CN105869156A - Infrared small target detection method based on fuzzy distance - Google Patents
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Abstract
The invention aims at effectively detecting an infrared small target in a complex background, discloses an infrared small target detection method based on fuzzy distance, and relates to the technical field of digital image processing. The method firstly proposes a fuzzy distance concept for a characteristic that the appearance of a small target will cause a bigger change of local texture, and converts the change of the local texture into the measurement of the fuzzy distance. The method secondly proposes a multi-scale fuzzy distance and a multi-scale fuzzy distance map for a characteristic that the size of the small target will change along with the change of an imaging distance, thereby removing a large amount of background noise and noise interference. The method thirdly effectively inhibits the residual background and noise through iterative operation, and enhances the target. The method finally detects the target through an adaptive threshold value. The method is simple and efficient.
Description
Technical field
The present invention relates to digital image processing techniques field, a kind of infrared small target detection based on fuzzy distance
Method.
Background technology
Infrared target detection technology is used widely at many civil areas, such as medical science infrared imaging, remote sensing and gloomy
Woods detection, early warning detection etc..Detection performance quality directly determines EFFECTIVE RANGE and the complexity of equipment of infrared system
Degree.Remote infrared system is remote because of image-forming range, thus causes that target size is little, intensity is weak, and be easily submerged in very noisy and
In background clutter.In this case, effectively detect that the Small object of unknown position/speed/size/shape faces very disaster
Degree, thus this kind of technology receives and continues and universal concern.
Existing small target deteection technology can simply be divided into detection before follow the tracks of (Track before Detect, TBD) and
Detection (Detect before Track, DBT) two classes before following the tracks of.TBD method the most first searches for target all possible motion rail
Mark, and it is cumulative to complete target energy, thus obtain the posterior probability of every movement locus, finally utilize the real mesh of threshold decision
Mark movement locus, such as three-dimensional matched filtering, three-dimensional filtering.TBD method be prone to set up relatively more completely theoretical model and
Processing method, but calculate complexity, hardware realize trouble, apply in Practical Project less (C.Q.Gao, D.Y.Meng,
Y.Yang,Y.T.Wang,X.F.Zhou,A.G.Hauptmann,Infrared patch-image model for small
target detection in a single image,IEEE Transactions on Image Processing,22
(12):4996-5009,2013).DBT method typically first detects candidate target according to the gamma characteristic in short-term of single-frame images, then
Kinetic characteristic in short-term according to target rejects false target, thus obtains the real movement locus of target.Compared with TBD method,
DBT algorithm is simple, it is simple to program modularity realizes, thus play a significant role in real-time target detection field (H.Deng,
X.P.Sun,M.L.Liu,C.H.Ye,X.Zhou,Infrared small-target detection using
multiscale gray difference weighted image entropy,IEEE Transactions on
Aerospace and electronic Systems,52(1):60-72,2016).Be given according to SPIE
Small object defines, and the size of Small object is usually no more than the 0.12% of entire image size, thus the appearance of target is to view picture figure
As texture structure impact is less, but the impact of localized region texture structure is bigger.Based on this feature, some describe local grain and become
Change operator to be suggested, can effectively detect infrared small target, as multiple dimensioned intensity-weighted image entropy, sparse ring represent, the main one-tenth of probability
Analyze, local contrast estimates (C.L.Philip, H.Li, Y.T.Wei, T.Xia, and Y.Y.Tang, A local
contrast method for small infrared target detection,IEEE Transactions on
Geoscience and Remote Sensing,51(1):574-581,2014).The application patented method is under the jurisdiction of DBT side
Method.Compared with conventional DBT method, the application patented method proposes a kind of fuzzy distance concept, can effectively portray target internal,
Distance inside background, between target and background, thus the local grain caused because of the appearance of Small object change is converted into mould
Stick with paste the tolerance of distance, it is achieved background suppression, targets improvement, be conducive to improving the detection probability of target, reduce false-alarm probability.
Although infrared small target detection field is achieved with a lot of achievements, and existing a lot of TBD and DBT algorithms are in engineering
Application has obtained good realization.But for low signal-to-noise ratio infrared small target image under complex background, object detection system work
Cheng Yiran faces the biggest difficulty and complexity.Therefore, how to design that result is simple, the infrared small target detection of strong robustness
Algorithm is the key issue of target detection technique applied research.
