CN109801288A - A kind of image Focus field emission array implementation method based on directional statistics characteristic - Google Patents
A kind of image Focus field emission array implementation method based on directional statistics characteristic Download PDFInfo
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Abstract
The image Focus field emission array implementation method based on directional statistics characteristic that the invention discloses a kind of, belongs to technical field of image processing.This method first pre-processes image, and treated image is then carried out piecemeal and calculates the directional statistics characteristic of each width subgraph, obtains the statistical value on corresponding subgraph four direction.The sharpness information of subgraph is constructed by the statistical value on four direction again.The variance of all constituted set of subgraph sharpness information is finally sought, and using the variance yields as the Focus field emission array of entire image.The present invention is by the way of image block and directional statistics features extracts image detail information, have the advantages that principle is simple, computation complexity is low, implementation through the above steps simultaneously, the interference of noise on image detailed information is reduced, the especially noise robustness under low contrast image-forming condition is strong.This method is suitable for the passive imaging system of camera, popularizing value with higher.
Description
Technical field
The invention belongs to technical field of image processing, and in particular to a kind of image Focus field emission array based on directional statistics characteristic
Implementation method.
Background technique
Photographing device in daily life, such as the monitoring of slr camera, the mobile phone with camera function and crossing are grabbed
Shooting system etc. can obtain clearly image.However the acquisition of clear image is to rely on the automatic focusing performance of photographing device
It realizes.Currently, photographing device in the market mainly uses the Techniques of Automatic Focusing of imaging and passive imaging.Its core is one use of design
In the Focus field emission array of evaluation image definition, clearest image and preservation are selected by Focus field emission array.Therefore, a performance
Excellent Focus field emission array implementation method directly influences the quality of captured image.
Be using relatively broad image Focus field emission array method at present constructed based on image detail information, such as based on
The Focus field emission array of Edge extraction.Typical method has image single order Gaussian derivative method, Second Derivative Methods, single order local derviation
Counting method, gradient summation method and Laplce's summation method etc..The essence of such method is construction one having a size of 3 × 3
Or 5 × 5 convolution masks, convolution algorithm is carried out with the template and entire image.Convolution algorithm the result is that extracting image
Marginal information, then using take absolute value or square summation in the form of construct the Focus field emission array of entire image.Such construct
There are following two major defects for the method for Focus field emission array.It is that convolution algorithm complexity is high first, needs the institute to entire image
There is pixel to carry out traversing operation, there is presently no the fast algorithm on more mature fast algorithm, especially hardware device,
So that the focusing real-time index of such Focus field emission array method is poor.The followed by noise of image and marginal information belongs to high frequency
Information can enhance noise information after convolution algorithm, that is to say, that such Focus field emission array eventually leads to mistake vulnerable to influence of noise
Focusing accidentally.
In addition a kind of Focus field emission array method is the method based on image transformation, i.e., the height of image is extracted at transform domain (frequency domain)
Frequency information constructs Focus field emission array.The ratio of typical method has a thin multi-scale wavelet coefficient and high frequency and low-frequency wavelet coefficients,
Focus field emission array based on discrete cosine transform, the Focus field emission array based on Fourier transformation and Short-Time Fractional Fourier Transform
Focus field emission array.These methods based on transformation have the characteristics that one it is common, extract high-frequency information after exactly converting to image,
With this as image Focus field emission array value.Such methods are consistent in the way of thinking based on the method for edge extracting with front
, emphasize high-frequency information.Only the former is Focus field emission array to be constructed using the method for convolution in airspace, and the latter is in frequency
Domain constructs Focus field emission array by the way of transformation.Based on image transformation method be also easy it is affected by noise, and some become
The computation complexity changed is bigger, such as wavelet transformation and Fourier Transform of Fractional Order, unmature hardware fast algorithm.
Above-mentioned two classes method is to construct Focus field emission array with the global information of entire image there are one common feature.Such as
The background of fruit image is relatively uniform or smoother, easily affected by noise at this time, so that corresponding Focus field emission array can not be anti-
Mirror the sharpness information of image.Such as with the camera function of mobile phone under the weaker indoor or night scenes of illumination condition
It takes pictures, we can have found that the automatic focusing function of (mobile phone) camera is not handy, and captured image out exists fuzzy existing
As and have granular sensation.Here it is an embodiments of Focus field emission array algorithm failure.Therefore, how to construct with noise robustness
Focus field emission array has important research significance and practical value.
