CN105759610B - Adaptive optics system intelligent control method based on Energy distribution judgement - Google Patents
Adaptive optics system intelligent control method based on Energy distribution judgement Download PDFInfo
- Publication number
- CN105759610B CN105759610B CN201610110889.8A CN201610110889A CN105759610B CN 105759610 B CN105759610 B CN 105759610B CN 201610110889 A CN201610110889 A CN 201610110889A CN 105759610 B CN105759610 B CN 105759610B
- Authority
- CN
- China
- Prior art keywords
- aperture
- hartmann
- sub
- array lens
- adaptive optics
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
Abstract
The invention discloses a kind of adaptive optics system intelligent control method based on Energy distribution judgement, the basic ideas of this method are：First, Hartmann's array lens respective weights matrix B is measured by experiment；Then, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, obtains 0/1 matrix A；Finally, according to C=Sum (A.B)>T carries out the judgement of Hartmann's array lens full aperture Energy distribution, according to court verdict to adaptive optics system send whether closed loop commands.It this method propose a kind of intelligent control method for controlling adaptive optics system automatic open-loop closed loop, compared with traditional adaptive optics system, the introducing of this method reduces the human input of system, has very great help for the automating of adaptive optics system, intelligent operation.
Description
Technical field
The present invention relates to the technical fields of adaptive optics intelligent control, and in particular to it is a kind of based on Energy distribution judgement
Adaptive optics system intelligent control method is mainly used in the case where being participated in without personnel, controls adaptive optics system
Carry out automatic open-loop and closed loop.
Background technology
Adaptive optics system has optical system automatic by the way that wavefront distortion is detected, controls and corrected in real time
Adapt to change of external conditions, the ability for remaining good working state.Adaptive optics system is by Wavefront sensor, wavefront control
Device and wave-front corrector three parts composition processed, at present, most of adaptive optics system generally use Shack-Hartmann wavefront pass
Sensor carries out Wavefront detecting.Hartmann wave front sensor real-time detection goes out wavefront distortion, this signal is handled through ripple wavefront controller
After produce control signal and be added in wavefront controller, generate with the wavefront distortion that is detected equal in magnitude, symbol is on the contrary
Wavefront correction amount makes light wave wavefront since the distortion for being subject to dynamic disturbance and generating obtains real-time compensation, so as to improve into image quality
Amount.
Heavy foundation astronomical telescope is using adaptive optics system to be increased to its Imaging Resolution.On the one hand, prestige is worked as
When remote mirror system observes dark weak signal target, it will the situation that Hartmann sensor lacks light compared with multiple sub-apertures occur, need people at this time
For to adaptive optics system can normal closed loop judge, and manual control its closed loop.On the other hand, in adaptive optics
In system operation, when detect target due to orbit altitude changes and, it is necessary to artificially judge system shape when there is energy variation
State simultaneously its open loop of manual control.Due to adaptive optics system can not automatic running, generally require experimenter and observe in real time
Image come control the system when closed loop, when open loop.Based on such premise, sentenced this paper presents one kind based on Energy distribution
Adaptive optics intelligent control technology certainly.
The content of the invention
The technical problem to be solved by the present invention is to：Adaptive optics system open loop closed loop is overcome to need manual intervention not
Foot, it is proposed that a kind of adaptive optics system intelligent control method based on Energy distribution judgement, in advance experiment measure Hartmann
The corresponding weight matrix of array lens, then to observed result, institute carries out into hot spot in each sub-aperture of Hartmann's array lens
Detection obtains energy binaryzation matrix, passes through the ratio that the dot product result of energy binaryzation matrix and weight matrix is summed with threshold value
Relatively come decide whether to adaptive optics system send closed loop commands.
The technical solution adopted by the present invention to solve the technical problems is：Adaptive optics system based on Energy distribution judgement
Unite intelligent control method this method the step of be：First, Hartmann's array lens respective weights matrix B is measured by experiment；So
Afterwards, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, obtains 0/1 matrix A；
Finally, according to C=Sum (A.B)>T carries out the judgement of Hartmann's array lens full aperture Energy distribution, wherein, A is hot spot binaryzation
Matrix, B measure Hartmann's array lens weight matrix for experiment, and T is the threshold value that experiment measures, according to court verdict to adaptive
Optical system send whether closed loop commands.