Summary of the invention
The present invention be directed to the above-mentioned technical problem that existing infrared small target detection method exists, it is provided that a kind of based on mould
Stick with paste the infrared small target detection method of distance.
A kind of infrared small target detection method based on fuzzy distance, comprises the following steps:
Step 1, initialization relevant parameter:
Arranging maximum iteration time L, wherein L is positive integer;Initialize iterations index k=0;Maximum local window is set
Mouth size m × n, wherein m and n is the positive odd number more than 1;
Step 2, solve the fuzzy distance of each pixel of infrared image I, comprise the following steps:
Step 2.1, acquisition each pixel of Single Infrared Image Frame I (x, neighborhood space collection { Ω y)l| l=1,
2 ..., s}, wherein s=min{0.5 (m-1), 0.5 (n-1) }, ΩlSize be (2l+1) × (2l+1), pixel
(x, neighborhood space Ω y)lDefinition be Ωl=(p, q) | max (| p-x |, | q-y |)≤l}, (p q) is neighborhood space Ωl
Interior pixel;
Step 2.2, calculate each pixel (x, each neighborhood space Ω y)lThe gray average D of interior pixell(x, y):
Wherein, # ΩlRepresent neighborhood space ΩlThe number of interior pixel, (a b) represents neighborhood space Ω to IlInterior pixel
(a, b) gray value at place.
Step 2.3, calculate each pixel (x, y) corresponding to maximum neighborhood space ΩsEmpty with other each neighborhood
Between Ωi, i=1,2 ..., the fuzzy distance E between s-1i:
Wherein e is natural constant, DsRepresent maximum neighborhood space ΩsThe gray average of interior pixel, DiRepresent i-th neighborhood
Space ΩiThe gray average of interior pixel;
Step 3, solve multiple dimensioned fuzzy distance figure:
Traversal infrared image I in each pixel, obtain each pixel multiple dimensioned fuzzy distance E (x, y), so
According to the multiple dimensioned fuzzy distance E of each pixel, (x y) and by method for normalizing obtains the multiple dimensioned of infrared image I afterwards
Fuzzy distance figure E;
Step 4, iteration stopping criterion judge:
Iterations index k adds 1, it is judged that the relation between iterations index k and maximum iteration time L, if k < L,
The multiple dimensioned fuzzy distance figure E that step 3 is obtained, as new infrared image I, returns step 2;If k >=L, stop iteration,
The multiple dimensioned fuzzy distance figure E that step 3 is obtained, as final filter result, carries out step 5;
Step 5, solve adaptive threshold T:
To the final filter result obtained through step 4, the most multiple dimensioned fuzzy distance figure E, solve adaptive threshold T,
And by adaptive threshold T, multiple dimensioned fuzzy distance figure E is carried out binaryzation, detect infrared small target.
(x, multiple dimensioned fuzzy distance y) is expressed as step 3 mid-infrared each pixel of image I as above
E (x, y)=max{0, E1,E2,…,Es-1}。
In step 5 as above, the determination method of adaptive threshold T is
T=α Emax+β·mt
Wherein, α and β is positive constant, mtFor the gray average of multiple dimensioned fuzzy distance figure E, EmaxFor multiple dimensioned fuzzy away from
Gray scale maximum from figure E.
The present invention compared with prior art, has the advantage that
1. the appearance for Small object can cause local grain this feature of generation large change, and the present invention proposes one
Fuzzy distance concept, can effectively portray the distance inside target internal, background, between target and background, thus by local grain
Change is converted into the tolerance of fuzzy distance.
2. the size for Small object can occur this feature of respective change with the change of image-forming range, and the present invention proposes
A kind of multiple dimensioned fuzzy distance measure, thus it is effectively improved the detection probability of target, reduce false-alarm probability.
3. first the present invention builds the multiple dimensioned fuzzy distance figure of infrared image under complex background, can reject a large amount of background miscellaneous
Ripple and noise jamming;Secondly by interative computation, the background of effectively suppression residual and noise, strengthen target;Then utilize adaptive
Answer threshold value separation target, simply and efficiently detect target.
Accompanying drawing explanation
Fig. 1 is the FB(flow block) of the present invention.