Summary of the invention
Aiming at the problems existing in the prior art, the present invention provides a kind of, and the image with very noisy robustness focuses survey
Implementation method is spent, the technical solution used in the present invention is as follows:
A kind of image Focus field emission array implementation method based on directional statistics characteristic, includes the following steps:
The line number of original image pixels and columns: being adjusted to the integral multiple of 2n+1 by step S1, and wherein n is one just whole
Number and n >=2, obtain image f (x, y), the line number and columns of image f (x, y) is indicated with M and N respectively；
Step S2: carrying out piecemeal processing to image f (x, y), obtains the subgraph that size is (2n+1) × (2n+1) pixel
Si(x, y), wherein i=1,2 ..., M × N/ (2n+1)2；Subgraph SiThe variable-value of (x, y) are as follows: x=0,1 ..., 2n, y
=0,1 ..., 2n；
Step S3: each width subgraph S is calculatediThe directional statistics characteristic of (x, y), that is, calculate corresponding subgraph 0 °,
Statistical value on 45 °, 90 ° and 135 ° this four directions, is respectively labeled as Ti(0°)、Ti(45°)、Ti(90 °) and Ti(135°)；
Step S4: it chooses statistical value of each width subgraph on 0 °, 45 °, 90 ° and 135 ° this four direction and carrys out constructor
The clarity of image is simultaneously labeled as Fi；
Step S5: by all subgraph image sharpness FiThe set constituted is denoted as { Fi| i=1,2 ..., M × N/ (2n+1)2,
The variance of the set is sought, and using variance yields as the Focus field emission array value of entire image.
Preferably, the following formula meter of statistical value in the step S3, on 0 °, 45 °, 90 ° and 135 ° this four direction
It calculates:
Wherein angle, θ=0 °, 45 °, 90 °, 135 °, σ () represents the variance operation in statistics, and μ () represents statistics
Mean operation in.
Preferably, the step S4 neutron image clarity FiIt is calculated using average weighted mode, specific formula are as follows:
Fi=ω1×Ti(0°)+ω2×Ti(45°)+ω3×Ti(90°)+ω4×Ti(135°)
Wherein ω1+ω2+ω3+ω4=1.
Compared with prior art, the invention has the advantages that: the principle of the invention is simple, using the side of image block
Formula and directional statistics feature extract image detail information, have the advantages that computation complexity is low, while passing through step S2~S5
Implementation, the interference of noise on image detailed information is largely reduced, so that the obtained Focus field emission array of this method has
Higher noise robustness, the antinoise energy being suitable under the passive imaging system of camera, especially low contrast image-forming condition
Power is stronger, is suitable for promoting the use of.
Detailed description of the invention
Fig. 1 is implementation steps block diagram of the invention.
Fig. 2 is implementation steps S3 neutron image directional statistics characteristic schematic diagram of the invention.
Specific embodiment
Technical solution of the present invention is understood for the ease of technical staff, now in conjunction with Figure of description and embodiment to the present invention
Technical solution be described in further detail.
The invention proposes a kind of image Focus field emission array implementation method based on directional statistics characteristic, implementation step block diagram
As shown in Figure 1, in the present embodiment, choose n=4, then each step refinement of method are as follows:
The line number of original image pixels and columns: being adjusted to 9 integral multiple by step S1, obtains image f (x, y), here
Assuming that the line number and columns of f (x, y) are respectively M and N.Method of adjustment can using image pixel interpolation method zoom in and out or
The clique picture of person's interception image central area makes the integral multiple of its line number and columns 9.Why to picturedeep and column
It is that can reduce subsequent reality because method proposed by the invention is realized on the basis of image block that number, which is adjusted,
Apply the computation complexity of step.
In order to reduce the influence of picture noise (mainly Gaussian noise, salt-pepper noise and multiplying property impact noise), next
Step S2~S5 play key effect.
Step S2: piecemeal processing is carried out to the image f (x, y) of previous step, obtaining several width sizes is 9 × 9 pixels
Subgraph Si(x, y), wherein i=1,2 ..., M × N/81.The size for paying attention to subgraph is 9 × 9 pixels, therefore subgraph Si
The variable-value of (x, y) are as follows: x=0,1 ..., 8, y=0,1 ..., 8.Why carrying out piecemeal processing to image is for drop
The considerations of low computation complexity.This in traditional airspace filter calculating process using pixel-by-pixel point handled by the way of phase
Than greatly reducing computation complexity.In addition, can reduce and make an uproar there are also the effect of smothing filtering based on the calculation method of piecemeal
Influence of the sound to image definition quantized result.
Step S3: each width subgraph S is calculatediThe directional statistics characteristic of (x, y), obtain corresponding subgraph 0 °, 45 °,
Statistical value on 90 ° and 135 ° of this four directions, is respectively labeled as Ti(0°)、Ti(45°)、Ti(90 °) and Ti(135°).Fig. 2 is
Subgraph directional statistics characteristic schematic diagram.In the figure, there are four reference axis, are 0 °, 45 °, 90 ° and 135 ° this four sides respectively
To solid " dot " represents the pixel of subgraph.The reference axis in any one of this four direction direction all passes through 9 pictures
The following formula of the grey scale pixel value of the pixel passed through is calculated directional statistics characteristic by vegetarian refreshments:
Wherein angle, θ=0 °, 45 °, 90 °, 135 °, σ () represents the variance operation in statistics, and μ () represents statistics
Mean operation in.