Wherein, Hartmann's array lens respective weights matrix B is measured by experiment, wherein each element reacts corresponding
Sub-aperture to the influence degree of observed result later stage closed loop effect, experiment understands that sub-aperture lacks influence of the light to closed loop effect
Degree is related with the distance of sub-aperture to array lens center, and the value of weight matrix will successively be passed by center to surrounding here
Subtract.
Wherein, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, the
One time threshold process carries out background inhibition：If it is f (x, y) to input sub-aperture image, size is m × n, is found out in f (x, y)
Then the value max of the gray value maximum and value min of gray value minimum calculates gray scale in f (x, y) and is located at k1max(0<k1<1) and
k2min(k2>1) gray average of the pixel between is as threshold value t1, threshold value t is subtracted with each pixel value in original image f (x, y)1
Obtain image f1(x, y), wherein k1,k2Value chosen according to the concrete condition of experimental data.
Wherein, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, looks for
Go out f1The point respective coordinates (x, y) of gray value maximum in (x, y), (x, y) together with its 8 connected regions is extracted, is denoted as
S, i.e.,
Wherein, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, the
Secondary threshold process:Global threshold is carried out in image S to handle to obtain threshold value t2With image S1；Threshold value t2It can be acquired after determining
Wherein, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, uses
Less than the signal-to-noise ratio that mean variance method calculates image f (x, y), SNR=10lg ((Imax-B)/σn), in formula：ImaxFor in f (x, y)
Gray scale maximum, approximation represent target gray, and B is background gray scale, and computational methods are being averaged less than image average pixel, σnFor
Noise criteria is poor, and computational methods are the standard deviation of the pixel less than image average.
Wherein, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, sentences
It is disconnected to whether there is hot spot：As image S1Middle grey scale pixel value summation is more than threshold value T1And signal-to-noise ratio is more than threshold value T2When (T1And T2According to reality
Border situation is measured by experiment), then judge with the presence of hot spot in the sub-aperture, otherwise do not have.
Wherein, to observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, time
Go through each sub-aperture in a width complete array lens picture, repeat 3 to 7 steps, obtain final hot spot whether there is it is complete
Discrimination matrix A.
Wherein, according to C=Sum (A.B)>T carries out the judgement of Hartmann's array lens full aperture Energy distribution, the gained by before
The matrix A arrived carries out dot product with B, i.e. then C=A.B carries out all elements in C summation and is denoted as sum, differentiated as follows：
As sum >=T, closed loop commands are sent to Adaptable System, Adaptable System is allowed to carry out closed loop, then repeatedly above step continues
It is observed judgement；, work as sum<During T, adaptive optics system closed loop conditions are unsatisfactory for, without sending closed loop commands to system,
It still repeats more than link and continues observation judgement, here, threshold value T is determined by experiment as the case may be.
The principle of the present invention is：
It is a kind of based on Energy distribution judgement adaptive optics system intelligent control method, be divided into following three big steps into
Row：
Hartmann's array lens respective weights matrix is measured in first step, experiment；
Second step, sub-aperture hot spot binaryzation；
3rd step, the judgement of array lens full aperture Energy distribution；
The calculating of Hartmann's array lens respective weights matrix includes：One and Hartmann are measured by experiment in advance
The corresponding weight matrix B of array lens sub-aperture arrangement, wherein each element react corresponding sub-aperture and observation are tied
The influence degree of fruit closed loop effect.Experiment understands that it is saturating to array with sub-aperture to the influence degree of closed loop effect that sub-aperture lacks light
The distance at mirror center is related, and the value of weight matrix will successively be successively decreased by center to surrounding here；
The sub-aperture hot spot binaryzation includes：
1) first time threshold process carries out background inhibition：If it is f (x, y) to input sub-aperture image, size is m × n；It looks for
Go out the value max of the gray value maximum in f (x, y) and the value min of gray value minimum, then calculate gray scale in f (x, y) and be located at
k1max(0<k1<And k 1)2min(k2>1) gray average of the pixel in, as threshold value t1, with each picture in original image f (x, y)
Plain value subtracts threshold value and obtains image f1(x, y), wherein k1,k2Value chosen according to the fluctuation of experimental data；
2) f is found out1The point respective coordinates (x, y) of gray value maximum in (x, y), (x, y) is carried together with its 8 connected regions
It takes out, is denoted as S, i.e.,
3) second of threshold process:Global threshold is carried out in image S to handle to obtain threshold value t2With image S1；
Threshold value t2It can be acquired after determining
4) with the calculating that signal-to-noise ratio is carried out less than mean variance method, i.e. SNR=10lg ((Imax-B)/σn)
In formula：ImaxFor gray scale maximum in f (x, y), approximation represents target gray, and B is background gray scale, and computational methods are
Less than being averaged for image average pixel, σnPoor for noise criteria, computational methods are the standard deviation of the pixel less than image average.
