CN109242803A - A kind of restored method and device applied to microwave imagery - Google Patents
A kind of restored method and device applied to microwave imagery Download PDFInfo
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Abstract
The application provides a kind of restored method and device applied to microwave imagery, belongs to field of image processing, which comprises obtains former microwave imagery；The former microwave imagery is pre-processed, the first microwave imagery is obtained；Using multi-faceted degree variance gradient operator, the weight matrix of the first microwave imagery is determined；The number of iterations is determined using structural similarity evaluation algorithms；Based on the weight matrix and the number of iterations, image restoration is carried out using weighted space restoration algorithm to first microwave imagery, obtains restoring microwave imagery.The disclosure is applied in microwave imaging because defocusing, moving, blurred picture caused by environmental factor etc., can be improved the signal-to-noise ratio of microwave imagery after being imaged, can restore microwave imagery to a certain extent, improve visualization, meanwhile image detail abundant is kept, obtain the restored image of high quality.
Description
Technical field
The present invention relates to field of image processing more particularly to a kind of restored methods and device applied to microwave imagery.
Background technique
Image restoration is an important branch in image procossing, and the modern times also promote the quality requirement of imaging therewith,
And in imaging process, because of scattering, focusing is inaccurate, microwave imagery mould caused by the reasons such as noise in signals transmission
Paste phenomenon, it would be highly desirable to solve.Microwave imagery is easy to appear that contrast is low, the relatively low situation of resolution ratio, to obtain higher resolution
Microwave imagery, image restoration process are essential.
Lagendijk et al. proposition is restricted adaptive restoration algorithm (also known as weighted space restoration algorithm), it is intended that
In order to overcome ringing ripples, it is subject to recovery and smoothly local Adaptive Control.However, clear function hour drops, problem performance
It is unobvious, increase with clear function size is dropped, problem will become very serious, and auto-adaptive controling ability is deteriorated, and seamed edge nearby goes out
Existing irregular ripple.
Summary of the invention
The present invention provides a kind of restored method and device applied to microwave imagery, to solve image seamed edge in image restoration
Nearby there is the problem of irregular ripple.
In order to solve the above technical problems, the present invention provides a kind of restored method applied to microwave imagery, the method packet
It includes:
Obtain former microwave imagery；
The former microwave imagery is pre-processed, the first microwave imagery is obtained；
Using multi-faceted degree variance gradient operator, the weight matrix of the first microwave imagery is determined；
The number of iterations is determined using structural similarity evaluation algorithms；
Based on the weight matrix and the number of iterations, first microwave imagery is carried out using weighted space restoration algorithm
Image restoration obtains restoring microwave imagery.
Optionally, it is described to former microwave imagery carry out pretreatment include:
Wiener filtering is carried out to the former microwave imagery, obtains the first microwave imagery.
Optionally, described to utilize multi-faceted degree variance gradient operator, determine that the weight matrix of the first microwave imagery includes:
Image spreading is carried out to first microwave imagery, obtains the second microwave imagery；
Convolution is carried out using multi-faceted degree variance gradient operator and second microwave imagery, obtains multiple third microwaves
Image；
The multiple third microwave imagery is averaged, the weight matrix of first microwave imagery is obtained.
Optionally, described to include: to first microwave imagery progress image spreading
Image spreading is carried out to the first microwave imagery using Nuo Yiman boundary condition, obtains the second microwave imagery.
Optionally, it is based on the weight matrix and the number of iterations, first microwave imagery is restored using weighted space
Algorithm carries out image restoration, and obtaining restored image includes:
Utilize the weight matrix in the weight matrix replacement weighted space restoration algorithm of first microwave imagery；
The number of iterations in weighted space restoration algorithm is determined using the number of iterations；
Recovery is weighted to the second microwave imagery using the weighted space restoration algorithm；
The middle section of microwave imagery after restoring to weighting intercepts, and truncated picture is having a size of former microwave imagery
Size；
It obtains and restores microwave imagery.