In step s3, variance operation can extract the dispersion information of image in one direction, the bigger table of the value
Show that the variation of pixel value in this direction is more violent, representative image is more clear.Meanwhile the mean operation in the step be equivalent into
The influence of noise can be effectively removed in row low-pass filtering.Statistics in this direction is defined by the ratio of variance and mean value
Characteristic can be good at the detailed information for characterizing subgraph, and reduce influence of noise.
Step S4: it chooses statistical value of each width subgraph on 0 °, 45 °, 90 ° and 135 ° this four direction and carrys out constructor
The clarity of image is simultaneously labeled as Fi。
In step s 4, subgraph image sharpness FiIt is calculated using average weighted mode, specific formula are as follows:
Fi=ω1×Ti(0°)+ω2×Ti(45°)+ω3×Ti(90°)+ω4×Ti(135°)
Wherein ω1=0.25, ω2=0.25, ω3=0.25, ω4=0.25.If the texture information in image is brighter
It shows, such as the texture information in image in horizontal direction is more, can give ω1Higher weight is assigned, but to meet following item
Part:
ω1+ω2+ω3+ω4=1.
Step S5: by all subgraph characteristic information FiThe set constituted is denoted as { Fi| i=1,2 ..., M × N/81 },
The variance of the set is sought, and using variance yields as the Focus field emission array value of entire image.Good image, institute are focused for a width
The detailed information for including is more, such as marginal information or zone boundary information, and these detailed information are both present in image
Regional area in.And the extraction of this partial information is most important for the calculating of Focus field emission array.This is also that the present invention implements step
Another reason of piecemeal operation is carried out in rapid S2 to image f (x, y).In fact, image is more clear, the brightness change of image
It is more obvious, is exactly that the pixel value of clear image has biggish dispersion from the viewpoint of image pixel value.In statistics
It is upper to measure this discrete feature usually using variance.Therefore, the present invention passes through in step s 5 calculates all subgraph features
Information FiVariance obtain Focus field emission array.
The variance yields that step S5 is calculated means that more greatly sharpness information included in each width subgraph
Contrast it is bigger, i.e., the detailed information for including in image is more.
It should be noted that above-described embodiment can be freely combined as needed.The above is only of the invention preferred
Embodiment, it is noted that for those skilled in the art, in the premise for not departing from the principle of the invention
Under, several improvements and modifications can also be made, these modifications and embellishments should also be considered as the scope of protection of the present invention.
Claims (3)
1. a kind of image Focus field emission array implementation method based on directional statistics characteristic, it is characterised in that: specific step is as follows:
The line number of original image pixels and columns: being adjusted to the integral multiple of 2n+1 by step S1, wherein n be integer and n >=2,
Image f (x, y) is obtained, the line number and columns of image f (x, y) is indicated with M and N respectively；
Step S2: carrying out piecemeal processing to image f (x, y), obtains the subgraph S that size is (2n+1) × (2n+1) pixeli(x,
Y), wherein i=1,2 ..., M × N/ (2n+1)2；Subgraph SiThe variable-value of (x, y) are as follows: x=0,1 ..., 2n, y=0,
1 ..., 2n；
Step S3: each width subgraph S is calculatediThe directional statistics characteristic of (x, y) calculates corresponding subgraph at 0 °, 45 °, 90 °
Statistical value on 135 ° of this four directions, is respectively labeled as Ti(0°)、Ti(45°)、Ti(90 °) and Ti(135°)；
Step S4: statistical value of each width subgraph on 0 °, 45 °, 90 ° and 135 ° this four direction is chosen to construct subgraph
Clarity and be labeled as Fi；
Step S5: by all subgraph image sharpness FiThe set constituted is denoted as { Fi| i=1,2 ..., M × N/ (2n+1)2, it asks
The variance of the set, and using variance yields as the Focus field emission array value of entire image.
2. the image Focus field emission array implementation method based on directional statistics characteristic as described in claim 1, it is characterised in that: described
In step S3, the statistical value on 0 °, 45 °, 90 ° and 135 ° this four direction is calculated with following formula:
Wherein angle, θ=0 °, 45 °, 90 °, 135 °, σ () represents the variance operation in statistics, and μ () is represented in statistics
Mean operation.
3. the image Focus field emission array implementation method based on directional statistics characteristic as described in claim 1, it is characterised in that: described
Step S4 neutron image clarity FiIt is calculated using average weighted mode, specific formula are as follows:
Fi=ω1×Ti(0°)+ω2×Ti(45°)+ω3×Ti(90°)+ω4×Ti(135°)
Wherein ω1+ω2+ω3+ω4=1.
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