Fig. 2 is the multiple dimensioned fuzzy distance figure after successive ignition, and wherein, A figure is the Small object figure under original sky background
As (white rectangle frame represents target region), B figure is the multiple dimensioned fuzzy distance figure after an iteration, and C figure is to change for twice
Multiple dimensioned fuzzy distance figure after Dai, D figure is the multiple dimensioned fuzzy distance figure after three iteration, and E figure is many after four iteration
Yardstick fuzzy distance figure, F figure is the testing result for using adaptive threshold.
Fig. 3 is the filter result schematic diagram of the single infrared small target image using the present embodiment method to obtain.
A, B, C and D: (white rectangle frame represents target to the single infrared small target image being followed successively by under original different background
Region), wherein, A figure is the Small object image under sky background, and B figure is the Small object image under clutter background, and C figure is
The Small object image under water of marine background, D figure is the Small object image under surface feature background;
E, F, G and H: be corresponding in turn to the filter result using the present embodiment method to obtain in A, B, C and D.
Fig. 4 is the filter result schematic diagram of two the infrared small target images using the present embodiment method to obtain.
A, B, C and D: (white rectangle frame represents target to be followed successively by under original different background two infrared small target images
Region), wherein, A figure is the Small object image under sky background, and B figure is the Small object image under sky background, and C figure is
Small object image under the water surface background of ocean, D figure is the Small object image under surface feature background;
E, F, G and H: be corresponding in turn to the filter result using the present embodiment method to obtain in A, B, C and D.
Fig. 5 is the filter result schematic diagram using existing method to obtain for A, B, C and the D in Fig. 3.
A1, B1, C1 and D1: be corresponding in turn in Fig. 3 A, Fig. 3 B, Fig. 3 C and Fig. 3 D based on local contrast (Local
Contrast measure, LCM) filter result of method;
A2, B2, C2 and D2: be corresponding in turn in Fig. 3 A, Fig. 3 B, Fig. 3 C and Fig. 3 D based on Largest Mean filter (Maxmean
Filter, MME) filter result of method;
A3, B3, C3 and D3: be corresponding in turn in Fig. 3 A, Fig. 3 B, Fig. 3 C and Fig. 3 D based on max-medium filter
The filter result of (Maxmedian filter, MED) method;
A4, B4, C4 and D4: be corresponding in turn in Fig. 3 A, Fig. 3 B, Fig. 3 C and Fig. 3 D based on top cap filter (Top-hat
Filter, THT) filter result of method.
Fig. 6 is the filter result schematic diagram using existing method to obtain for A, B, C and the D in Fig. 4.
A1, B1, C1 and D1: be corresponding in turn in Fig. 4 A, Fig. 4 B, Fig. 4 C and Fig. 4 D based on local contrast (Local
Contrast measure, LCM) filter result of method;
A2, B2, C2 and D2: be corresponding in turn in Fig. 4 A, Fig. 4 B, Fig. 4 C and Fig. 4 D based on Largest Mean filter (Maxmean
Filter, MME) filter result of method;
A3, B3, C3 and D3: be corresponding in turn in Fig. 4 A, Fig. 4 B, Fig. 4 C and Fig. 4 D based on max-medium filter
The filter result of (Maxmedian filter, MED) method;
A4, B4, C4 and D4: be corresponding in turn in Fig. 4 A, Fig. 4 B, Fig. 4 C and Fig. 4 D based on top cap filter (Top-hat
Filter, THT) filter result of method.
Detailed description of the invention
Below by embodiment, and combine accompanying drawing, technical scheme is described in further detail.
Embodiment:
Fig. 1 is the structural schematic block diagram of embodiment of the present invention, mainly includes that image inputs: the input infrared little mesh of single frames
Logo image;Fuzzy distance solves: solve the fuzzy distance between input picture current region and neighborhood collection;Multiple dimensioned fuzzy distance
Figure solves: solve the multiple dimensioned fuzzy distance figure of input picture, portrays that may be present because of caused by the change of image-forming range
Target size changes this characteristic;Iteration stopping judges: judge the relation between iterations index and maximum iteration time, weight
Multiple iteration, it is thus achieved that final filter result;Threshold value solves: solve the segmentation threshold of final filter result;Binaryzation: divided by threshold value
From target, it is thus achieved that target centroid position.
