CN107240074A - Based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm - Google Patents
Based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm Download PDFInfo
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Abstract
Laser first, is divided into by two-beam road by first polarization beam apparatus based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm there is provided a kind of the invention belongs to the holographic field of optical scanner, wherein, the first pupil is random phase pupil p1(x, y)=exp j [2 π r (x, y)], the second pupil are p2(x, y)=1, by setting pupil, speckle noise form is converted to by the hot-tempered sound of defocus；Then gray-scale map reconstruction obtained obtains the total entropy H (s, t) of two-dimensional histogram using the optimal Entropic method of two dimension, and as fitness function, using Genetic algorithm searching optimal threshold.The present invention using two-dimentional optimal Entropic method selected threshold, and accelerates convergence rate using Improving Genetic Algorithm, improves threshold search efficiency, so that obtaining optimal threshold carries out image segmentation, effectively improve slice imaging quality by extracting gradation of image information.
Description
Technical field
The invention belongs to the holographic field of optical scanner, it is related to a kind of based on two-dimentional optimal Entropic method and genetic algorithm
The hot-tempered sound removing method of defocus.
Background technology
Optical scanner holographic technique (Optical Scanning Holography), abbreviation OSH is Digital Holography
A branch, it is proposed earliest by Poon and Korpel, OSH the information of three-dimensional body can be stored as two dimension holography
Figure.Since being proposed from this technology, obtained extensively in fields such as scanning holographic microscope, 3D rendering identification and 3D optical remote sensings
General application.In OSH, when going out the sectioning image of each layer from hologram reconstructing, traditional method for reconstructing can make obtained reconstruction figure
As the hot-tempered sound of defocus containing other layer of section, cause image quality poor；Therefore, the elimination of the hot-tempered sound of defocus turns into the weight that we study
Point.
At present, document《Thresholding Using Two-Dimensional Histogram andFuzzy
Entropy Principle》Disclosed in one kind noisy image is handled using two-dimensional histogram threshold value and fuzzy entropy, it is but two-dimentional
Entropic method design parameter is more, and the speed of service is slower, and effect is less desirable.Document《Image Denoising Based
on Genetic Algorithm》Disclosed in it is a kind of using genetic algorithm progress image denoising, but the conventional genetic method is searched
Rope efficiency is slower, it is impossible to be quickly found out globally optimal solution, i.e., can not efficiently carry out image denoising processing.
The content of the invention
It is an object of the invention in view of the above-mentioned problems, being calculated there is provided a kind of based on the optimal Entropic method of two dimension with heredity
The hot-tempered sound removing method of defocus in the OSH of method, by extracting gradation of image information, threshold is chosen using two-dimentional optimal Entropic method
Value, and accelerate convergence rate using Improving Genetic Algorithm, threshold search efficiency is improved, so that obtaining optimal threshold carries out image
Segmentation, effectively improves slice imaging quality.
To achieve the above object, the technical solution adopted by the present invention is：
Based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm, comprise the following steps：
Laser is divided into two-beam road by step 1. by first polarization beam apparatus, wherein, the first pupil is random phase
Pupil p1(x, y)=expj [2 π r (x, y)], r (x, y) are the random function between (0,1), and the second pupil is p2(x, y)=1,
Wherein, x, y represent spatial coordinated information respectively；It will be gathered respectively through the two of two-beam road light beams by second polarization beam apparatus
Interference forms Fresnel interference fringe after light；
Step 2. carries out two-dimensional scan using Fresnel interference fringe measuring targets, and is swept by photoelectric detector reception
Optical information after retouching, so as to obtain the hologram of object under test；
Step 3. does gained hologram after Fourier transformation, with the random phase optical transfer function with distance parameter
Frequency-domain expression conjugate multiplication after, then by inverse Fourier transform, then the reconstruction gray-scale map of object under test is obtained, wherein wrapping
Containing the defocus noise in speckle noise form；
Step 4. will rebuild obtained gray-scale map using the optimal Entropic method of two dimension, and setting threshold value is vectorial (s, t),
The total entropy for obtaining two-dimensional histogram is H (s, t)：
Wherein, PARepresent background probability, PBRepresent destination probability, HAThe entropy of target area is represented, H represents whole image
Entropy；
With H (s, t) for fitness function, using Genetic algorithm searching optimal threshold.
