CN106991659A - A kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance change - Google Patents
A kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance change Download PDFInfo
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Abstract
The present invention is a kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change：Astronomical target image or spatial target images not in the same time after adaptive optics correction are gathered, the linear equation of solving system point spread function is set up；Set up the convex Optimized model of system point spread function solution；System point spread function is solved using the classical convex convex optimization method of Optimization Solution Algorithm for Solving；The estimate of target to be observed is solved according to the system point spread function solved, so as to recover image.The method of the present invention takes full advantage of the dynamic characteristic of turbulent flow, and timeliness to IMAQ, the real-time to system are not required, and image recovery process belongs to linear solution, computation amount.Without carrying out constantly alternately solving to observed object and system point spread function.Image recovery method stability is strong, in the absence of divergence problem；Algorithm is succinct and directly perceived.
Description
Technical field
The invention belongs to optical image security field, it is related to a kind of optical imagery that can adapt to atmospheric turbulance change extensive
Compound method, the particularly astronomical target or spatial target images processing method after adaptive optics system image correction.
Background technology
After the real-time aberration correction of adaptive optics system, compensate already for causing image blurring most of low order picture
Difference.But generally, limited by system cost, finite bandwidth and detection noise etc., adaptive optics method is to air
Not exclusively, the high-frequency information of target is still suppressed and decayed for the compensation of turbulent flow.The loss of high-frequency information then causes target thin
Save feature unintelligible, then the requirement that space is accurately positioned with target identification hardly results in satisfaction.
After-treatment is carried out to the adaptive optical image after above-mentioned partial correction, will not compensated by adaptive optics system
Wavefront residual error be corrected, to obtain more preferable image quality.It is extensive that stigmatic image technology has been successfully applied to astronomical point target
It is multiple, such as Knox-Thompson cross spectrums or bispectrum treatment technology.But reference target is needed in this method implementation process, and is needed
Handle thousands of width short exposed images.When for extending target, reference information is generally difficult to obtain, and has used its limitation
Property；Lucky imaging technique can be used in expansion target, but the precision that image recovers is to a certain extent dependent on acquisition " good fortune
The probability of fortune picture ", it is adaptable to image-forming condition when turbulent flow is smaller.According to system point spread function, whether oneself knows, can be by convolution class
Method is divided into deconvolution (known), blind deconvolution (unknown) and Myopic uncoilings (fraction is understood but unreliable).Deconvolution Method
Wave front detector is generally used, the requirement to hardware is higher.In actual conditions, system point spread function is often difficult to really
It is fixed.Therefore, in the case where neither knowing that dreamboat does not know dot system point spread function again, it is not necessary to which any priori is known
Know, the blind deconvolution algorithm low to system requirements is used widely, but its constringency performance also needs to improve, in low photon water
Flat or in the case that noise is larger, algorithm is often not sufficiently stable, very sensitive to noise.
Above-mentioned several image post-processing methods require that the corresponding imaging system of several short exposed images integrates point and expanded mostly
Dissipate function (including atmospheric transfer function and optical system transfer function) identical, i.e., within the time that turbulent flow is freezed, complete several
The collection and processing of short exposed images, thus it is high to the requirement of real-time of algorithm.Such as speckle imaging, lucky imaging, extension target
Imaging, but these methods all exist computationally intensive, the problems such as poor real, are only applicable to the occasion not required real-time.
The content of the invention
The technical problems to be solved by the invention are to adapt to atmospheric turbulance dynamic there is provided one kind in view of the shortcomings of the prior art
The multi-frame self-adaption optical image restoration methods of change.
