rim detection, from image processing techniques principle for, edge is the most basic feature of image, figure
The profile of picture, details are all present in the marginal portion of image substantially, can greatly reduce by it and will locate in graphical analysis
The information of reason, remains the shape information of objects in images again.Therefore, rim detection is in image procossing, pattern-recognition and machine
Play a very important role in the fields such as vision.In rim detection, more common is classical Sobel operator, and it is actually
Field convolution is carried out using the direction template of horizontal and vertical two 3 × 3 with each pixel in image in image space.Although
Classical two direction Sobel operator edge detection are extracted the edge gradient information of horizontal and vertical both direction, but this edge
Detection algorithm considers less, normal lost part edge details to the direction character of image border, especially under dynamic environment, real
The gradient direction of border image is unknown, and such as horizontal or vertical edge, and the gradient direction of reality is probably level side
One kind in, vertical direction, positive 45 ° of directions, minus 45 ° of directions, and gradient direction also phase not to the utmost on different marginal points
Same, so single calculate from both direction to gray scale of certain point neighbor point, as a result there is certain error.Classical Sobel edge
Edge operator since the proposition, with its calculating simple, practicality is wider the advantages of, widely should obtain in Image Edge-Detection
With the later stage is also suggested for its various improvement Sobel algorithms.
As the pure and fresh degree evaluation function of image, extreme point search strategy is also to affect automatic focusing algorithm real-time energy
Key factor.Calculated in focus area after each image definition according to evaluation function, need positive Jiao to be found out with search strategy
Point position.Positive focus search policy mandates fast convergence rate, accuracy rate are high.The major defect of traditional search by hill climbing strategy is step-length
Determine that subjectivity is very strong.Such as less using step-length, acquisition process image spends the time more, poor real of focusing, and as walked
Long acquirement is too small, is affected by outside noise, environmental change etc., and focusing accuracy there will not be large increase.Conversely, the mistake of step-length choosing
Greatly, although speed can have been lifted, but focusing accuracy is than relatively low.It is true that automatic focusing algorithm research many at this stage
Static environment is both for, and the research for automatic focusing under dynamic environment is little.
Content of the invention
The present invention is in order to solve the above problems, there is provided a kind of dynamic auto focusing algorithm based on improved sobel method,
It is characterized in that according to the vision multichannel characteristic of human visual system (Human visual system), vision system is to water
Square to and vertical direction stimulation most sensitive, and gradually weaken toward diagonally opposed sensitiveness.Therefore, will from all directions to
The travel direction weight distribution when gradient is calculated of Sobel edge edge composition, adjustment all directions marginal element is in image gradient calculating
Proportion, so as to can more accurately calculate image definition evaluation value under dynamic environment.Process bag is realized in actual focusing
Include step：A) system original position is located at over focus, in the liquid zoom lens driving current range toward electric current augment direction with
Big step-length is scanned for, and records corresponding evaluation function value；B) according to big step length searching process in step a, if the figure of sampled point
Declining twice continuously occurs in image sharpness evaluation function value, then searched with middle step-length with big step-length output current rightabout
Rope, and calculate corresponding evaluation function value in real time；C) according to step length searching process in step b, if the image definition of sampled point
Once declining occurs in evaluation function value, then scanned for small step length with middle step-length output current rightabout, and calculate in real time
Each sampled point evaluation function value；If in the little step length searching that near focal point is carried out, there is once decline and be considered as declining previous adopting
Sampling point is positive Jiao position, while whole search procedure terminates.
Preferably, the computational methods of described image gradient are：
In formula, α, β are respectively 0 °, 90 °, 180 °, 270 ° of direction marginal elements and 45 °, 135 °, 225 °, 315 ° of directions edge
Weight coefficient corresponding to composition, Bi(i=1,2...8) is the marginal element on eight directions of image.
Preferably, the marginal element B on eight directionsiThe computing formula of (i=1,2...8) is：
B1=F (x, y) * d1B2=F (x, y) * d2
B3=F (x, y) * d3B4=F (x, y) * d4
B5=F (x, y) * d5B6=F (x, y) * d6
B7=F (x, y) * d7B8=F (x, y) * d8
Wherein, F (x, y) is gradation of image, and * is convolution algorithm, di(i=1,2...8) is boundary operator.
Preferably, all directions to the expression formula of boundary operator is：
Preferably, image definition evaluation standard is：
Wherein, x, y, M, N difference representative image pixel horizontal coordinate, vertical coordinate, horizontal pixel point are total and vertical
Pixel sum.