On the other hand, the present invention provides a kind of restoring means applied to microwave imagery, and described device includes:
Obtain module: for obtaining former microwave imagery；
Preprocessing module: for pre-processing to the former microwave imagery, the first microwave imagery is obtained；
Weight matrix determining module: for utilizing multi-faceted degree variance gradient operator, the power of the first microwave imagery is determined
Value matrix；
The number of iterations determining module: for determining the number of iterations using structural similarity evaluation algorithms；
Image restoration module: being based on the weight matrix and the number of iterations, empty using weighting to first microwave imagery
Between restoration algorithm carry out image restoration, obtain restore microwave imagery.
Optionally, the preprocessing module includes pretreatment unit；
The pretreatment unit is used to carry out Wiener filtering to former microwave imagery, obtains the first microwave imagery.
Optionally, the weight matrix computing module includes: that image expansion unit, convolution unit and weight matrix calculate list
Member；
Described image expanding element obtains the second microwave imagery for being extended to first microwave imagery；
The convolution unit is used to carry out convolution using multi-faceted degree variance gradient operator and second microwave imagery,
Obtain multiple third microwave imageries；
It is micro- to obtain described first for being averaged to the multiple third microwave imagery for the weight matrix computing unit
The weight matrix of wave image.
Optionally, described image expansion module includes image expansion unit；
Described image expanding element is used to carry out image spreading to the first microwave imagery using Nuo Yiman boundary condition, obtains
Second microwave imagery.
Optionally, described image restoration module includes:
Replacement unit, for the power in the weight matrix replacement weighted space restoration algorithm using first microwave imagery
Value matrix；
The number of iterations determination unit determines the number of iterations in weighted space restoration algorithm using the number of iterations；
Restoration unit is weighted recovery to the second microwave imagery using the weighted space restoration algorithm；
The middle section of interception unit, the microwave imagery after restoring to weighting intercepts, and truncated picture is having a size of original
The size of microwave imagery；
Obtaining unit: it obtains and restores microwave imagery.
In the embodiment of the present invention, it can prevent from increasing with the clear function size of drop using multi-faceted degree variance gradient operator
Add, the auto-adaptive controling ability of weighted space restoration algorithm is deteriorated, and seamed edge nearby irregular ripple occurs, can prevent boundary
Be truncated generate artifacts, and alleviate because point function i.e. convolution kernel it is excessive caused by artifacts, improve image noise
Than, and realize adaptively selected suitable the number of iterations, achieve the purpose that microwave imagery restores.
The present invention is applied in microwave imaging because defocusing, moving, blurred picture caused by environmental factor etc., can be improved into
As the signal-to-noise ratio of rear microwave imagery, microwave imagery can be restored to a certain extent, improve visualization, meanwhile, it keeps abundant
Image detail obtains the restored image of high quality.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below by institute in the description to the embodiment of the present invention
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is a kind of step flow chart applied to microwave imagery restored method of the embodiment of the present invention one；
Fig. 2 is a kind of specific steps flow chart applied to microwave imagery restored method of the embodiment of the present invention two；
Fig. 3 is the exemplary former microwave imagery of the embodiment of the present invention two；
Fig. 4 is the exemplary recovery microwave imagery after image restoration of the embodiment of the present invention two；
Fig. 5 is a kind of structural block diagram applied to microwave imagery restoring means of apparatus of the present invention embodiment three；
Fig. 6 is a kind of specific block diagram applied to microwave imagery restoring means of apparatus of the present invention embodiment three.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hair
Embodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
[embodiment of the method one]
Referring to Fig.1, the step flow chart that one of embodiment of the present invention is applied to microwave imagery restored method is shown,
Specific steps include:
Step 101: obtaining former microwave imagery；
In the present embodiment, the microwave imagery is the microwave energy that object emission is received with microwave radiometer and the figure that is formed
Picture.Image-forming microwave radiometer can receive the image that the microwave radiation that atural object launch wavelength is 1~3 centimetre can be formed.The original is micro-
Wave image is the microwave imagery obtained by radar.The approach for obtaining microwave imagery can be through terminal device, network downloading
Etc. modes.