Further, by setting two pupils respectively in the step 1, then optical transfer function is expressed as：
Wherein, k0Represent wave number, kxAnd kySpatial frequency is represented, f is the focal length of lens, P1It is that the first pupil is random phase
Pupil p1The Fourier transformation of (x, y), Zi, i=1,2,3 ..., n represents i-th layer of object to the distance of the plane of scanning motion, and n is total layer
Number.
Further, the hologram that object under test is obtained in the step 2 is expressed as：
Wherein, g (x, y) represents hologram, F-1, F represent inverse Fourier transform and Fourier transformation, I (x, y respectively；zi)
Represent the complex amplitude function of i-th layer of determinand.
The detailed process of the step 3 is：
Select p1dAnd p2dAs decoding pupil, wherein, p1d(x, y)=1,For rebuilding
I-th layer pattern, then it is, as follows by embodying for optical transfer function reconstruction image：
Wherein, I ' (x, y；zi) represent i-th layer of object under test reconstruction image information, N (x, y；zi) represent speckle noise；
Then：
The detailed process of genetic algorithm is in the step 4：
(1) random generation initialization population；
(2) selection initial population scale is popsize, maximum evolutionary generation gmax；
(3) fitness function：Fitness function is used as using H (s, t)；
(4) encode：Encoded using eight gray level images, threshold parameter is set to 0≤s, and individual UVR exposure is by t≤255
16 binary codes, high eight-bit represents s, and low eight represent t；High eight-bit is similarly decoded as s by decoding, and low eight are decoded as t；
(5) selection opertor：Be combined using elitism strategy and roulette method, according to elitism strategy by colony 10% it is excellent
Elegant individual is copied directly to the next generation, and then remaining individual is selected with roulette method；
(6) crossover operator：The crossover probability p of setting search early stagec=0.85, the crossover probability in search later stage is pc=
0.65；
(7) mutation operator：Select parabolic type operator pm, it is defined as follows：
Wherein, pmmax=20pmmin, pmmin=pb、pbFor basic mutation probability, g ∈ (1, gmax)；
(8) termination algorithm：The termination algorithm when reaching maximum evolutionary generation, that is, obtain optimal threshold.
The beneficial effects of the present invention are：
(1) defocus noise is changed into speckle noise form by the present invention by setting pupil, then by two-dimentional optimal histogram
Entropy method is combined the method for removing defocus noise in OSH with genetic algorithm, and two-dimentional best entropy method takes full advantage of the gray scale of pixel
Spatial correlation information between distributed intelligence and pixel, improves the noiseproof feature of Threshold segmentation；
(2) present invention accelerates the convergence of search procedure using Revised genetic algorithum, has finally given global optimum
Solution；Genetic algorithm application includes robotics, image procossing, automatically controlled, therefore the present invention is applied to many fields,
Application is very wide；
(3) not only implementation is simple, be easy to operation by the present invention, while having very strong practicality, is adapted to promote the use of.
Brief description of the drawings
Fig. 1 provides the stream based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm for the present invention
Journey schematic diagram.
Fig. 2 is the OSH system basic block diagrams that use in embodiment.
Fig. 3 be embodiment in use artwork.
Fig. 4 be embodiment in random phase function encode OSH systems in reconstruction image.
Fig. 5 is reconstruction image to be carried out to obtain result after the hot-tempered sound of defocus is eliminated in embodiment.
Fig. 6 is two-dimensional histogram in embodiment step 4.
Embodiment
With reference to embodiment and accompanying drawing, the present invention is described in detail.