Particularly as follows:
Step 1, initialization relevant parameter:
Arranging maximum iteration time L, wherein L is positive integer, generally 2,3 or 4;Initialize iterations index k=0;
Arranging maximum local window size m × n, wherein m and n is the positive odd number more than 1, and the value of general m and n is disposed as 7,9 or
11。
Step 2, solves the fuzzy distance of each pixel of infrared image I:
Infrared small target image under complex background is typically made up of target, complex background and noise three part.By degree
Distance inside amount target internal, background, between target and background, thus by because of the local grain caused by the appearance of Small object
Change is converted into the tolerance of fuzzy distance, it is achieved background suppression and targets improvement.
The solution procedure of the fuzzy distance of each pixel of infrared image I is as follows:
(1) each pixel of Single Infrared Image Frame I (x, neighborhood space collection { Ω y) are obtainedl| l=1,2 ..., s}, its
Middle s=min{0.5 (m-1), 0.5 (n-1) }, ΩlSize be (2l+1) × (2l+1), (x, neighborhood y) is empty for pixel
Between ΩlDefinition be Ωl=(p, q) | max (| p-x |, | q-y |)≤l}, (p q) is neighborhood space ΩlInterior pixel.
(2) each pixel (x, each neighborhood space Ω y) are calculatedlThe gray average D of interior pixell(x, y):
Wherein, # ΩlRepresent neighborhood space ΩlThe number of interior pixel, (a b) represents neighborhood space Ω to IlInterior pixel
(a, b) gray value at place.
(3) calculate each pixel (x, y) corresponding to maximum neighborhood space ΩsWith other each neighborhood space Ωi,i
=1,2 ..., the fuzzy distance E between s-1i:
Wherein e is natural constant, DsRepresent maximum neighborhood space ΩsThe gray average of interior pixel, DiRepresent i-th neighborhood
Space ΩiThe gray average of interior pixel.
Step 3, solves multiple dimensioned fuzzy distance figure:
Although lack the prioris such as infrared small target size, size, but along with the change of image-forming range, the chi of target
The features such as very little, size can occur to change to a certain extent.Multiple dimensioned fuzzy distance is utilized to portray that may be present because of image-forming range
The target size caused by change change this character.
(x, multiple dimensioned fuzzy distance y) is expressed as each pixel of infrared image I
E (x, y)=max{0, E1,E2,...,Es-1} (3)
Wherein, Ei, i=1,2 ..., s-1 represents pixel (x, a series of fuzzy distances y).
Each pixel in traversal infrared image I, obtains the multiple dimensioned fuzzy distance of each pixel, then passes through
Method for normalizing obtains multiple dimensioned fuzzy distance figure E (as shown in the B of Fig. 2) of infrared image I.From the B of Fig. 2 it can be seen that
Homogenizing sky background and the cloud layer interior background of infrared image I are inhibited, and target is strengthened.
Step 4, iteration stopping criterion judges:
Iterations index k adds 1, it is judged that the relation between iterations index k and maximum iteration time L, if k < L, will
The multiple dimensioned fuzzy distance figure E that step 3 is obtained, as new infrared image I, returns step 2;If k >=L, stop iteration, will
The multiple dimensioned fuzzy distance figure that step 3 is obtained, as final filter result, carries out step 5.
The complex background border of infrared image has the thermal imaging feature similar to target, permissible by iteration is repeated several times
Eliminate residual background and effect of noise (as shown in Figure 2).The B of Fig. 2 represents the multiple dimensioned fuzzy distance figure after an iteration.From
It can be seen that the homogeneous background (inside homogenizing sky and homogenizing cloud layer) in the B of Fig. 2 is well suppressed in the B of Fig. 2, but
Remain more cloud layer edge.The multiple dimensioned fuzzy distance figure (as shown in the C of Fig. 2) of the B of Fig. 2 can remove overwhelming majority residual
Cloud layer edge, and the multiple dimensioned fuzzy distance figure (as shown in the D of Fig. 2) of the C of Fig. 2 can remove remaining cloud layer edge so that multiple
Miscellaneous cloud layer border is suppressed further, and target is further enhanced from.The multiple dimensioned fuzzy distance figure of the D of Fig. 2 is (such as figure
Shown in the E of 2) little with the D difference of Fig. 2, illustrate to use the most limited iterations to be obtained with comparatively ideal filtering knot
Really.