Step 102: the former microwave imagery being pre-processed, the first microwave imagery is obtained
In the present embodiment, microwave imagery is during generation and transmission usually due to interference and influence by various noises
Reduce picture quality, this has an adverse effect to the processing of subsequent image and image vision effect, therefore, in order to inhibit to make an uproar
Sound, improving image quality, the present embodiment pre-processes the former microwave imagery of acquisition, described to be located in advance to former microwave imagery
Reason includes denoising to microwave imagery, and image denoising includes using mean filter, adaptive wiener filter, median filtering
Device is filtered, to remove the noise of image.
Step 103, using multi-faceted degree variance gradient operator, determine the weight matrix of the first microwave imagery；
In the present embodiment, an original degree variance gradient operator (sobel operator) only has both direction template, a detection
Horizontal edge, another detection vertical edge.And the weight that the present embodiment is determined using a multi-faceted degree variance gradient operator
Matrix improves the fine degree of weight matrix, carries out image restoration using the weight matrix, the edge of image can be made to believe
It ceases more complete.
In the present embodiment, the Sobel operator is one of most important operator in pixel image edge detection, in engineering
The science technology information fields such as habit, Digital Media, computer vision play very important effect.Technically, it is one discrete
First difference operator, for calculate brightness of image function First-order Gradient approximation.This is used in any point of image
Operator, it will generate the corresponding gradient vector of point or its law vector.
Step 104 determines the number of iterations using structural similarity evaluation algorithms；
In the present embodiment, weighted space restoration algorithm is iterated recovery using conjugation iterative algorithm, but in iterative process
In, the number of iterations is preset fixed value, and the recovery obtained after restoring is iterated using preset the number of iterations
Image can cause the effect of restored image undesirable because of the improper of the number of iterations selection.In this regard, the present embodiment utilizes structure
Similarity evaluation algorithm, limits the number of iterations adaptively.After the present embodiment is by the former microwave imagery of calculating and iteration n times
The structural similarity of microwave imagery obtains adaptive the number of iterations according to preset threshold value.
Step 105 is based on the weight matrix and the number of iterations, is restored to first microwave imagery using weighted space
Algorithm carries out image restoration, obtains restoring microwave imagery.
In the present embodiment, the weight matrix for the first microwave imagery determined using multi-faceted degree variance gradient operator and
The weight matrix and iteration time in the number of iterations replacement weighted space restoration algorithm determined using structural similarity evaluation algorithms
Number, reinforces smooth, decrease recovery in the flat region of former microwave imagery, reinforces restoring in former microwave imagery seamed edge region, weaken
Smoothly.
In the embodiment of the present invention, the artifacts that described image restored method can prevent boundary truncation from generating, and delay
Solved because point function i.e. convolution kernel it is excessive caused by artifacts, improve signal noise ratio (snr) of image, and realize it is adaptively selected suitable
The number of iterations, achieve the purpose that microwave imagery restore.
[embodiment of the method two]
Referring to Fig. 2, the specific steps stream that one of embodiment of the present invention is applied to microwave imagery restored method is shown
Cheng Tu, specific steps include:
Step 201: obtaining former microwave imagery.
In a particular application, former microwave imagery is M row N column, and the size of point function is 10*10, utilizes error analysis parameter
Estimation, obtaining Gaussian parameter is 0.18.
Step 202: Wiener filtering being carried out to former microwave imagery, obtains the first microwave imagery.
In the embodiment of the present invention, the pretreatment of microwave imagery can also be, other filtering are carried out to image, comprising: intermediate value
Other filtering modes such as filtering, mean filter, gaussian filtering.
In concrete application, Wiener filtering is selected to carry out denoising to former microwave imagery, first after obtaining noise remove
Microwave imagery.
Step 203: image spreading being carried out to the first microwave imagery using Nuo Yiman boundary condition, obtains the second microwave figure
How described more picture carries out convolution using multi-faceted degree variance operator and the second microwave imagery, obtains multiple third microwave imageries,
A third microwave imagery is averaged, and obtains the weight matrix of the first microwave imagery.
In embodiments of the present invention, using Nuo Yiman boundary condition, the first boundary microwave imagery y is extended, up and down
5 row of border extension, 5 column of right boundary extension.The second microwave imagery is obtained, the second microwave imagery size is M+10 row, and N+10 is arranged,
M is the line number of the first microwave imagery, and N is the columns of the first microwave imagery.