The present embodiment provides a kind of based on the two-dimentional optimal hot-tempered sound removing method of the defocus of Entropic method and genetic algorithm, its
Schematic flow sheet is as shown in figure 1, in the present embodiment, system architecture is as shown in Fig. 2 its artwork is as shown in figure 3, specific implementation process
Comprise the following steps：
Step 1. uses system construction drawing as shown in Figure 2, and the angular frequency sent by same lasing light emitter passes through for ω light
First polarization beam apparatus is divided into two-beam road, wherein the first pupil is random phase pupil p1(x, y)=expj [2 π r (x,
Y)], r (x, y) be (0,1) between random function, by transmissive spatial light modulator (Spatial Light Modulator,
SLM) realize；And the second pupil is p2(x, y)=1, wherein, x, y represents spatial coordinated information respectively；This two light beams are passed through
Interference forms Fresnel interference fringe after second polarization beam apparatus optically focused；Then, optical transfer function is expressed as：
Wherein, k0Represent wave number, kxAnd kySpatial frequency is represented, f is the focal length of lens, P1It is that the first pupil is random phase
Pupil p1The Fourier transformation of (x, y), Zi, i=1,2,3 ..., n represents i-th layer of object to the distance of the plane of scanning motion, and n is total layer
Number；
Step 2. is scanned by above-mentioned Fresnel interference fringe measuring targets, and is received using photoelectric detector
Optical information after scanning, so as to obtain the hologram of object under test：
Wherein, g (x, y) represents hologram, F-1, F represent inverse Fourier transform and Fourier transformation, I (x, y respectively；zi)
Represent the complex amplitude function of i-th layer of determinand；
Step 3. does the hologram of gained after Fourier transformation, with the random phase optical delivery letter with distance parameter
After the conjugate multiplication of several frequency domain tabular forms, then by inverse Fourier transform, you can obtain the reconstruction image of object under test；By right
The design of pupil, the defocus noise in traditional slice imaging can be expressed as the form of speckle noise by we, as shown in Figure 4；
In this example, in order to rebuild i-th layer of section, we select p1dAnd p2dAs decoding pupil, wherein, p1d(x,y)
=1,Therefore, described in step 3 by optical transfer function reconstruction image embody as
Under：
Wherein, I ' (x, y；zi) represent i-th layer of object under test reconstruction image information, N (x, y；zi) represent speckle noise；
Then：
Represent that i-th layer of object under test rebuilds the noise brought in figure by jth layer pattern；
The gray-scale map that step 4. obtains reconstruction is most preferably determined with two-dimentional optimal Entropic method in two dimensional gray space search
Plan variable determines maximum entropy；In two-dimentional optimal Entropic method, a threshold value vector (s, t) is set, if selected ash
The intensity value ranges for spending figure are (0, L-1), and two-dimensional histogram is divided into four regions as shown in Figure 6, BlockAAnd BlockBPoint
Background and target, Block are not representedCAnd BlockDFor edge noise region, then have：
Background probability:
Destination probability:
By the calculation formula of whole image entropy：
For BlockAAnd BlockBTwo regions, have：
For BlockCAnd BlockDEdge noise region, value be far smaller than on two-dimensional histogram diagonal target and
The value of background, therefore can make rationally approximately to ignore, then BlockAAnd BlockBThe relevant entropy of probability distribution is respectively：
Now the total entropy of two-dimensional histogram is：
According to principle of maximum entropy, there is optimal threshold：(sopt,topt)=Arg { maxH (s, t) }
Wherein, Arg { } represents function of negating；
Step 5. improves threshold search efficiency with improved adaptive GA-IAGA, eventually finds optimal threshold value, finally obtains place
Manage the less image of the preferable noise of effect；
The characteristics of for two-dimentional optimal Entropic method, improvement is made to traditional genetic algorithm, particularly to selection, handed over
Setting is optimized in the operators such as fork, variation, and specific improvement situation is as follows:
(1) random generation initialization population；
(2) selection initial population scale is popsize=50, maximum evolutionary generation gmax=100；
(3) fitness function：With
It is used as fitness function；
(4) encode：Emulation experiment is carried out using eight gray level images, threshold parameter is set to 0≤s, t≤255, by individual volume
Code is 16 binary codes, and high eight-bit represents s, and low eight represent t；High eight-bit is similarly decoded as s, low eight decodings by decoding
