CN109886943A - A kind of image Focus field emission array implementation method based on number theoretic transform - Google Patents
A kind of image Focus field emission array implementation method based on number theoretic transform Download PDFInfo
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Abstract
The image Focus field emission array implementation method based on number theoretic transform that the invention discloses a kind of, belongs to imaging and passive imaging technical field.This method carries out noise reduction process to image first, then carries out piecemeal to the image after noise reduction and calculates the number theoretic transform of each width subgraph, obtains the frequency coefficient of corresponding subgraph.Choose the frequency coefficient of each width subgraph again to construct the sharpness information of image.The variance of all constituted set of subgraph sharpness information is finally found out, and using the variance yields as the Focus field emission array of entire image.The present invention is on the basis of carrying out noise reduction and piecemeal to image, the detailed information of image is extracted using number theoretic transform, 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, convenient for promoting the use of.
Description
Technical field
The invention belongs to automated imaging technical fields, and in particular to a kind of image Focus field emission array realization based on number theoretic transform
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.That typical method has a thin multi-scale wavelet coefficient and Focus field emission array, base based on warp wavelet
Focus field emission array in discrete cosine transform, the Focus field emission array based on Fourier transformation and Short-Time Fractional Fourier Transform it is poly-
Coke is estimated.These methods based on transformation have the characteristics that one it is common, extract high-frequency information after exactly converting to image, with
This is as image Focus field emission array value.Such methods and front are consistent in the way of thinking based on the method for edge extracting,
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
Focus field emission array is constructed by the way of transformation.Based on image transformation method be also easy it is affected by noise, and some convert
Computation complexity it is bigger, such as wavelet transformation and Fourier Transform of Fractional Order, not no mature 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 number theoretic transform, includes the following steps:
Step S1: the line number of original image pixels and columns are adjusted to 2nIntegral multiple, wherein n be positive integer, obtain
To image f (x, y), the line number and columns of image f (x, y) is indicated with M and N respectively；
Step S2: noise reduction process is carried out to image f (x, y), obtains image g (x, y)；
Step S3: carrying out piecemeal processing to image g (x, y), and obtaining size is 2n×2nThe subgraph S of pixeli(x, y),
Middle i=1,2 ..., M × N/22n；Subgraph SiThe variable-value of (x, y) are as follows: x=0,1 ..., 2n- 1, y=0,1 ..., 2n-
1；
Step S4: each width subgraph S is calculatediThe number theoretic transform of (x, y) obtains the frequency coefficient T of corresponding subgraphi(u,
V), wherein u=0,1 ..., 2n- 1, v=0,1 ..., 2n-1；
Step S5: each width subgraph frequency coefficient is chosen to construct the clarity of image and labeled as Fi；
Step S6: by all subgraph image sharpness FiThe set constituted is denoted as { Fi| i=1,2 ..., M × N/22n, it asks
The variance of the set, and using variance yields as the Focus field emission array value of entire image.
Preferably, noise reduction formula in the step S2 are as follows: g (x, y)=f (x, y) * ST；
Wherein ST is 4 × 4 symmetrical Filtering Templates, specifically:
Preferably, the number theoretic transform formula in the step S4 are as follows:
Wherein P is positive integer, since number theoretic transform is handled gray level image, P=256.Number theoretic transform is public
" (mod P) " indicates to carry out the operation that mould is P in formula, using P as the integer item of mould is defined as: ZP={ 0,1,2 ..., P-1 }；α
It is Z with the equal value of βPIn a certain numerical value, i.e. α, β ∈ ZP。
Preferably, the step S5 neutron image sharpness information FiIt is to be obtained by the ratio calculation of frequency coefficient,
Specific formula for calculation are as follows:
Compared with prior art, the invention has the advantages that: the principle of the invention is simple, to image carry out piecemeal
On the basis of, image detail information is extracted using number theoretic transform, has the advantages that computation complexity is low, while passing through step S2
The implementation of~S6 largely reduces the interference of noise on image detailed information, so that the obtained focusing of this method is surveyed
Degree has higher noise robustness, the anti-noise being suitable under the passive imaging system of camera, especially low contrast image-forming condition
Sound ability is stronger, is suitable for promoting the use of.
Detailed description of the invention
Fig. 1 is implementation steps block 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 image Focus field emission array implementation method based on number theoretic transform that the invention proposes a kind of, implementation step block diagram is as schemed
Shown in 1, in the present embodiment, n=3 is selected, then the specific steps refinement of this method are as follows:
The line number of original image pixels and columns are adjusted to 8 integral multiple by step S1, can be by cutting out to image
It cuts or interpolation is realized, just obtain image f (x, y) in this way.Here, the line number of f (x, y) and columns are indicated with M and N respectively.