Using Miller Regularization, it is specified that two limitations:
| | y-Bx | |≤ε and | | Cx | |≤E
Wherein, x indicates the image after restoring, and ε is constant, the noise energy depending on the first microwave imagery y；E is constant,
It is decided by the restored image high-frequency energy allowed.Weighting matrix is added, obtains:
||y-Bx||R=[(y-Bx)TR(y-Bx)]1/2≤ε
||Cx||S=[(Cx)TS(Cx)]1/2≤E
It is released by two above formula:
JW(x)=[(y-Bx)TR(y-Bx)]+α[(Cx)TS(Cx)]≤2ε2,
Minimize JW(x) it obtains: (BTRB+αCTSC) x=BTRy
JWIt (x) is functional, α is constant, the ratio depending on ε and E；Matrix B and C are aperiodic convolution nuclear matrix, by it
Be denoted as FhAnd Fc, then solving equation becomes
Formula is rewritten after conjugate gradient iterative algorithm is added are as follows:
P is size any number vector identical with x, is the auxiliary vector in calculating process；Q is auxiliary in calculating process
Variable is helped, can use FFT realization.
Wherein, R and S is 2 diagonal matrix, contains the weight coefficient r having an effect to each pixelijAnd sij.Formulate rij
Value is emphasized to keep the seamed edge of image in recuperation, can control the non-stationary of noise variation, can be also used for enhancing and lose
Lose the recovery of data.Specified sijIt can be with Partial controll flatness, to eliminate ring shape artifacts.It follows that in image
Flat place need to use big sijWith small rij, and at image seamed edge, it is exactly the opposite.
For weighted matrix R and S, the calculation method of formula is abandoned, uses improved multi-faceted degree variance gradient operator
Operator is filtered the second microwave imagery image y, by original level and the edge detection operator of vertical both direction, extension
To 8, i.e., 0 degree, 45 degree, 90 degree, 135 degree, 180 degree, 225 degree, 270 degree, 315 degree.Operator Model is respectively as follows:
Convolution algorithm is carried out with the second microwave imagery respectively using multi-faceted operator template, obtains matrix: K0, K45, K90,
K135, K180, K225, K270, K315.Take mean value:
K=(K0+K45+K90+K135+K180+K225+K270+K315)/8
Then obtain weight matrix
Step 204: determining the number of iterations using structural similarity evaluation algorithms.
In embodiments of the present invention, selection is configured to the number of iterations, using SSIM (structural similarity) evaluation algorithms,
Keep the number of iterations adaptive.For microwave imagery of the invention, the knot of the Restoration images after calculating former microwave imagery and iteration n times
Structure similarity, taking threshold value is 0.9997 and the number of iterations n > 0.So that it is determined that the number of iterations.
Image structure similarity concept (SSIM) as long as think can calculate the variation of object construction information, it will be able to be felt
Know image fault value.The concept is introduced into the clarity evaluation for calculating full reference picture, it is believed that the clarity of image can be with
It is indicated using the structural similarity between target image and reference picture, and the structural similarity between image includes following three portions
The comparison divided:
Brightness is compared:
Contrast compares:
Structural information compares:
Finally three combination of function are got up, obtain SSIM exponential function:
SSIM (x, y)=[l (x, y)]α[C(x,y)]β[S(x,y)]γ
SSIM is from three brightness (mean value), contrast (variance) and structure level movement images distortions, and wherein structure accounts for master
Want influence factor.As α=β=γ=1 and C3=C2When/2, obtain:
μx、μyRespectively indicate the mean value of image X and Y, σx、σyRespectively indicate the standard deviation of image X and Y, σx 2、σy 2Table respectively
Variance of the diagram as X and Y.σxyRepresentative image X and Y covariance.C1, C2And C3It is in order to avoid denominator maintains for 0 for constant
Stablize.α, the weight occupied of the beta, gamma by brightness of image, contrast and structural information between image in structural similarity.
Step 205 utilizes the weight square in the weight matrix replacement weighted space restoration algorithm of first microwave imagery
Battle array；Utilize the number of iterations in the number of iterations replacement weighted space restoration algorithm of first microwave imagery；Added using described
Weight space restoration algorithm is weighted recovery to the second microwave imagery；The middle section of microwave imagery after restoring to weighting carries out
Interception, size of the truncated picture having a size of former microwave imagery；It obtains and restores microwave imagery.