The technical problems to be solved by the invention are realized by following technical scheme.The present invention is a kind of adaptation
The multi-frame self-adaption optical image restoration methods of atmospheric turbulance dynamic change, are characterized in, this method includes：
(1) multiple system point spread functions under collection turbulent-flow conditions after adaptive optics correction not in the same time, i.e. system
Point target imaging, multiple images are fourier transformed and are transformed into frequency domain, system point spread function after different time corrections is analyzed
Proximity and heterogeneite between number；Use the condition of the related Silvester Sylvester matrixes of different point spread functions
Number provides reference come the degree of closeness between judging for regular terms selection；
(2) the astronomical target image or spatial target images after the correction of collection adaptive optics not in the same time, set up and solve
The linear equation of system point spread function, linear solution is converted into by image recovery problem；When the size of image is larger, matrix
Dimension is higher, is solved using the method based on Fast Fourier Transform (FFT) FFT；
(3) based on the heterogeneite degree and the hangover characteristic of point spread function between imaging noise, point spread function, addition
Regular terms is to obtain stable solution；Using least square as canonical bound term, due to point spread function after adaptive optics correction
Several spectrums generally has sparsity structure characteristic, uses l1Norm regularization model is handled between point spread function to a certain degree
Proximity and point spread function hangover；Set up the convex Optimized model of system point spread function solution；
(4) using the classical convex convex optimization method of Optimization Solution Algorithm for Solving, so as to solve system point spread function；
(5) objective function, adds regular terms, and deconvolution solves target to be observed；Pressed down very well using having to noise
The full variation regular terms of making, solves the estimate for obtaining target to be observed, so as to recover image.
A kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change of the present invention, it enters
The preferred technical scheme of one step is：Described convex optimized algorithm is using interior point method, projection subgradient algorithm or low-rank method.
A kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change of the present invention, it enters
The preferred technical scheme of one step is：Gather astronomical target image or extraterrestrial target figure not in the same time after adaptive optics correction
Picture, sets up the linear equation of solving system point spread function, such as formula (3)：
Image recovery problem is converted into linear solution；
Wherein subscript i and j represent that the i-th frame is imaged I respectivelyi(x, y) and jth frame are imaged IjThe sequence number of (x, y)；BiAnd BjPoint
It is not by being imaged Ii(x, y) and IjThe Teoplitz toeplitz matrixes that (x, y) is constituted, the component of this toeplitz matrix is
Teoplitz block block toeplitz matrixes, T represents transposition；Hi(x, y) and Hj(x, y) is respectively i-th corresponding with j two field pictures
System point spread function.
A kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change of the present invention, it enters
The preferred technical scheme of one step is：The linear equation of solving system point spread function is set up, using least square (4) as just
Then bound term：
Wherein * is product calculation, | | | |2For l2Norm operator, l2Norm is also known as not this Frobenius of Luo Beini crows
Norm operator.
A kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change of the present invention, it enters
The preferred technical scheme of one step is：Use liNorm regularization model is a certain degree of close between point spread function to handle
The hangover of property and point spread function；Set up the convex Optimized model of system point spread function solution：Such as following formula：
During formula (5) is convex optimization problem, formula | | | |1For l1Norm operator, fft () is Fourier transform, s.t.
It is subject to abbreviation, implication is " constrained in ", constraintsBy the energy of point spread function
Be constrained to 1, to avoid the appearance of null solution.
A kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change of the present invention, it enters
The preferred technical scheme of one step is：Using the full variation regular terms to noise with fine inhibitory action, solution obtains to be observed
The estimate of target, so as to recover image；Objective function is as follows：
WhereinFor regular terms, O (x, y) is observed object, solves the estimate that above formula obtains target to be observed,
So as to recover image.