Step 5, solves adaptive threshold T:
The final filter result (the most multiple dimensioned fuzzy distance figure E) obtained through step 4 is solved adaptive threshold T,
And by adaptive threshold T, multiple dimensioned fuzzy distance figure E is carried out binaryzation, (binaryzation result is such as to detect infrared small target
Shown in the F of Fig. 2).The determination method of adaptive threshold T is
T=α Emax+β·mt (4)
Wherein, α and β is positive constant, mtFor the gray average of multiple dimensioned fuzzy distance figure E, EmaxFor multiple dimensioned fuzzy distance figure
The gray scale maximum of E.
Use the filter result of different infrared small target detection method as shown in Figure 5 and Figure 6.Comparison diagram 3, Fig. 4, Fig. 5 and
Fig. 6, the filtering performance that the present embodiment method obtains is best, wherein, based on local contrast (Local contrast
Measure, LCM) method comes from document C.L.Philip, H.Li, Y.T.Wei, T.Xia, and Y.Y.Tang, A local
contrast method for small infrared target detection,IEEE Transactions on
Geoscience and Remote Sensing, 51 (1): 574-581,2014, LCM methods first pass through local contrast measure
Dissimilarity between current region and neighborhood, then by threshold value separation target;(Maxmean is filtered based on Largest Mean
Filter, MME) or max-medium filter (Maxmedian filter, MED) method come from document S.Deshpande,
M.Er,and R.Venkateswarlu,Maxmean and Maxmedian filters for detection of
Small-targets, Proceeding of SPIE, 1999,3809:74-83, MME/MED method be first pass through Largest Mean/
Median filter wiping out background noise jamming, then determines threshold value according to the statistical property of image, separates target;Filter based on top cap
Ripple (Top-hat filter, THT) method comes from document X.Z.Bai and F.G.Zhou, Analysis of new top-
hat transformation and the application for infrared dim small target
Detection, Pattern Recognition, 2010,43 (6): 2145-2156, THT method first passes through top-cap operator suppression
Background and noise, then use threshold value to separate target from filtered image.LCM, MME, MED, THT are all under the jurisdiction of DBT method.
Use Background suppression factor (Background suppression factor, BSF) objective evaluation infrared small target
The filtering performance of detection method.The definition of BSF is:
BSF=σI/σO (5)
Wherein, σIRepresent the gray standard deviation of filtered image, σORepresent the gray standard deviation of filter wavefront image.Use
The BSF numerical value that LCM, MME, MED, THT and the present embodiment method are obtained is shown in Table 1.As it can be seen from table 1 the present embodiment method
Obtain the highest BSF value, illustrate that the present embodiment method can suppress complex background and the noise of infrared small target image effectively, with
The conclusion that Fig. 5 with Fig. 6 is obtained is consistent.
Table 1 uses the Background suppression factor (BSF) of different infrared small target detection method to compare
Image | LCM method | MME method | MED method | THT method | The present embodiment method |
Fig. 3 A | 0.8134 | 0.5638 | 0.5826 | 1.0157 | 5.8339 |
Fig. 3 B | 0.9348 | 0.7368 | 0.7396 | 1.8542 | 8.2408 |
Fig. 3 C | 0.5686 | 0.3288 | 0.3521 | 0.7993 | 6.0785 |
Fig. 3 D | 0.4272 | 0.3800 | 0.3843 | 1.6548 | 6.3409 |
Fig. 4 A | 0.4179 | 0.2640 | 0.2476 | 1.6413 | 1.7025 |
Fig. 4 B | 0.1698 | 0.1259 | 0.1232 | 0.3681 | 0.5156 |
Fig. 4 C | 0.2227 | 0.2171 | 0.2164 | 1.0775 | 4.7691 |
Fig. 4 D | 0.5685 | 0.5246 | 0.5227 | 1.6320 | 7.5393 |
Specific embodiment described herein is only to present invention spirit explanation for example.Technology neck belonging to the present invention
Described specific embodiment can be made various amendment or supplements or use similar mode to replace by the technical staff in territory
Generation, but without departing from the spirit of the present invention or surmount scope defined in appended claims.