In embodiments of the present invention, using in weight matrix R and S the replacement weighted space restoration algorithm of the first microwave imagery
Weight matrix, utilize structural similarity functionCome
The number of iterations in adaptive determination weighted space restoration algorithm, and using the weighted space restoration algorithm to border extension
The second microwave imagery afterwards is weighted recovery, and the microwave imagery after restoring to weighting intercepts intermediary matrix M*N (original size),
Obtain the restored image having a size of M*N.
With reference to Fig. 3, for former microwave imagery used in the embodiment of the present invention；
With reference to Fig. 4, for the present invention, by a kind of microwave imagery restoration algorithm described in embodiment, treated restores microwave
Image.
In the embodiment of the present invention, the artifacts that described image restored method can prevent boundary truncation from generating, and delay
Solved because point function i.e. convolution kernel it is excessive caused by artifacts, improve signal noise ratio (snr) of image, and realize it is adaptively selected suitable
The number of iterations, achieve the purpose that microwave imagery restore.
It should be noted that for the aforementioned method embodiment, for simple description, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the sequence of acts described, because according to
According to the present invention, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know that,
The embodiments described in the specification are all preferred embodiments, and related movement is not necessarily essential to the invention.
[Installation practice three]
With reference to Fig. 5, the structural frames that one of embodiment of the present invention is applied to microwave imagery restoring means 300 are shown
Figure.Include:
Module 310 is obtained, for obtaining former microwave imagery；
Preprocessing module 320 obtains the first microwave imagery for handling the former microwave imagery.
Preferably, referring to Fig. 6, on the basis of Fig. 5, the microwave imagery restoring means 300 can also include:
The preprocessing module 320 includes:
Pretreatment unit 3201 obtains the first microwave imagery for carrying out Wiener filtering to former microwave imagery.
Weight matrix determining module 330 determines the first microwave imagery for utilizing multi-faceted degree variance gradient operator
Weight matrix.
Preferably, with reference to Fig. 6, on the basis of Fig. 5, the microwave imagery restoring means 300 can also include:
The weight matrix determining module 330 includes:
Image expansion unit 3301 obtains the second microwave imagery for being extended to first microwave imagery；
Convolution unit 3302, for being rolled up using multi-faceted degree variance gradient operator and second microwave imagery
Product, obtains multiple third microwave imageries；
Weight matrix computing unit 3303 obtains described first for being averaged to the multiple third microwave imagery
The weight matrix of microwave imagery.
Preferably, described image expansion unit 3301 includes:
Image spreading subelement is obtained for carrying out image spreading to the first microwave imagery using Nuo Yiman boundary condition
Second microwave imagery.
The number of iterations determining module 340, for determining the number of iterations using structural similarity evaluation algorithms；
Image restoration module 350 is based on the weight matrix and the number of iterations, utilizes weighting to first microwave imagery
Palinspastic reconstruction algorithm carries out image restoration, obtains restoring microwave imagery.
Preferably, with reference to Fig. 6, on the basis of Fig. 5, the microwave imagery restoring means 300 can also include:
Described image restoration module 350 includes:
Replacement unit 3501, in the weight matrix replacement weighted space restoration algorithm using first microwave imagery
Weight matrix；
The number of iterations determination unit 3502 determines the iteration in weighted space restoration algorithm time using the number of iterations
Number；
Restoration unit 3503 is weighted recovery to the second microwave imagery using the weighted space restoration algorithm；
Interception unit 3504, the middle section for the microwave imagery after restoring to weighting intercept, truncated picture
Size having a size of former microwave imagery；
Obtaining unit 3505 restores microwave imagery for obtaining.
In the embodiment of the present invention, the artifacts that described image restoring means can prevent boundary truncation from generating, and delay
Solved because point function i.e. convolution kernel it is excessive caused by artifacts, improve signal noise ratio (snr) of image, and realize it is adaptively selected suitable
The number of iterations, achieve the purpose that microwave imagery restore.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, all of these belong to the protection of the present invention.