For t；
(5) selection opertor：Be combined using elitism strategy and roulette method, according to elitism strategy by colony 10% it is excellent
Elegant individual is copied directly to the next generation, and then remaining individual is selected with roulette method；
(6) crossover operator：The crossover probability of traditional genetic algorithm self-consistentency is improved, the intersection for searching for early stage is general
Rate pc=0.85, the crossover probability in search later stage is pc=0.65, early stage (0≤g≤50) crossover probability height make individual update compared with
It hurry up, bigger solution space can be reached, and energy reduction obtains the probability of non-optimal solution, the later stage (50≤g≤100) reduces crossover probability,
Accelerate convergence rate, globally optimal solution is obtained as early as possible；
(7) mutation operator：Select parabolic type operator pm, it is defined as follows：
In above formula, pmmax=20pmmin,pmmin=pb,g∈(1,gmax), in evolution early stage, mid-term and later stage with difference
Probability becomes the effect that dissident is optimal, in evolution early stage, to be made a variation compared with small probability, maintains a good evolution modelling；Enter
Change mid-term to increase mutation probability, improve search capability, it is to avoid be absorbed in local optimum；Later stage of evolution, reduces mutation probability, accelerates
Speed of the algorithmic statement to globally optimal solution；pbFor basic mutation probability, it can be estimated as the following formula：
Work as pb≈0.009,gmaxWhen=100, mutation operator p can be obtainedmFor：
pm=pm(g)=0.09-0.0000684 × (g-50)2；
(8) termination algorithm：The termination algorithm when reaching maximum evolutionary generation,
When genetic algorithm finally meets end condition, using the individual overall situation as genetic algorithm with highest fitness
Optimal solution, i.e. maxH (s, t), by (sopt,toptThe optimal threshold that)=Arg { maxH (s, t) } must can be split to reconstruction image
Value, obtains reconstruction image after denoising as shown in Figure 5.
The foregoing is only a specific embodiment of the invention, any feature disclosed in this specification, except non-specifically
Narration, can alternative features equivalent by other or with similar purpose replaced；Disclosed all features or all sides
Method or during the step of, in addition to mutually exclusive feature and/or step, can be combined in any way.
Claims (5)
1. based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm, comprise the following steps：
Laser is divided into two-beam road by step 1. by first polarization beam apparatus, wherein, the first pupil is random phase pupil p1
(x, y)=expj [2 π r (x, y)], r (x, y) are the random function between (0,1), and the second pupil is p2(x, y)=1, wherein,
X, y represent spatial coordinated information respectively；By respectively through the two of two-beam road light beams after second polarization beam apparatus optically focused
Interference forms Fresnel interference fringe；
Step 2. carries out two-dimensional scan using Fresnel interference fringe measuring targets, and is received by photoelectric detector after scanning
Optical information, so as to obtain the hologram of object under test；
Step 3. does gained hologram after Fourier transformation, the frequency with the random phase optical transfer function with distance parameter
After the conjugate multiplication of domain expression formula, then by inverse Fourier transform, then obtain the reconstruction gray-scale map of object under test, wherein comprising in
The defocus noise of speckle noise form；
Step 4. will rebuild obtained gray-scale map using the optimal Entropic method of two dimension, and setting threshold value is vectorial (s, t), obtains
The total entropy of two-dimensional histogram is H (s, t)：
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Wherein, PARepresent background probability, PBRepresent destination probability, HAThe entropy of target area is represented, H represents the entropy of whole image；
With H (s, t) for fitness function, using Genetic algorithm searching optimal threshold.
2. based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm as described in claim 1, it is special
Levy and be, by setting two pupils respectively in the step 1, then optical transfer function is expressed as：
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Wherein, k0Represent wave number, kxAnd kySpatial frequency is represented, f is the focal length of lens, P1It is that the first pupil is random phase pupil p1
The Fourier transformation of (x, y), Zi, i=1,2,3 ..., n represents i-th layer of object to the distance of the plane of scanning motion, and n is total number of plies.