5) hot spot is determined whether：As image S1Middle pixel summation is more than threshold value T1And signal-to-noise ratio is more than threshold value T2When (T1And T2
Measured according to actual conditions by experiment), then judge in the sub-aperture with the presence of hot spot；It is then corresponding in binaryzation discrimination matrix A
Position is 1, is otherwise set to 0；
6) each sub-aperture in a width complete array lens picture is traveled through, repeats 1) to 5) step, obtains final hot spot
The discrimination matrix A that whether there is；
The array lens full aperture Energy distribution judgement includes：Obtained matrix A it will carry out dot product, i.e. C with B before
Then=A.B carries out all elements in C summation and is denoted as sum, differentiated as follows：As sum >=T, passed to Adaptable System
Closed loop commands are passed, Adaptable System is allowed to carry out closed loop, then repeatedly above step continues observation judgement；, work as sum<During T,
Adaptive optics system closed loop conditions are unsatisfactory for, more than link is repeated and continues observation judgement.Here, threshold value T is according to specific
Situation is determined by experiment.
The beneficial effects of the invention are as follows：
1) in sub-aperture hot spot binaryzation link, twice threshold processing, first time picture middle gray have been carried out to picture
Pixel average as threshold value, certain inhibition has been carried out to picture background, rational region has then been selected to carry out the second subthreshold
Value processing, the signal-to-noise ratio of size and picture in its entirety in conjunction with light spot energy further detected the presence of hot spot, effectively
Improve the accuracy of spot detection.
2) experiment measures the corresponding weight matrix of Hartmann's array lens to the present invention in advance, then to observed result in Hart
It is detected to obtain energy binaryzation matrix into hot spot in the graceful each sub-aperture of array lens, by energy binaryzation matrix
Comparison with the summation of the dot product result of weight matrix and threshold value decides whether to send closed loop commands to adaptive optics system.
Adaptive optics system open loop closed loop is automatically controlled in the case of manual intervention is not required, substantially increases its intelligent and work
Efficiency.
Description of the drawings
Fig. 1 is that the present invention is based on the flow charts of the adaptive optics intelligent control technology of Energy distribution judgement；
Fig. 2 is self-adaptive optical telescope composition figure；
Fig. 3 a are the Hartmann's array lens sub-aperture arrangement illustratons of model used in this actual experiment；
Fig. 3 b are the full sub-aperture figures actually used in this actual experiment；
Fig. 4 a are a sub-aperture artworks in Fig. 3 b；
Fig. 4 b are the figures after a sub-aperture is detected with this experimental method；
Fig. 5 is the figure of the matrix A for the generation of picture shown in Fig. 3 b in this experiment；
Fig. 6 is the topology example figure of the weight matrix B measured in this experiment for the experiment of Fig. 3 a type arrays lens；
Fig. 7 a are the width picture that telescope collects；
Fig. 7 b are the picture after adaptive optics system corrects of picture shown in Fig. 7 a.
Specific embodiment
Below in conjunction with the accompanying drawings and the present invention is discussed in detail in specific embodiment.
The present invention realizes flow and is carried out as shown in Figure 1, being divided into following three links：Hartmann's array lens pair are measured in experiment
Answer the detection of hot spot and the judgement of array lens full aperture Energy distribution in weight matrix, sub-aperture.The present invention is based on such as Fig. 3 a institutes
Show that Hartmann's array lens of arrangement carry out, entire array lens size is 80 × 80, wherein each sub-aperture size is 5 × 5.