The present invention adopts the Image Definition based on eight weighted direction Sobel and Step-varied back propagation extreme value
Point search strategy, it is achieved that the fast automatic focusing under dynamic environment.Using the figure based on eight weighted direction Sobel in the present invention
Image sharpness evaluation function.According to the vision multichannel characteristic of human visual system (Human visual system), vision system
System is most sensitive to the stimulation of horizontal direction and vertical direction, and gradually weakens toward diagonally opposed sensitiveness.Therefore, will be from all directions
Weight distribution is carried out when gradient is calculated to Sobel edge edge composition, adjustment all directions marginal element is in image gradient calculating
Proportion, should be appreciated that aforementioned description substantially so as to can more accurately calculate image definition evaluation value under dynamic environment
Exemplary illustration and explanation are with follow-up description in detail, the limit that should not be used as to the claimed content of the present invention
System.
Description of the drawings
With reference to the accompanying drawing that encloses, the more purposes of the present invention, function and advantage are by by the as follows of embodiment of the present invention
Description is illustrated, wherein：
The step of Fig. 1 schematically shows the dynamic auto focusing algorithm according to the present invention based on improved sobel method；
Fig. 2 schematically shows the Step-varied back propagation search for being used based on improved sobel method according to the present invention
Strategic process；
Fig. 3 is schematically shown according to all directions of the present invention to Sobel edge edge Detection results figure；
Fig. 4 schematically shows the evaluation letter of the dynamic auto focusing algorithm based on improved sobel method according to the present invention
Number experimental image；
Fig. 5 (a)-Fig. 5 (b) schematically shows and is calculated according to the dynamic auto focusing based on improved sobel method of the present invention
The evaluation function of method resists hot-tempered property to contrast.
Specific embodiment
By reference to one exemplary embodiment, the purpose of the present invention and function and the side for realizing these purposes and function
Method will be illustrated.However, the present invention is not limited to one exemplary embodiment disclosed below；Can by multi-form come
Which is realized.The essence of specification is only to aid in the detail of the various equivalent modifications Integrated Understanding present invention.
Hereinafter, embodiments of the invention will be described with reference to the drawings.In the accompanying drawings, identical reference represents identical
Or similar part.
In order to solve the above problems, the present invention adopt Image Definition based on eight weighted direction Sobel and
Step-varied back propagation extreme point search strategy, it is achieved that the fast automatic focusing under dynamic environment；
Using the Image Definition based on eight weighted direction Sobel in the present invention：According to human visual system
The vision multichannel characteristic of (Human visual system), vision system to the stimulation of horizontal direction and vertical direction most
Sensitivity, and gradually weaken toward diagonally opposed sensitiveness.
Therefore, all directions is carried out weight distribution to Sobel edge edge composition when gradient is calculated, adjusts all directions marginal element
Proportion in image gradient is calculated, so as to can more accurately calculate image definition evaluation value under dynamic environment, wherein,
To boundary operator expression formula it is from all directions：
Image gradient computing formula is as follows：
In formula, α, β are respectively 0 °, 90 °, 180 °, 270 ° of direction boundary operators and 45 °, 135 °, 225 °, 315 ° of directions edge
Weight coefficient corresponding to composition, Bi(i=1,2...8) is the marginal element on eight directions of image, and computing formula is：
B1=F (x, y) * d1B2=F (x, y) * d2
B3=F (x, y) * d3B4=F (x, y) * d4
B5=F (x, y) * d5B6=F (x, y) * d6
B7=F (x, y) * d7B8=F (x, y) * d8(2)
In formula, F (x, y) is gradation of image, and * is convolution algorithm, and all Grad more than predetermined threshold value are added, and handle
The corresponding pixel of the Grad is considered image border, using edge gradient and as image definition evaluation standard, i.e.,：
When focusing on relatively good, image detail enriches, many for high fdrequency component in frequency domain representation, then shows as in spatial domain adjacent
The feature value changes of pixel are big, with bigger gradient function value, so, above-mentioned evaluation function value will be larger.Conversely, then commenting
The value of valency function is less, therefore, focuses on best image and just has maximal margin energy, and its evaluation function value is also just maximum.
Step-varied back propagation extreme point search strategy is adopted in the present invention, and the search strategy has：(1) can avoid due to adopting
Less with step-length, acquisition process image spends the time more, and focusing poor real, precision be not high, and easily receives outside noise, environment
The impact such as change；(2) the excessive of step-length choosing can be avoided, although speed can have been lifted, but the low shortcoming of focusing accuracy.