3. based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm as described in claim 2, it is special
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Wherein, g (x, y) represents hologram, F-1, F represent inverse Fourier transform and Fourier transformation, I (x, y respectively；zi) represent
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4. based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm as described in claim 1, it is special
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Select p1dAnd p2dAs decoding pupil, wherein, p1d(x, y)=1,For rebuilding i-th layer
Figure, then it is, as follows by embodying for optical transfer function reconstruction image：
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<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<msub>
<mi>k</mi>
<mi>y</mi>
</msub>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mn>2</mn>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<msub>
<mi>k</mi>
<mi>x</mi>
</msub>
</mrow>
<mi>f</mi>
</mfrac>
<mo>,</mo>
<mfrac>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<msub>
<mi>k</mi>
<mi>y</mi>
</msub>
</mrow>
<mi>f</mi>
</mfrac>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>=</mo>
<msup>
<mi>I</mi>
<mo>&prime;</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>;</mo>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>N</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>;</mo>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein, I ' (x, y；zi) represent i-th layer of object under test reconstruction image information, N (x, y；zi) represent speckle noise；Then：
<mrow>
<mi>N</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>;</mo>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>&NotEqual;</mo>
<mi>j</mi>
</mrow>
</munder>
<msup>
<mi>F</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>{</mo>
<mi>F</mi>
<mo>{</mo>
<mi>I</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>;</mo>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>}</mo>
<mo>*</mo>
<mi>exp</mi>
<mo>&lsqb;</mo>
<mi>j</mi>
<mfrac>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>z</mi>
<mi>j</mi>
</msub>
</mrow>
<mrow>
<mn>2</mn>
<msub>
<mi>k</mi>
<mn>0</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msubsup>
<mi>k</mi>
<mi>x</mi>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>k</mi>
<mi>y</mi>
<mn>2</mn>
</msubsup>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>*</mo>
<msubsup>
<mi>P</mi>
<mn>1</mn>
<mo>*</mo>
</msubsup>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<msub>
<mi>k</mi>
<mi>x</mi>
</msub>
</mrow>
<mi>f</mi>
</mfrac>
<mo>,</mo>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<msub>
<mi>k</mi>
<mi>y</mi>
</msub>
</mrow>
<mi>f</mi>
</mfrac>
<mo>)</mo>
</mrow>
<mo>*</mo>
<msub>
<mi>P</mi>
<mrow>
<mn>2</mn>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<msub>
<mi>k</mi>
<mi>x</mi>
</msub>
</mrow>
<mi>f</mi>
</mfrac>
<mo>,</mo>
<mfrac>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<msub>
<mi>k</mi>
<mi>y</mi>
</msub>
</mrow>
<mi>f</mi>
</mfrac>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
5. based on the hot-tempered sound removing method of the two-dimentional optimal defocus of Entropic method and genetic algorithm as described in claim 1, it is special
Levy and be, the detailed process of genetic algorithm is in the step 4：
(1) random generation initialization population；
(2) selection initial population scale is popsize, maximum evolutionary generation gmax；
(3) fitness function：Fitness function is used as using H (s, t)；
(4) encode：Encoded using eight gray level images, threshold parameter is set to 0≤s, and individual UVR exposure is 16 by t≤255
Binary code, high eight-bit represents s, and low eight represent t；High eight-bit is similarly decoded as s by decoding, and low eight are decoded as t；
(5) selection opertor：It is combined using elitism strategy and roulette method, according to elitism strategy by outstanding of in colony 10%
Body is copied directly to the next generation, and then remaining individual is selected with roulette method；
(6) crossover operator：The crossover probability p of setting search early stagec=0.85, the crossover probability in search later stage is pc=0.65；
(7) mutation operator：Select parabolic type operator pm, it is defined as follows：
<mrow>
<msub>
<mi>p</mi>
<mi>m</mi>
</msub>
<mo>=</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>m</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>-</mo>
<mfrac>
<mrow>