The principle of the present invention is as follows：
It is right when the transmission function of different passages meets relatively prime property in multichannel it was found from multichannel blind recognition knowwhy
Multiple fuzzy signals can be attributed to a relatively easy blind identification problem by simply handling, just, directly
Connect and estimate the point spread function of system, deconvolution computing is then carried out again and obtains high-resolution echo signal.It is astronomical
In target or extraterrestrial target imaging, due to the dynamic of atmospheric turbulance, the corresponding imaging system of image not gathered in the same time is comprehensive
Chalaza spread function is different, and different systems integrates point spread function and can be regarded as different passages, just meets multichannel blind
Recognize signal recovery method requirement, this point exactly with existing astronomic graph as or spatial target images love your processing method
Fundamental difference.During noiseless, by taking two width figures as an example, observed object O (x, y), system point spread function H (x, y) and imaging I (x,
Y) following relation is met
WhereinFor convolution algorithm, subscript i and j represent that the i-th frame is imaged I respectivelyi(x, y) and jth frame are imaged Ij(x's, y)
Sequence number, Hi(x, y) and Hj(x, y) is respectively the i-th system point spread function corresponding with j two field pictures.Then
Equation does not contain original object information in (2).If multiple sampled datas of equation (2), one can be write out
On Hi(x, y) and HjThe linear equation of (x, y).The equation left side will be moved on on the right of equation (2), convolution algorithm is melted into matrix
The form of multiplication, can obtain a linear equation：
Wherein BiAnd BjIt is by being imaged I respectivelyi(x, y) and IjTeoplitz (toeplitz) matrix that (x, y) is constituted, this
The component of individual toeplitz matrix is Teoplitz block (block toeplitz) matrix, and T represents transposition.Can by mathematical theory
Know, matrix [B during noiselessi-Bj] the corresponding singular vector of zero singular value be exactly Hi(x, y) and HjThe solution of (x, y), therefore can
To realize Hi(x, y) and HjThe complete recovery of (x, y).More generally, in the presence of noise, minimum singular value is corresponding unusual
Value vector is then the solution of the two.
Compared with prior art, the advantage of the invention is that：
1st, require that the corresponding system point spread function of multiple blurred pictures of collection is consistent relative to existing method, need into
Picture system is completed in turbulent flow freeze-off time constant for the collection of image, and method of the invention then takes full advantage of turbulent flow
Dynamic characteristic, the timeliness to IMAQ is not required.
2nd, relative to existing method is to the requirement of real-time height of system and image recovery process is computationally intensive, poor real
For problem, the present invention is not required the real-time of system, and image recovery process belongs to linear solution, and amount of calculation subtracts significantly
It is few.
3rd, the inventive method is constantly alternately solved without being carried out to observed object and system point spread function.
4th, the image recovery method stability of the inventive method is strong, in the absence of divergence problem；Algorithm is succinct and directly perceived.
Brief description of the drawings
The flow chart of Fig. 1 the inventive method；
The instance graph of Fig. 2-6 the inventive method image restoration results；Wherein：Fig. 2 and Fig. 3 is fuzzy graph not in the same time
Picture, corresponds to the I in formula 1 respectivelyi(x, y) and Ii(x, y), directly being gathered by imaging system to fall, and Fig. 4 and Fig. 5 are to use this hair
The system point spread function figure that bright method is recovered, is solved to the convex optimization method set up and obtains, and the H in formula 1 is corresponded to respectivelyi
(x, y) and Hj(x, y)；Fig. 6 is the target imaging figure O (x, y) recovered using the inventive method, based on convex Optimization Solution
Point spread function, is obtained using deconvolution method.
Embodiment
Referring to the drawings, the concrete technical scheme of the present invention is further described, in order to which those skilled in the art enters
Understand the present invention to one step.
Embodiment 1, reference picture 1, a kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change,
This method includes：
(1) multiple system point spread functions under collection turbulent-flow conditions after adaptive optics correction not in the same time, i.e. system
Point target imaging, multiple images are fourier transformed and are transformed into frequency domain, system point spread function after different time corrections is analyzed
Proximity and heterogeneite between number；Judge that using the conditional number of the related Sylvester matrixes of different point spread functions
Degree of closeness between this, reference is provided for regular terms selection；
(2) the astronomical target image or spatial target images after the correction of collection adaptive optics not in the same time, set up and solve
The linear equation of system point spread function, linear solution is converted into by image recovery problem；When the size of image is larger, matrix
Dimension is higher, is solved using the method based on Fast Fourier Transform (FFT) FFT；
(3) based on the heterogeneite degree and the hangover characteristic of point spread function between imaging noise, point spread function, addition
Regular terms is to obtain stable solution；Using least square as canonical bound term, due to point spread function after adaptive optics correction
Several spectrums generally has sparsity structure characteristic,
Use l1Norm regularization model handles a certain degree of proximity and the point spread function between point spread function
Several hangovers；Set up the convex Optimized model of system point spread function solution；
(4) using the classical convex convex optimization method of Optimization Solution Algorithm for Solving, so as to solve system point spread function；
(5) objective function, adds regular terms, and deconvolution solves target to be observed；Pressed down very well using having to noise
The full variation regular terms of making, solves the estimate for obtaining target to be observed, so as to recover image.