Implement step as follows：
Step 101：System original position is located at over focus A, and sharpness evaluation function value is Ei, drive in liquid zoom lens
Toward electric current augment direction with big step-length L in streaming current range1Scan for, and record corresponding evaluation function value.
Step 102：According to big step length searching process in step 101, the such as evaluation function value of sampled point B continuously occurs twice
Decline, then with big step-length output current rightabout with middle step-length L2Scan for, and calculate corresponding evaluation function value in real time.
Step 103：According to step length searching process in step 102, if once declining occurs in the evaluation function value of sampled point,
Then with middle step-length output current rightabout with little step-length L3Scan for, and calculate each sampled point evaluation function value in real time.
As the little step length searching carried out near focal point, it is positive burnt for once decline occur being considered as declining previous sampled point C
Position, while whole search procedure terminates, its Step-varied back propagation search strategy flow process is as shown in Figure 2.
As this Step-varied back propagation search strategy Local Extremum is occurring away from parfocal region and easily and adopts
It is big step length searching strategy, and the dull output of the driving current of liquid zoom lens, produce can strictly in theory system
Out of focus-positive Jiao-out of focus imaging effect, is judged by accident due to the positive focus that Local Extremum causes so as to effectively avoid.
Sharpness evaluation function is calculated due to employing eight weighted direction Sobel operators, and adopt the evaluation letter of this operator
This feature of number curve：Positive near focal point regional evaluation function curve not only has unimodality, and has very well in peak value both sides
Monotonicity, more precipitous compared to other evaluation methods, sensitivity is higher, therefore preferably can meet definition evaluate letter
Several requirements, therefore only need to judge that once evaluation of estimate declines i.e. toward rightabout search.
The algorithm of the present invention have passed through experimental verification, and experimental provision is become by PC, a target and based on liquid lens
As system, liquid zoom lens and a multispectral camera are included based on liquid lens imaging system.PC in device
Configuration Intel Core i7-4790 3.60G Hz CPU, 8GB internal memory, Win7 operating system, Matlab 8.3 and VS2010 are compiled
Translate environment.
In the present invention, to carry out edge extracting method as follows for eight-direction Sobel operator：First, emulated using Matlab8.3 soft
Part carries out gray-scale map conversion to 2 original color figures, and 8 direction templates for being then given using formula 1 carry out pointwise meter to image
Calculating, and maximum being taken for the new gray value of pixel, the corresponding template direction of maximum is the edge direction of the pixel.If
In image, new gray value is more than or equal to given threshold, then can primarily determine that the point is marginal point, and the direction of the point is edge
The direction of point, is otherwise non-edge point.Judge that formula draws marginal point finally according to edge, the purpose of edge thinning is reached, detection
Go out the edge of image.From all directions to the contrast of Sobel edge edge Detection results figure as shown in Figure 3.
Image Definition in the present invention based on eight weighted direction Sobel operators is calculated and method for analyzing performance
As follows：Experimental data is that the pixel gathered on imaging device based on liquid zoom lens is 640 × 480 and adds Gauss to make an uproar
Sound, salt-pepper noise, poisson noise and speckle noise by fuzzy-clear-fuzzy one group of (17) figure (this time only 6 width, wherein
9 width are positive Jiao's images), such as Fig. 4 is the evaluation function experimental image of dynamic auto focusing algorithm.
Actually one good Image Definition should have stronger unimodality, preferable unbiasedness and relatively
High sensitivity, while have certain antijamming capability to different noises.
Proposed by the present invention possess good anti-hot-tempered ability based on eight weighted direction Sobel operator evaluation functions, overcomes
Based on the shortcoming of conventional flat and vertical two directions Sobel operator evaluation function to noise-sensitive.
Fig. 5 (a) and Fig. 5 (b) are that the evaluation function based on two kinds of different operators adds same noise to same group of image
Resist hot-tempered property simulation result：
In figure 5 (a) is the anti-hot-tempered performance that traditional two directions Sobel operator evaluation function shows, as seen from the figure, this calculation
Son is very sensitive to noise, and the evaluation function of artwork presents good performance substantially, but adds Gaussian noise (gaussian
Noise), salt-pepper noise (salt＆pepper noise), poisson noise (poisson noise) and speckle noise (speckle
Noise unstability has been occurred as soon as after), evaluation function curve substantial deviation artwork curve and many local extremums occurred
Point, this confirm that two direction Sobel operator evaluation functions have the shortcomings that anti-hot-tempered property is not enough.