Claims (10)
1. a kind of restored method applied to microwave imagery, which is characterized in that the described method includes:
Obtain former microwave imagery；
The former microwave imagery is pre-processed, the first microwave imagery is obtained；
Using multi-faceted degree variance gradient operator, the weight matrix of the first microwave imagery is determined；
The number of iterations of the first microwave imagery is determined using structural similarity evaluation algorithms；
Based on the weight matrix and the number of iterations, image is carried out using weighted space restoration algorithm to first microwave imagery
It restores, obtains restoring microwave imagery.
2. the method according to claim 1, wherein it is described to former microwave imagery carry out pretreatment include:
Wiener filtering is carried out to the former microwave imagery, obtains the first microwave imagery.
3. being determined the method according to claim 1, wherein described utilize multi-faceted degree variance gradient operator
The weight matrix of first microwave imagery includes:
Image spreading is carried out to first microwave imagery, obtains the second microwave imagery；
Convolution is carried out using multi-faceted degree variance gradient operator and second microwave imagery, obtains multiple third microwave figures
Picture；
The multiple third microwave imagery is averaged, the weight matrix of first microwave imagery is obtained.
4. according to the method described in claim 3, it is characterized in that, described carry out image spreading packet to first microwave imagery
It includes:
Image spreading is carried out to the first microwave imagery using Nuo Yiman boundary condition, obtains the second microwave imagery.
5. according to the method described in claim 4, it is characterized in that, the weight matrix and the number of iterations are based on, to described the
One microwave imagery carries out image restoration using weighted space restoration algorithm, and obtaining restored image includes:
Utilize the weight matrix in the weight matrix replacement weighted space restoration algorithm of first microwave imagery；
Utilize the number of iterations in the number of iterations replacement weighted space restoration algorithm of first microwave imagery；
Recovery is weighted to the second microwave imagery using the weighted space restoration algorithm；
The middle section of microwave imagery after restoring to weighting intercepts, ruler of the truncated picture having a size of former microwave imagery
It is very little；
It obtains and restores microwave imagery.
6. a kind of restoring means applied to microwave imagery, which is characterized in that described device includes:
Obtain module: for obtaining former microwave imagery；
Preprocessing module: for pre-processing to the former microwave imagery, the first microwave imagery is obtained；
Weight matrix determining module: for utilizing multi-faceted degree variance gradient operator, the weight square of the first microwave imagery is determined
Battle array；
The number of iterations determining module: for determining the number of iterations using structural similarity evaluation algorithms；
Image restoration module: being based on the weight matrix and the number of iterations, multiple using weighted space to first microwave imagery
Former algorithm carries out image restoration, obtains restoring microwave imagery.
7. device according to claim 6, which is characterized in that the preprocessing module includes pretreatment unit；
The pretreatment unit is used to carry out Wiener filtering to former microwave imagery, obtains the first microwave imagery.
8. device according to claim 6, which is characterized in that the weight matrix determining module includes: image spreading list
Member, convolution unit and weight matrix computing unit；
Described image expanding element obtains the second microwave imagery for being extended to first microwave imagery；
The convolution unit is used to carry out convolution using multi-faceted degree variance gradient operator and second microwave imagery, obtains
Multiple third microwave imageries；
The weight matrix computing unit obtains the first microwave figure for being averaged to the multiple third microwave imagery
The weight matrix of picture.
9. device according to claim 7, which is characterized in that described image expanding element includes image spreading subelement；
Described image extends subelement and is used to carry out image spreading, acquisition the to the first microwave imagery using Nuo Yiman boundary condition
Two microwave imageries.
10. device according to claim 9, which is characterized in that described image restoration module includes:
Replacement unit, for the weight square in the weight matrix replacement weighted space restoration algorithm using first microwave imagery
Battle array；
The number of iterations determination unit determines the number of iterations in weighted space restoration algorithm using the number of iterations；
Restoration unit is weighted recovery to the second microwave imagery using the weighted space restoration algorithm；
Interception unit, the middle section for the microwave imagery after restoring to weighting intercept, and truncated picture is having a size of original
The size of microwave imagery；
Obtaining unit restores microwave imagery for obtaining.
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