<mn>4</mn>
<mo>*</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>m</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>m</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<msubsup>
<mi>g</mi>
<mi>max</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<mi>g</mi>
<mo>-</mo>
<mfrac>
<msub>
<mi>g</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
<mn>2</mn>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
Wherein, pmmax=20pmmin, pmmin=pb、pbFor basic mutation probability, g ∈ (1, gmax)；
(8) termination algorithm：The termination algorithm when reaching maximum evolutionary generation, that is, obtain optimal threshold.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108089425A (en) * | 2018-01-16 | 2018-05-29 | 电子科技大学 | A kind of method of the elimination optical scanner holography defocus noise based on deep learning |
| CN112258525A (en) * | 2020-10-30 | 2021-01-22 | 西安费斯达自动化工程有限公司 | Image abundance statistics and population recognition algorithm based on bird high frame frequency sequence |
| CN114418888A (en) * | 2022-01-19 | 2022-04-29 | 西安交通大学 | Ghost imaging method, system and storage medium based on genetic algorithm |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102805613A (en) * | 2012-08-13 | 2012-12-05 | 电子科技大学 | High-resolution optical scanning holographic slice imaging method based on two-time scanning |
| CN102920438A (en) * | 2012-10-30 | 2013-02-13 | 电子科技大学 | High-resolution optical scanning holographic slice imaging method based on variable pupils |
| CN104159094A (en) * | 2014-07-09 | 2014-11-19 | 四川大学 | Method for improving optical scanning holographic tomography effect |
| CN105184295A (en) * | 2015-07-27 | 2015-12-23 | 电子科技大学 | Holographic scanning space distance extracting method based on wavelet transform and connected domain |
| CN105204311A (en) * | 2015-07-06 | 2015-12-30 | 电子科技大学 | Gaussian apodization based optical scanning holographic edge detection method |
| CN106157278A (en) * | 2015-03-23 | 2016-11-23 | 南通录万电子有限公司 | Threshold image segmentation method based on improved adaptive GA-IAGA |
| CN106228169A (en) * | 2016-08-02 | 2016-12-14 | 电子科技大学 | The distance extracting method in holoscan space based on discrete cosine transform |
-
2017
- 2017-05-15 CN CN201710338528.3A patent/CN107240074A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102805613A (en) * | 2012-08-13 | 2012-12-05 | 电子科技大学 | High-resolution optical scanning holographic slice imaging method based on two-time scanning |
| CN102920438A (en) * | 2012-10-30 | 2013-02-13 | 电子科技大学 | High-resolution optical scanning holographic slice imaging method based on variable pupils |
| CN104159094A (en) * | 2014-07-09 | 2014-11-19 | 四川大学 | Method for improving optical scanning holographic tomography effect |
| CN106157278A (en) * | 2015-03-23 | 2016-11-23 | 南通录万电子有限公司 | Threshold image segmentation method based on improved adaptive GA-IAGA |
| CN105204311A (en) * | 2015-07-06 | 2015-12-30 | 电子科技大学 | Gaussian apodization based optical scanning holographic edge detection method |
| CN105184295A (en) * | 2015-07-27 | 2015-12-23 | 电子科技大学 | Holographic scanning space distance extracting method based on wavelet transform and connected domain |
| CN106228169A (en) * | 2016-08-02 | 2016-12-14 | 电子科技大学 | The distance extracting method in holoscan space based on discrete cosine transform |
Non-Patent Citations (2)
| Title |
|---|
| HAIYAN OU 等: "Defocus noise suppression with combined frame difference and connected component methods in optical scanning holography", 《OPTICS LETTERS》 * |
| 李宏言 等: "基于二维最大熵原理和改进遗传算法的图像阈值分割", 《计算机与现代化》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108089425A (en) * | 2018-01-16 | 2018-05-29 | 电子科技大学 | A kind of method of the elimination optical scanner holography defocus noise based on deep learning |
| CN112258525A (en) * | 2020-10-30 | 2021-01-22 | 西安费斯达自动化工程有限公司 | Image abundance statistics and population recognition algorithm based on bird high frame frequency sequence |
| CN112258525B (en) * | 2020-10-30 | 2023-12-19 | 西安费斯达自动化工程有限公司 | Image abundance statistics and population identification algorithm based on bird high-frame frequency sequence |
| CN114418888A (en) * | 2022-01-19 | 2022-04-29 | 西安交通大学 | Ghost imaging method, system and storage medium based on genetic algorithm |
| CN114418888B (en) * | 2022-01-19 | 2024-02-02 | 西安交通大学 | Ghost imaging method, system and storage medium based on genetic algorithm |
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