Each step is specific as follows：
First link：Hartmann's array lens respective weights matrix is measured in experiment
Hartmann's array lens are irradiated by the use of a branch of directional light as reference light source, by institute in each sub-aperture into hot spot energy
Amount with full aperture energy and ratio be used as its weighted value；Occurrence is obtained by repeated measurement in experiment in weight matrix；
Second link：Sub-aperture hot spot binaryzation
1) first time threshold process carries out background inhibition：If observation image is F (x, y), wherein first sub-aperture f is taken
(x, y), size are 5 × 5；
2) the value max of the gray value maximum in f (x, y) and the value min of gray value minimum are found out, calculates gray scale in f (x, y)
Positioned at k1max(0<k1<And k 1)2min(k2>1) gray average of the pixel in is subtracted with each pixel value in original image f (x, y)
Threshold value is gone to obtain image f1(x, y), in this experiment (threshold value value)；
3) f is found out1The point respective coordinates (x, y) of gray value maximum in (x, y), (x, y) is carried together with its 8 connected regions
It takes out, is denoted as S；
4) second of threshold process:Global threshold is carried out in image S to handle to obtain threshold value t2With image S1；
The calculating process of threshold value is as follows：
A. the gray average of S is calculated as initial threshold t2；
B. gray value in S is more than t2Be denoted as g1, less than t2Part be denoted as g2；
C. zoning g is distinguished1And g2Pixel average μ1And μ2；
D. new threshold value t is calculated2=(m1+m2)/2；
E. above step is repeated, it is known that t in successive iterations2Difference be less than a predefined value t3Until, t here3=
0.5；Threshold value t2It can be acquired after determining
5) with the calculating that signal-to-noise ratio is carried out less than mean variance method, i.e. SNR=10lg ((Imax-B)/σn)；
In formula：ImaxFor gray scale maximum in f (x, y), approximation represents target gray, and B is background gray scale, and computational methods are
Less than being averaged for image average pixel, σnPoor for noise criteria, computational methods are the standard deviation of the pixel less than image average；
6) hot spot is determined whether：As image S1When middle pixel summation is more than threshold value 700 and signal-to-noise ratio and is more than threshold value 7, then sentence
It is disconnected with the presence of having hot spot in the sub-aperture；As image S1Middle pixel summation is more than threshold value T1And signal-to-noise ratio is more than threshold value T2When (T1With
T2Measured according to actual conditions by experiment), then judge in the sub-aperture with the presence of hot spot；Then phase in binaryzation discrimination matrix A
It is 1 to answer position, is otherwise set to 0；
7) travel through each sub-aperture in F (x, y), repeat 1) to obtain to 6) step final hot spot whether there is it is complete
Discrimination matrix A；
3rd link：Array lens full aperture Energy distribution is adjudicated
Obtained matrix A dot product will be carried out with B before, i.e. then C=A.B carries out all elements in C summation and is denoted as
Sum is differentiated as follows：As sum >=T, closed loop commands are sent to Adaptable System, Adaptable System are allowed to carry out closed loop, so
Above step is repeated afterwards continues observation judgement；, work as sum<During T, be unsatisfactory for adaptive optics system closed loop conditions, repeat with
Upper link continues observation judgement.Here, threshold value T is determined by experiment as the case may be.