Why picturedeep and columns are adjusted be because method proposed by the invention be to be realized based on image block, when
When n=3, need image to be divided into several width subgraphs that size is 8 × 8 pixels.
In order to reduce the influence of picture noise (mainly Gaussian noise, salt-pepper noise and multiplying property impact noise), next
Step S2~S6 play key effect.
Step S2 carries out noise reduction process to the image f (x, y) of previous step, obtains image g (x, y).Specific formula for calculation
Are as follows: g (x, y)=f (x, y) * ST.Wherein ST is 4 × 4 symmetrical Filtering Templates, is specifically defined are as follows:
In fact, due to the symmetry characteristic of Filtering Template ST and it in value part " mean value " effect, with filtering
Template ST carries out handling to be equivalent to being extracted the intermediate frequency information of image to image f (x, y), and has filtered out most of high-frequency noise letter
Breath.So it lays a good foundation for the quantification treatment of subsequent step.
Preliminary anti-noise sonication is realized by step S2.
Step S3 carries out piecemeal processing to the obtained image g (x, y) of step S2, and obtaining several width sizes is 8 × 8
Subgraph Si(x, y), wherein i=1,2 ..., M × N/64.The size for paying attention to subgraph is 8 × 8, therefore subgraph Si(x, y)
Variable-value are as follows: x=0,1 ..., 7, y=0,1 ..., 7.Why carrying out piecemeal processing to image is calculated for reduction
The considerations of complexity.This in traditional airspace filter calculating process using pixel-by-pixel point handled by the way of compared with, significantly
Reduce computation complexity.In addition, based on the calculation method of piecemeal, there are also the effects of smothing filtering, can further decrease noise
Influence to image definition quantized result.
Step S4 calculates each width subgraph SiThe number theoretic transform of (x, y) obtains the frequency coefficient T of corresponding subgraphi(u,
V), wherein u=0,1 ..., 7, v=0,1 ..., 7.
Here, number theoretic transform formula becomes:
Wherein P is positive integer, here, since the object of number theoretic transform processing is gray level image, P=256.Number theory becomes
Changing in formula " (mod P) " indicates to carry out the operation that mould is P, using P as the integer item of mould is defined as: ZP=0,1,2 ...,
255}.The value of α and β are as follows: α=2, β=2.
Step S5: each width subgraph frequency coefficient is chosen to construct the clarity of image and labeled as Fi, specific to count
Calculate formula are as follows:
Above-mentioned subgraph image sharpness FiCalculation formula be with the sum of intermediate frequency and high frequency coefficient divided by low frequency coefficient Ti(0,0),
The sum of intermediate frequency and high frequency coefficient are equivalent to the variance of image frequency domain coefficient, are capable of the clarity of effective quantized image, count simultaneously
The energy that the denominator in formula is substantially image is calculated, the ratio of the two can be effectively reduced the influence of noise.
Step S6: by all subgraph image sharpness FiThe set constituted is denoted as { Fi| i=1,2 ..., M × N/64 }, it asks
The variance of the set, and using variance yields as the Focus field emission array value of entire image.Good image is focused for a width, is wrapped
The detailed information contained is more, specifically has 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 S3 to image g (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, it is clear to pass through all subgraphs of calculating in step s 6 by the present invention
Spend FiVariance obtain Focus field emission array.