Comparatively speaking, Fig. 5 (b) be set forth herein based on eight weighted direction Sobel operator evaluation functions to different noises
Antijamming capability curve, as seen from the figure in artwork add Gaussian noise (gaussian noise), salt-pepper noise
(salt＆pepper noise), poisson noise (poisson noise) and speckle noise (speckle noise) all do not go out
Now deviate the phenomenon of artwork evaluation function curve, the evaluation function based on eight weighted direction Sobel operators that this explanation is proposed has
There is good anti-interference, preferably can apply in the automatic focusing algorithm under dynamic environment.
With reference to eight weighted direction Sobel edge edge operators and the dynamic auto tune of Step-varied back propagation search strategy in the present invention
Burnt algorithm performance testing procedure is as follows：First by the bionical vision imaging device based on liquid zoom lens to eight different scenes
It is imaged respectively, the focusing frequency of failure and used time that target object in each scene moves 10 times is then counted, finally draws focusing
Accuracy rate and average used time, detailed data such as table 1.In table, the focusing used time is when object produces change in displacement in visual field, executes one
Secondary automatic focusing process total used time, including gathering image, evaluation image sharpness computation and confirming extreme value, drive liquid lens burnt
Time away from three processes of change.
1 dynamic environment automatic focusing algorithm of table focusing accuracy rate and used time
Can be seen that from experimental data above：The present invention proposes automatic focusing algorithm, due to considering human vision pair
The stimulation of horizontal direction and vertical direction is most sensitive, and gradually weakens toward diagonally opposed sensitiveness.Therefore, image is being calculated
Edge during gradient to eight directions becomes to carry out weight distribution, make calculate object selection when more accurate, so focusing use
When not many than based on classical two directions Sobel operator algorithm of focusing, and under dynamic environment, this automatic focusing algorithm
Focusing rate of accuracy reached to 97.5%, test result indicate that：Automatic focusing algorithm proposed by the present invention has under dynamic environment
Very high focusing accuracy rate and real-time, are that the research of bionical visual field automatic focusing algorithm is had laid a good foundation.
The accompanying drawing is only schematically and draws not in scale.Although the present invention is entered already in connection with preferred embodiment
Description is gone, it is to be understood that protection scope of the present invention is not limited to embodiment as described herein.
Explanation and practice in conjunction with the present invention for disclosing here, the other embodiment of the present invention is for those skilled in the art
All will be readily apparent and understand.Illustrate and embodiment be to be considered only as exemplary, the true scope of the present invention and purport equal
It is defined in the claims.
Claims (5)
1. a kind of dynamic auto focusing algorithm based on improved sobel method, it is characterised in that：Process is realized in the focusing to be included
Step：
A) system original position is located at over focus, toward electric current augment direction to walk greatly in liquid zoom lens driving current range
Length is scanned for, and records respective image sharpness evaluation function value；
B) according to big step length searching process in step a, if under the Image Definition value of sampled point continuously occurs twice
Drop, then scanned for middle step-length with big step-length output current rightabout, and calculates corresponding evaluation function value in real time；
C) according to step length searching process in step b, if once declining occurs in the Image Definition value of sampled point,
Scanned for small step length with middle step-length output current rightabout, and calculate each sampled point evaluation function value in real time；If in Jiao
, there is once decline and is considered as declining previous sampled point for positive Jiao position, while entirely searching in the little step length searching that point is nearby carried out
Rope process terminates.
2. algorithm according to claim 1, it is characterised in that：The computational methods of described image gradient are：
In formula, α, β are respectively 0 °, 90 °, 180 °, 270 ° of direction marginal elements and 45 °, 135 °, 225 °, 315 ° of direction marginal elements
Corresponding weight coefficient, Bi(i=1,2...8) is the marginal element on eight directions of image.
3. algorithm according to claim 2, it is characterised in that：Marginal element B on eight directionsi(i=1,
2...8 computing formula) is：
B1=F (x, y) * d1B2=F (x, y) * d2
B3=F (x, y) * d3B4=F (x, y) * d4
B5=F (x, y) * d5B6=F (x, y) * d6
B7=F (x, y) * d7B8=F (x, y) * d8
Wherein, F (x, y) is gradation of image, and * is convolution algorithm, di(i=1,2...8) is boundary operator.
4. algorithm according to claim 3, it is characterised in that：The boundary operator diComputing formula be：
5. algorithm according to claim 1, it is characterised in that：Image definition evaluation standard is：
Wherein, x, y, M, N difference representative image pixel horizontal coordinate, vertical coordinate, horizontal pixel point sum and vertical pixel
Point sum.
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| CN110012218A (en) * | 2018-12-19 | 2019-07-12 | 杭州晨安科技股份有限公司 | A kind of auto focusing method under state substantially out of focus |
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