Embodiment 2, a kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change, by following step
It is rapid to carry out：
1st, the point target imaging of multiple point spread functions under collection turbulent-flow conditions after adaptive optics correction, i.e. system,
Multiple images are fourier transformed and are transformed into frequency domain, the proximity between system point spread function after different time corrections is analyzed
And heterogeneite.Using the conditional number of the related Sylvester matrixes of different point spread functions come between judging close to journey
Degree, reference is provided for regular terms selection.
2nd, the astronomical target image or spatial target images after the correction of collection adaptive optics not in the same time, set up and solve system
The linear equation of system point spread function, such as formula (3).
Image recovery problem is converted into linear solution.When the size of image is larger, matrix dimension is higher, can use base
Solved in the method for Fast Fourier Transform (FFT) (FFT).
3rd, in view of the heterogeneite degree and the hangover characteristic of point spread function between imaging noise, point spread function, add
Plus regular terms is to obtain stable solution.The present invention is used as canonical bound term using least square (4) first.
The spectrum of point spread function generally has sparsity structure characteristic after being corrected due to adaptive optics, uses l1Norm canonical
Change model to handle a certain degree of proximity between point spread function and the hangover of point spread function.Set up system point expansion
Dissipate the convex Optimized model that function is solved, such as following formula.
Formula (5) is the appearance that bound term in convex optimization problem, formula avoids null solution.
4th, using the classical above-mentioned convex optimization method of convex Optimization Solution Algorithm for Solving, so as to solve system point spread function.
Classical convex optimized algorithm can be interior point method (interior point methods), projection subgradient algorithm (projected
Sub-gradient method), low-rank method (low-rank parameterization) etc..
5th, using full variation (total variation) regular terms to noise with fine inhibitory action, solution is obtained
The estimate of target to be observed, so as to recover image.Objective function is as follows：
The estimate for obtaining target to be observed is solved, so as to recover image.
Fig. 2-6 is the instance graph that the inventive method recovers image.Fig. 2 and Fig. 3 is the fault image not gathered in the same time, right
Answer different system point spread functions；Fig. 4 and Fig. 5 are the system point spread function solved；Fig. 6 is to be expanded using the point solved
Dissipate function and carry out the target imaging figure that deconvolution computing is obtained.
Claims (6)
1. a kind of multi-frame self-adaption optical image restoration methods for adapting to atmospheric turbulance dynamic change, it is characterised in that this method
Including：
(1) point of the multiple system point spread functions, i.e. system under collection turbulent-flow conditions after adaptive optics correction not in the same time
Target imaging, is fourier transformed multiple images and is transformed into frequency domain, analyze after different time corrections system point spread function it
Between proximity and heterogeneite；Conditional number using the related Silvester Sylvester matrixes of different point spread functions is come
Degree of closeness between judgement, reference is provided for regular terms selection；
(2) the astronomical target image or spatial target images after the correction of collection adaptive optics not in the same time, set up solving system
The linear equation of point spread function, linear solution is converted into by image recovery problem；When the size of image is larger, matrix dimension
It is higher, solved using the method based on Fast Fourier Transform (FFT) FFT；
(3) based on the heterogeneite degree and the hangover characteristic of point spread function between imaging noise, point spread function, canonical is added
To obtain stable solution；Using least square as canonical bound term, point spread function after being corrected due to adaptive optics
Spectrum generally has sparsity structure characteristic, uses l1Norm regularization model handles a certain degree of phase between point spread function
The hangover of nearly property and point spread function；Set up the convex Optimized model of system point spread function solution；
(4) using the classical convex convex optimization method of Optimization Solution Algorithm for Solving, so as to solve system point spread function；
(5) objective function, adds regular terms, and deconvolution solves target to be observed；Suppress to make very well using having noise
Full variation regular terms, solves the estimate for obtaining target to be observed, so as to recover image.