The variance yields that step S6 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 (4)
1. a kind of image Focus field emission array implementation method based on number theoretic transform, it is characterised in that: specific step is as follows:
Step S1: the line number of original image pixels and columns are adjusted to 2nIntegral multiple, wherein n be positive integer, obtain image
The line number and columns of f (x, y), image f (x, y) are indicated with M and N respectively；
Step S2: noise reduction process is carried out to image f (x, y), obtains image g (x, y)；
Step S3: carrying out piecemeal processing to image g (x, y), and obtaining size is 2n×2nThe subgraph S of pixeli(x, y), wherein i
=1,2 ..., M × N/22n；Subgraph SiThe variable-value of (x, y) are as follows: x=0,1 ..., 2n- 1, y=0,1 ..., 2n-1；
Step S4: each width subgraph S is calculatediThe number theoretic transform of (x, y) obtains the frequency coefficient T of corresponding subgraphi(u, v),
Wherein u=0,1 ..., 2n- 1, v=0,1 ..., 2n-1；
Step S5: each width subgraph frequency coefficient is chosen to construct the clarity of image and labeled as Fi；
Step S6: by all subgraph image sharpness FiThe set constituted is denoted as { Fi| i=1,2 ..., M × N/22n, seek the collection
The variance of conjunction, 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 number theoretic transform as described in claim 1, it is characterised in that: the step
Noise reduction formula in S2 are as follows: g (x, y)=f (x, y) * ST；Wherein ST is 4 × 4 symmetrical Filtering Templates, specifically:
3. the image Focus field emission array implementation method based on number theoretic transform as described in claim 1, it is characterised in that: the step
Number theoretic transform formula in S4 are as follows:
Wherein P is positive integer；(mod P) indicates to carry out the operation that mould is P；Using P as the integer item of mould is defined as: ZP=0,1,
2 ..., P-1 }；α, β ∈ ZP。
4. the image Focus field emission array implementation method based on number theoretic transform as described in claim 1, it is characterised in that: the step
S5 neutron image sharpness information FiIt is to be obtained by the ratio calculation of frequency coefficient, specific formula for calculation are as follows:
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112330757A (en) * | 2019-08-05 | 2021-02-05 | 复旦大学 | Complementary color wavelet measurement for evaluating color image automatic focusing definition |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101946188A (en) * | 2008-02-20 | 2011-01-12 | 剑桥定位***有限公司 | Handle the satellite radio signal that is received |
| CN103870438A (en) * | 2014-02-25 | 2014-06-18 | 复旦大学 | Circuit structure using number theoretic transform for calculating cyclic convolution |
| CN104917970A (en) * | 2015-06-18 | 2015-09-16 | 中国工程物理研究院激光聚变研究中心 | Automatic focusing method |
| CN105354817A (en) * | 2015-09-25 | 2016-02-24 | 济南中维世纪科技有限公司 | Noise image automatic focusing method |
| CN109035176A (en) * | 2018-08-24 | 2018-12-18 | 宁波永新光学股份有限公司 | A kind of micro-image clarity evaluation method |
-
2019
- 2019-01-25 CN CN201910102017.0A patent/CN109886943A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101946188A (en) * | 2008-02-20 | 2011-01-12 | 剑桥定位***有限公司 | Handle the satellite radio signal that is received |
| CN103870438A (en) * | 2014-02-25 | 2014-06-18 | 复旦大学 | Circuit structure using number theoretic transform for calculating cyclic convolution |
| CN104917970A (en) * | 2015-06-18 | 2015-09-16 | 中国工程物理研究院激光聚变研究中心 | Automatic focusing method |
| CN105354817A (en) * | 2015-09-25 | 2016-02-24 | 济南中维世纪科技有限公司 | Noise image automatic focusing method |
| CN109035176A (en) * | 2018-08-24 | 2018-12-18 | 宁波永新光学股份有限公司 | A kind of micro-image clarity evaluation method |
Non-Patent Citations (7)
| Title |
|---|
| LIQIANG GUO等: ""Focus Measure Based on the Image Moments"", 《2018 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA)》, 7 October 2018 (2018-10-07), pages 1151 - 1156 * |
| LIQIANG GUO等: ""Focus Measure Based on the Image Moments"", 《2018 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA)》, pages 1151 - 1156 * |
| SANG-YONG LEE等: ""Enhanced Autofocus Algorithm Using Robust Focus Measure and Fuzzy Reasoning"", 《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY》, vol. 18, no. 9, pages 1237 - 1246, XP011226689, DOI: 10.1109/TCSVT.2008.924105 * |
| 刘兵 等: ""一种新的数字图像置乱方法"", 《计算机工程与应用》, vol. 46, no. 6, 28 February 2010 (2010-02-28), pages 181 - 184 * |
| 刘兵 等: ""一种新的数字图像置乱方法"", 《计算机工程与应用》, vol. 46, no. 6, pages 181 - 184 * |
| 向东 等: ""数字全息波前准确重建的实验研究"", 《激光技术》, vol. 41, no. 3, 31 May 2017 (2017-05-31), pages 406 - 410 * |
| 向东 等: ""数字全息波前准确重建的实验研究"", 《激光技术》, vol. 41, no. 3, pages 406 - 410 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112330757A (en) * | 2019-08-05 | 2021-02-05 | 复旦大学 | Complementary color wavelet measurement for evaluating color image automatic focusing definition |
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