Claims (3)
1. an infrared small target detection method based on fuzzy distance, it is characterised in that comprise the following steps:
Step 1, initialization relevant parameter:
Arranging maximum iteration time L, wherein L is positive integer;Initialize iterations index k=0;It is big that maximum local window is set
Little m × n, wherein m and n is the positive odd number more than 1;
Step 2, solve the fuzzy distance of each pixel of infrared image I, comprise the following steps:
Step 2.1, acquisition each pixel of Single Infrared Image Frame I (x, neighborhood space collection { Ω y)l| l=1,2 ..., s},
Wherein s=min{0.5 (m-1), 0.5 (n-1) }, ΩlSize be (2l+1) × (2l+1), pixel (x, neighborhood y)
Space ΩlDefinition be Ωl=(p, q) | max (| p-x |, | q-y |)≤l}, (p q) is neighborhood space ΩlInterior pixel;
Step 2.2, calculate each pixel (x, each neighborhood space Ω y)lThe gray average D of interior pixell(x, y):
Wherein, # ΩlRepresent neighborhood space ΩlThe number of interior pixel, (a b) represents neighborhood space Ω to IlInterior pixel (a, b)
The gray value at place.
Step 2.3, calculate each pixel (x, y) corresponding to maximum neighborhood space ΩsWith other each neighborhood space Ωi,
I=1,2 ..., the fuzzy distance E between s-1i:
Wherein e is natural constant, DsRepresent maximum neighborhood space ΩsThe gray average of interior pixel, DiRepresent i-th neighborhood space
ΩiThe gray average of interior pixel;
Step 3, solve multiple dimensioned fuzzy distance figure:
Traversal infrared image I in each pixel, obtain each pixel multiple dimensioned fuzzy distance E (x, y), then root
According to the multiple dimensioned fuzzy distance E of each pixel, (x y) and by method for normalizing obtains the multiple dimensioned fuzzy of infrared image I
Distance map E;
Step 4, iteration stopping criterion judge:
Iterations index k adds 1, it is judged that the relation between iterations index k and maximum iteration time L, if k < L, step 3
The multiple dimensioned fuzzy distance figure E obtained, as new infrared image I, returns step 2;If k >=L, stop iteration, step 3
The multiple dimensioned fuzzy distance figure E obtained, as final filter result, carries out step 5;
Step 5, solve adaptive threshold T:
To the final filter result obtained through step 4, the most multiple dimensioned fuzzy distance figure E, solve adaptive threshold T, and lead to
Cross adaptive threshold T and multiple dimensioned fuzzy distance figure E is carried out binaryzation, detect infrared small target.
A kind of infrared small target detection method based on fuzzy distance the most according to claim 1, it is characterised in that described
Step 3 mid-infrared each pixel of image I (x, multiple dimensioned fuzzy distance y) is expressed as E (x, y)=max{0, E1,
E2,...,Es-1}。
A kind of infrared small target detection method based on fuzzy distance the most according to claim 1, it is characterised in that described
Step 5 in the determination method of adaptive threshold T be
T=α Emax+β·mt
Wherein, α and β is positive constant, mtFor the gray average of multiple dimensioned fuzzy distance figure E, EmaxFor multiple dimensioned fuzzy distance figure E
Gray scale maximum.
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CN108198198A (en) * | 2017-12-22 | 2018-06-22 | 湖南源信光电科技股份有限公司 | Single frames infrared small target detection method based on wavelet transformation and Steerable filter |
CN109886980A (en) * | 2019-03-04 | 2019-06-14 | 电子科技大学 | A kind of infrared image cirrus detection method based on neighborhood intensity texture coding |
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CN107590496A (en) * | 2017-09-18 | 2018-01-16 | 南昌航空大学 | The association detection method of infrared small target under complex background |
CN108198198A (en) * | 2017-12-22 | 2018-06-22 | 湖南源信光电科技股份有限公司 | Single frames infrared small target detection method based on wavelet transformation and Steerable filter |
CN109886980A (en) * | 2019-03-04 | 2019-06-14 | 电子科技大学 | A kind of infrared image cirrus detection method based on neighborhood intensity texture coding |
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