Claims (5)
1. the adaptive optics system intelligent control method based on Energy distribution judgement, it is characterised in that：The step of this method is：
First, Hartmann's array lens respective weights matrix B is measured by experiment；Then, to observed result in Hartmann's array lens
Institute carries out energy binaryzation into hot spot in each sub-aperture, obtains 0/1 matrix A；Finally, according to C=Sum (A.B)>T is breathed out
Special graceful array lens full aperture Energy distribution judgement, wherein, A is hot spot binaryzation matrix, and it is saturating that B for experiment measures Hartmann's array
Mirror weight matrix, T are the threshold value that measures of experiment, according to court verdict to adaptive optics system send whether closed loop commands；
To observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, at the first subthreshold
Reason carries out background inhibition：If it is f (x, y) to input sub-aperture image, size is m × n, and the gray value found out in f (x, y) is maximum
Value max and gray value minimum value min, then calculate gray scale in f (x, y) and be located at k1Max, 0<k1<1 and k2min,k2>1 it
Between pixel gray average as threshold value t1, threshold value t is subtracted with each pixel value in original image f (x, y)1Obtain image f1(x,
Y), wherein k1,k2Value chosen according to the concrete condition of experimental data；
To observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, finds out f1In (x, y)
(x, y) together with its 8 connected regions is extracted, is denoted as S, i.e., by the point respective coordinates (x, y) of gray value maximum
To observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, at the second subthreshold
Reason:Global threshold is carried out in image S to handle to obtain threshold value t2With image S1；Threshold value t2It can be acquired after determining
According to C=Sum (A.B)>T carries out the judgement of Hartmann's array lens full aperture Energy distribution, the obtained matrix A by before
Dot product is carried out with B, i.e. then C=AB carries out all elements in C summation and is denoted as sum, differentiated as follows：As sum >=T
When, closed loop commands are sent to Adaptable System, Adaptable System are allowed to carry out closed loop, then repeatedly above step continues to observe
Judgement；Work as sum<During T, be unsatisfactory for adaptive optics system closed loop conditions, without to system send closed loop commands, still repeat with
Upper link continues observation judgement, and here, threshold value T is determined by experiment as the case may be.
2. the adaptive optics system intelligent control method according to claim 1 based on Energy distribution judgement, feature
It is：Hartmann's array lens respective weights matrix B is measured by experiment, wherein each element reacts corresponding sub-aperture
To the influence degree of observed result later stage closed loop effect, experiment understands that sub-aperture lacks light to the influence degree of closed loop effect with son
The distance at aperture to array lens center is related, and the value of weight matrix will successively be successively decreased by center to surrounding here.
3. the adaptive optics system intelligent control method according to claim 1 based on Energy distribution judgement, feature
It is：To observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, with less than average
Variance method calculates the signal-to-noise ratio of image f (x, y), SNR=10lg ((Imax-B)/σn), in formula：ImaxIt is maximum for gray scale in f (x, y)
Value, approximation represent target gray, and B is background gray scale, and computational methods are being averaged less than image average pixel, σnFor noise criteria
Difference, computational methods are the standard deviation of the pixel less than image average.
4. the adaptive optics system intelligent control method according to claim 1 based on Energy distribution judgement, feature
It is：To observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, determines whether light
Spot：As image S1Middle grey scale pixel value summation is more than threshold value T1And signal-to-noise ratio is more than threshold value T2When, T1And T2Led to according to actual conditions
It crosses experiment to measure, then judges with the presence of hot spot in the sub-aperture, otherwise do not have.
5. the adaptive optics system intelligent control method according to claim 1 based on Energy distribution judgement, feature
It is：To observed result, institute carries out energy binaryzation into hot spot in each sub-aperture of Hartmann's array lens, and traversal one is complete
Each sub-aperture in whole array lens picture, until obtaining the complete discrimination matrix A that final hot spot whether there is.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610110889.8A CN105759610B (en) | 2016-02-29 | 2016-02-29 | Adaptive optics system intelligent control method based on Energy distribution judgement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610110889.8A CN105759610B (en) | 2016-02-29 | 2016-02-29 | Adaptive optics system intelligent control method based on Energy distribution judgement |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105759610A CN105759610A (en) | 2016-07-13 |