2. a kind of multi-frame self-adaption optical image recovery side for adapting to atmospheric turbulance dynamic change according to claim 1
Method, it is characterised in that：Described convex optimized algorithm is using interior point method, projection subgradient algorithm or low-rank method.
3. a kind of multi-frame self-adaption optical image recovery side for adapting to atmospheric turbulance dynamic change according to claim 1
Method, it is characterised in that：Astronomical target image or spatial target images not in the same time after adaptive optics correction are gathered, foundation is asked
Solve the linear equation of system point spread function, such as formula (3)：
Image recovery problem is converted into linear solution；
Wherein subscript i and j represent that the i-th frame is imaged I respectivelyi(x, y) and jth frame are imaged IiThe sequence number of (x, y)；BiAnd BjBe respectively by
It is imaged Ii(x, y) and IjThe Teoplitz toeplitz matrixes that (x, y) is constituted, the component of this toeplitz matrix is Top's profit
Hereby block block toeplitz matrixes, T represents transposition；Hi(x, y) and Hi(x, y) is respectively the i-th system corresponding with j two field pictures
Point spread function.
4. a kind of multi-frame self-adaption optical image recovery side for adapting to atmospheric turbulance dynamic change according to claim 1
Method, it is characterised in that：The linear equation of solving system point spread function is set up, is constrained using least square (4) as canonical
：
Wherein * is product calculation, | | | |2For l2Norm operator, l2Norm is also known as this black Frobenius norm of not Luo Beini
Operator.
5. a kind of multi-frame self-adaption optical image recovery side for adapting to atmospheric turbulance dynamic change according to claim 1
Method, it is characterised in that：Use l1Norm regularization model handles a certain degree of proximity and point between point spread function
The hangover of spread function；Set up the convex Optimized model of system point spread function solution：Such as following formula：
During formula (5) is convex optimization problem, formula | | | |1For l1Norm operator, fft () is Fourier transform, and s.t. is
Subject to abbreviation, implication is " constrained in ", constraintsBy the energy of point spread function and
1 is constrained to, to avoid the appearance of null solution.
6. a kind of multi-frame self-adaption optical image recovery side for adapting to atmospheric turbulance dynamic change according to claim 1
Method, it is characterised in that：Using the full variation regular terms to noise with fine inhibitory action, solution obtains estimating for target to be observed
Evaluation, so as to recover image；Objective function is as follows：
WhereinFor regular terms, O (x, y) is observed object, solves the estimate that above formula obtains target to be observed, so that
Recover image.
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| CN110020994A (en) * | 2019-02-22 | 2019-07-16 | 华中光电技术研究所（中国船舶重工集团有限公司第七一七研究所） | A kind of atmospheric turbulence correction method of infrared target image |
| CN112345090A (en) * | 2019-08-08 | 2021-02-09 | 中国科学院长春光学精密机械与物理研究所 | Method and device for measuring infrared radiation of point target and readable storage medium |
| CN117078538A (en) * | 2023-07-19 | 2023-11-17 | 华中科技大学 | Correction method of remote atmospheric turbulence image based on pixel motion statistics |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020994A (en) * | 2019-02-22 | 2019-07-16 | 华中光电技术研究所（中国船舶重工集团有限公司第七一七研究所） | A kind of atmospheric turbulence correction method of infrared target image |
| CN110020994B (en) * | 2019-02-22 | 2022-11-22 | 华中光电技术研究所（中国船舶重工集团有限公司第七一七研究所） | Atmospheric turbulence correction method for infrared target image |
| CN112345090A (en) * | 2019-08-08 | 2021-02-09 | 中国科学院长春光学精密机械与物理研究所 | Method and device for measuring infrared radiation of point target and readable storage medium |
| CN117078538A (en) * | 2023-07-19 | 2023-11-17 | 华中科技大学 | Correction method of remote atmospheric turbulence image based on pixel motion statistics |
| CN117078538B (en) * | 2023-07-19 | 2024-02-13 | 华中科技大学 | Correction method of remote atmospheric turbulence image based on pixel motion statistics |
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