| CN105759610B true CN105759610B (en) | 2018-06-01 |
Family
ID=56331463
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610110889.8A Active CN105759610B (en) | 2016-02-29 | 2016-02-29 | Adaptive optics system intelligent control method based on Energy distribution judgement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105759610B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110441927B (en) * | 2019-07-25 | 2020-10-13 | 中国科学院光电技术研究所 | Automatic open/close loop decision method for adaptive optical system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288305A (en) * | 2011-07-18 | 2011-12-21 | 中国科学院光电技术研究所 | Wave-front sensor of self-adaptive optical system and detecting method thereof |
| CN103105235A (en) * | 2013-01-27 | 2013-05-15 | 中国科学院光电技术研究所 | Method for measuring tilt aberration of Hartmann wavefront sensor subaperture |
| CN103162846A (en) * | 2013-02-07 | 2013-06-19 | 中国科学院光电技术研究所 | Method of building coefficient transfer matrix between Zernike polynomial aberration model and Walsh function aberration model |
| CN103557948A (en) * | 2013-09-25 | 2014-02-05 | 南京理工大学 | Optical system wavefront measurement device and method based on circular carrier frequency phase demodulation method |
| CN105118035A (en) * | 2015-08-20 | 2015-12-02 | 南京信息工程大学 | Self-adaptive optical spot signal extraction method based on sparse representation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9678520B2 (en) * | 2013-03-15 | 2017-06-13 | Dominion Resources, Inc. | Electric power system control with planning of energy demand and energy efficiency using AMI-based data analysis |
-
2016
- 2016-02-29 CN CN201610110889.8A patent/CN105759610B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288305A (en) * | 2011-07-18 | 2011-12-21 | 中国科学院光电技术研究所 | Wave-front sensor of self-adaptive optical system and detecting method thereof |
| CN103105235A (en) * | 2013-01-27 | 2013-05-15 | 中国科学院光电技术研究所 | Method for measuring tilt aberration of Hartmann wavefront sensor subaperture |
| CN103162846A (en) * | 2013-02-07 | 2013-06-19 | 中国科学院光电技术研究所 | Method of building coefficient transfer matrix between Zernike polynomial aberration model and Walsh function aberration model |
| CN103557948A (en) * | 2013-09-25 | 2014-02-05 | 南京理工大学 | Optical system wavefront measurement device and method based on circular carrier frequency phase demodulation method |
| CN105118035A (en) * | 2015-08-20 | 2015-12-02 | 南京信息工程大学 | Self-adaptive optical spot signal extraction method based on sparse representation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105759610A (en) | 2016-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Carballo et al. | New approach for solar tracking systems based on computer vision, low cost hardware and deep learning | |
| CN109035204A (en) | A kind of weld seam target real-time detection method | |
| JP5918984B2 (en) | Information processing apparatus, information processing apparatus control method, and program | |
| CN101448085B (en) | Videography processing method and system supporting face detection | |
| Kribus et al. | Closed loop control of heliostats | |
| CN109522857A (en) | A kind of Population size estimation method based on production confrontation network model | |
| CN104537616A (en) | Correction method for fisheye image distortion | |
| CN101783888A (en) | Automatic exposure method based on analogous column diagram | |
| CN106204662B (en) | A kind of color of image constancy method under multiple light courcess environment | |
| CN111727412A (en) | Method, apparatus, system, and program for setting lighting condition, and storage medium | |
| CN109685716A (en) | A kind of image super-resolution rebuilding method of the generation confrontation network based on Gauss encoder feedback | |
| CN105759610B (en) | Adaptive optics system intelligent control method based on Energy distribution judgement | |
| CN106603933A (en) | Exposure method and apparatus | |
| CN107514745A (en) | A kind of method and system of intelligent air condition stereoscopic vision positioning | |
| CN116600104B (en) | Phase acquisition quality analysis method and system for IPC network camera | |
| CN106920241A (en) | A kind of steam water-level monitoring method, apparatus and system | |
| CN106846372A (en) | Human motion quality visual A+E system and method | |
| CN101261736A (en) | Collaborative detection method for multi-source image motive target | |
| CN111932633A (en) | Method for automatically adjusting camera based on face part detection technology | |
| CN110009696A (en) | It is demarcated based on ant colony algorithm Optimized BP Neural Network trinocular vision | |
| CN105118035A (en) | Self-adaptive optical spot signal extraction method based on sparse representation | |
| EP3788539A1 (en) | Predicting sun light irradiation intensity with neural network operations | |
| CN104065956B (en) | The detection of a kind of imageing sensor and caliberating device and method | |
| Hashim et al. | An intelligent classification model for rubber seed clones based on shape features through imaging techniques | |
| KR101502235B1 (en) | Method for deriving ground reaction force predicting model and ground reaction force predicting apparatus using the same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |