CN104751413B - A kind of SAS image automatic balancing methods based on time-varying curve model - Google Patents
A kind of SAS image automatic balancing methods based on time-varying curve model Download PDFInfo
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Abstract
The invention provides a kind of SAS image automatic balancing methods based on time-varying curve model, varied curve when having been derived based on propagation model, water-bed backscattering model and SAS imaging models（Time Variant Curve, TVC）The expression formula of model, with reference to the statistical nature of SAS images, varied curve observed quantity when deriving, and by this when varied curve observed quantity based on, utilize nonlinear least square fitting method to complete the estimation of time-varying parameter of curve；It is finally based on time-varying parameter of curve and its model carries out the automatic equalization of SAS images, data are tried with Hu Hai to verify the SAS image automatic balancing methods of the present invention, as a result show that the time-varying curve model expression formula derived and test data have the preferable goodness of fit, effectively eliminate the unbalanced problem of SAS images.
Description
Technical field
The present invention relates to technical field of image processing, more particularly to a kind of SVS images based on time-varying curve model are automatic
Equalization methods.
Background technology
By sound source level caused by Acoustic Wave Propagation, absorption loss water, water-bed backscatter intensity, sonar transmission and reception system
The influence of the factors such as fluctuation, sonar image intensity (brightness) often has bigger fluctuating.Due to the corresponding void of different distance
Intend aperture length different, the Strength Changes scope meeting of synthetic aperture sonar (Synthetic Aperture Sonar, SAS) image
It is bigger.Interpretation and processing of the unbalanced problem of sonar image intensity to sonar image cause very big influence.On the one hand, due to
The dynamic range of image output device (display screen, printer etc.) is limited, SAS image intensities are unbalanced be likely to result in it is important thin
Section or target are lost.On the other hand, computer aided detection or sorting technique (Computer Aided based on SAS images
Detection/Classification, CAD/CAC), target automatic detection and recognition methods (Automatic Target
Detection/Recognition, ATD/ATR) the big strength difference being dependent between target and background, unbalanced sonar chart
As CAD/CAC and ATD/ATR methods can be caused to fail.Therefore, image equalization is a committed step in sonar image processing.
In existing sonar image equalization processing method, mostly using the general equalization methods in optical image security, such as Gauss equilibrium.
These equalization methods do not account for the influence to sonar image intensity such as Acoustic Wave Propagation, water-bed back scattering and SAS imagings, because
This application effect is undesirable.
The content of the invention
It is an object of the present invention to be influenceed and different distance by outside environmental elements to solve synthetic aperture sonar picture
The unbalanced technical problem of image caused by corresponding virtual aperture length is different, the present invention provides one kind and is based on time-varying curvilinear mold
The SAS image automatic balancing methods of type, SAS images in a balanced way can be obtained using the automatic balancing method.
To achieve the above object, the present invention provides a kind of SAS image automatic balancing methods based on time-varying curve model, institute
The SAS image automatic balancing methods stated include：
Step 1) time-varying curve model is built, determine parameter to be estimated in the time-varying curve model；
Step 2) define image pixel set and comprising its internal pixel value, at the orientation coordinate of determination
The observed quantity of varied curve when SAS images are obtained；
Step 3) according to step 1) in parameter and step 2 to be estimated) in obtain observed quantity, utilize non-linear minimum
Two methods for multiplying fitting estimate the best estimator of parameter vector, and the optimal of varied curve is estimated when being determined according to the best estimator
Meter；
Step 4) according to step 1) in time-varying curve model and step 3) in obtained optimal estimation varied curve when trying to achieve,
And the image after equilibrium is calculated using varied curve when this.
Be used as the further improvement of above-mentioned technical proposal, the step 1) in time-varying curve model logarithm expression-form
For：
Wherein, a1Represent logarithm term coefficient, a3、b1And b2Represent linear term change, a2Represent quadratic term change, b3、c1And c2
Represent constant term change, a3、a2、a1、b1、c1、r1And r2It is parameter to be estimated, fwater、fapexAnd flgRepresent TVC changes
The change point of rule.
Be used as the further improvement of above-mentioned technical proposal, the step 2) in when varied curve observed quantity pass through it is following calculate
Formula is tried to achieve：
gy(r)=median { Ir} (7)
Above formula is met：
And
Y=(ymin+ymax)/2 (9)
Wherein, gy(r) observed quantity of varied curve, I when representingrImage pixel set is represented, I [r, y] represents sonar image, r
Distance is represented to coordinate, y represents orientation coordinate, and median represents to take median operation.
Be used as the further improvement of above-mentioned technical proposal, the step 3) in parameter vector be represented by：
Separately have：
Wherein,Represent parameter vector,Represent best estimator,The quadratic sum of error is represented,Table
The value to be estimated of varied curve, g (r when showingi) represent when varied curve observed quantity；
By best estimatorSubstitute into time-varying curve model f (r), the optimal estimation f of varied curve when producing*(r)。
Be used as the further improvement of above-mentioned technical proposal, the step 3) in best estimatorAsked using confidential interval method
, its step includes：
Step 301) in coordinate pointsNeighborhoodIt is interior to useThe second Taylor series it is approximate
I.e.：
Wherein, H representative functionsMatrix of second derivatives,Representative functionIn coordinate pointsThe gradient at place,
Q is representedApproximate function,
Above formula is met：
Step 302) optimum stepsize is calculated according to Local Minimum criterion and above-mentioned (10) formulaThe optimum stepsizeTable
It is shown as：
And
Wherein, Δ represents contiguous range；
Step 303) by step 302) in the optimum stepsize that obtainsTry to achieveIf
Then receiveAs new coordinate points, while increasing contiguous range Δ；Otherwise, contiguous range Δ is reduced；
Step 304) repeat above-mentioned steps 301) to step 303) until obtaining best estimator
It is used as the further improvement of above-mentioned technical proposal, the step 4) by combining above-mentioned (1) formula, (2) formula and (3) formula
Varied curve when trying to achieve, and calculate the image after equilibrium using the following calculation formula of time-varying curve negotiating：
Wherein：
f*(r) optimal estimation of varied curve, I when representing0(r, y) represents the image after equilibrium.
Be used as the further improvement of above-mentioned technical proposal, described step 4) in image I after equilibrium0(r, y) is by adjusting
The scale parameter enhancing contrast of whole Weibull distributions, all view data are normalized between [0,1], image I0(r,y)
It is indicated using the floating number between [0,1], user's expectation target is better than S with respect to the signal to noise ratio of background, then expected background
Intensity is expressed as：
Then there is the image after enhancing contrast to be transformed to：
Wherein, vmodeRepresent image I0The mode of (r, y), it is used to measure image I0The intensity of (r, y) background.
A kind of advantage of SAS image automatic balancing methods based on time-varying curve model of the present invention is：
Varied curve (Time when having been derived based on propagation model, water-bed backscattering model and SAS imaging models
Variant Curve, TVC) model expression formula, with reference to the statistical nature of SAS images, varied curve observed quantity when deriving, and
By this when varied curve observed quantity based on, utilize nonlinear least square fitting method to complete the estimation of time-varying parameter of curve；Finally
The automatic equalization of SAS images is carried out based on time-varying parameter of curve and its model, with Hu Hai try data to the SAS images of the present invention from
Dynamic equalization methods are verified, are as a result shown that the time-varying curve model expression formula derived has with test data and are preferably coincide
Degree, effectively eliminates the unbalanced problem of SAS images.
Brief description of the drawings
Fig. 1 is a kind of SAS image automatic balancing method flow charts based on time-varying curve model of the invention
Fig. 2 is vertical beam schematic diagram.
Embodiment
With reference to specific drawings and examples to a kind of SAS images based on time-varying curve model for providing of the present invention from
Dynamic equalization methods are further elaborated.
As shown in figure 1, a kind of SAS image automatic balancing methods based on time-varying curve model of the present invention include：Time-varying
Curve model construction, time-varying parameter of curve estimation, when four part operations of varied curve observed quantity estimation and image dynamic equalization.
Based on aforementioned four part operation, the SAS images automatic balancing method based on time-varying curve model is realized
Specific steps include：Step 1) time-varying curve model is built, determine parameter to be estimated in the time-varying curve model；The time-varying
Curve model is represented by：
Wherein, a1Represent logarithm term coefficient, a3、b1And b2Represent linear term change, a2Represent quadratic term change, b3、c1And c2
Represent constant term change, a1,a3,b1,b3,c1,c2,r1,r2It is parameter to be estimated, fwater、fapexAnd flgRepresent that TVC becomes
The change point of law；
Work as r=r1When, when varied curve should meet the condition of continuity of following formula：
a3r1+b3=a2r1 2+b2r1+c2 (15)
In order to ensure the continuity of sonar image, f in (1) formulaapexWith flgIt should be consecutive variations, therefore work as r=r2When,
When varied curve should meet the continuity and slickness condition of following formula：
Using above-mentioned (15) formula and (16) formula, f (r) number of parameters can be reduced to seven, i.e. a3、a2、a1、b1、c1、
r1And r2；As shown in Figure 2, the separation of water body echo and water-bed echo is B.Ideally, r1For line segment AB length；It is real
In the case of border, r is influenceed1And r2Factor it is a lot, such as sonar transducer array posture, water-bed landform, the depth of water, sonar transducer array vertically to angle of release,
SAS basic matrix established angles etc..
Step 2) define image pixel set and comprising its internal pixel value, at the orientation coordinate of determination
The observed quantity of varied curve when SAS images are obtained；
Step 3) according to step 1) in parameter and step 2 to be estimated) in obtain observed quantity, utilize non-linear minimum
Two methods for multiplying fitting estimate the best estimator of parameter vector, and the optimal of varied curve is estimated when being determined according to the best estimator
Meter；The parameter vector is represented by：
Separately have：
Wherein,Represent parameter vector,Represent best estimator,The quadratic sum of error is represented,Table
The value to be estimated of varied curve, g (r when showingi) represent when varied curve observed quantity；
By best estimatorSubstitute into time-varying curve model f (r), the optimal estimation f of varied curve when producing*(r)；
Step 4) according to step 1) in time-varying curve model and step 3) in obtained optimal estimation varied curve when trying to achieve,
And the image after equilibrium is calculated using varied curve when this.Varied curve when being tried to achieve with reference to above-mentioned (1) formula, (2) formula and (3) formula,
And calculate the image after equilibrium using the following calculation formula of time-varying curve negotiating：
Separately have：
Wherein, f*(r) varied curve, I when representing0(r, y) represents the image after equilibrium.
Based on above-described embodiment, the step 2) in when varied curve observed quantity can be tried to achieve by following calculation formula：
gy(r)=median { Ir} (7)
Above formula is met：
And
Y=(ymin+ymax)/2 (9)
Wherein, gy(r) observed quantity of varied curve, I when representingrImage pixel set is represented, I [r, y] represents sonar image, r
Distance is represented to coordinate, y represents orientation coordinate, and median represents to take median operation.In actual mechanical process, according to sonar
The difference of image statisticses feature, above-mentioned steps 2) in when varied curve observed quantity can also using average or other operation substitute in
Value Operations.
In addition, above-mentioned steps 3) in best estimatorIt can be tried to achieve using confidential interval method, its specific steps includes：
Step 301) in coordinate pointsNeighborhoodIt is interior to useThe second Taylor series it is approximate
I.e.：
Wherein, H representative functionsMatrix of second derivatives,Representative functionIn coordinate pointsThe gradient at place,
Q is representedApproximate function,
Above formula is met：
Step 302) optimum stepsize is calculated according to Local Minimum criterion and above-mentioned (10) formulaThe optimum stepsizeTable
It is shown as：
And
Wherein, Δ represents contiguous range；
Step 303) by step 302) in the optimum stepsize that obtainsTry to achieveIf
Then receiveAs new coordinate points, while increasing contiguous range Δ；Otherwise, contiguous range Δ is reduced；
Step 304) repeat above-mentioned steps 301) to step 303) until obtaining best estimator
On the one hand, because the corresponding echo strength of different substrates is variant, this species diversity has for the application such as Seafloor Classification
Important reference role.Therefore, in order to ensure that echo strength information is not lost, in above-mentioned (5) formula of application, β is added
Parameter.On the other hand, because human eye or the denotable dynamic range of computer are limited, therefore the image after (5) formula is balanced
I0(r, y) typically also needs to can be only achieved optimal display effect by strengthening contrast.Described step 4) in after equilibrium
Image I0(r, y) can strengthen contrast by adjusting the scale parameter of Weibull distributions, and all view data are normalized to
Between [0,1], image I0(r, y) is indicated using the floating number between [0,1], noise of user's expectation target with respect to background
Than better than S, then expected background intensity is expressed as：
Then there is the image after enhancing contrast to be transformed to：
Wherein, vmodeRepresent image I0The mode of (r, y), it is used to measure image I0The intensity of (r, y) background.
Understood according to formula (2) and formula (4), the image enhaucament conversion that (14) formula is completed is equivalent to the yardstick for changing image distribution
Parameter lambda.After equilibrium treatment, image I0The corresponding Weibull distributed constants phase of the pixel value of (r, y) in different distance
Closely, i.e. the form parameter and scale parameter of different distance epigraph pixel are close.Therefore it may only be necessary to adjust the scale parameter of distribution
The enhanced purpose of contrast can be reached.
It should be noted last that, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted.Although ginseng
The present invention is described in detail according to embodiment, it will be understood by those within the art that, to the technical side of the present invention
Case is modified or equivalent substitution, and without departure from the spirit and scope of technical solution of the present invention, it all should cover in the present invention
Right among.
Claims (6)
1. a kind of SAS image automatic balancing methods based on time-varying curve model, it is characterised in that described SAS images are automatic
Equalization methods include：
Step 1) time-varying curve model is built, determine parameter to be estimated in the time-varying curve model；
Step 2) define image pixel set and comprising its internal pixel value, schemed by the SAS at the orientation coordinate of determination
The observed quantity of varied curve during as obtaining；
Step 3) according to step 1) in parameter and step 2 to be estimated) in obtain observed quantity, utilize non-linear least square
The method of fitting estimates the best estimator of parameter vector, and when being determined according to the best estimator varied curve optimal estimation；
Step 4) according to step 1) in time-varying curve model and step 3) in obtained optimal estimation varied curve when trying to achieve, and profit
The image after equilibrium is calculated with varied curve when this；
The step 1) in the logarithm expression-form of time-varying curve model be：
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Constant term changes, a3、a2、a1、b1、c1、r1And r2It is parameter to be estimated, fwater、fapexAnd flgRepresent TVC changing rules
Change point.
2. the SAS image automatic balancing methods according to claim 1 based on time-varying curve model, it is characterised in that institute
State step 2) in when varied curve observed quantity tried to achieve by following calculation formula：
gy(r)=median { Ir} (7)
Above formula is met：
And
Y=(ymin+ymax)/2 (9)
Wherein, gy(r) observed quantity of varied curve, I when representingrImage pixel set is represented, I [r, y] represents sonar image, and r is represented
Distance is to coordinate, and y represents orientation coordinate, and median represents to take median operation.
3. the SAS image automatic balancing methods according to claim 2 based on time-varying curve model, it is characterised in that institute
State step 3) in parameter vector be represented by：
Separately have：
Wherein,Represent parameter vector,Represent best estimator,The quadratic sum of error is represented,During expression
The value to be estimated of varied curve, g (ri) represent when varied curve observed quantity；
By best estimatorSubstitute into time-varying curve model f (r), the optimal estimation f of varied curve when producing*(r)。
4. the SAS image automatic balancing methods according to claim 3 based on time-varying curve model, it is characterised in that institute
State step 3) in best estimatorTried to achieve using confidential interval method, its step includes：
Step 301) in coordinate pointsNeighborhoodIt is interior to useThe second Taylor series it is approximate
I.e.：Wherein, H representative functionsTwo
Order derivative matrix,Representative functionIn coordinate pointsThe gradient at place, q is representedApproximate function,
Above formula is met：Step 302) according to Local Minimum criterion and upper
State (10) formula and calculate optimum stepsizeThe optimum stepsizeIt is expressed as：
AndWherein, Δ represents contiguous range；
Step 303) by step 302) in the optimum stepsize that obtainsTry to achieveIfThen
ReceiveAs new coordinate points, while increasing contiguous range Δ；Otherwise, contiguous range Δ is reduced；
Step 304) repeat above-mentioned steps 301) to step 303) until obtaining best estimator
5. the SAS image automatic balancing methods according to claim 3 based on time-varying curve model, it is characterised in that institute
State step 4) varied curve when being tried to achieve by combining above-mentioned (1) formula, (2) formula and (3) formula, and utilize the following meters of the time-varying curve negotiating
Calculate formula and calculate the image after equilibrium：
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6. the SAS image automatic balancing methods according to claim 5 based on time-varying curve model, it is characterised in that institute
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| CN106875383B (en) * | 2017-01-24 | 2020-05-08 | 北京理工大学 | Content insensitive fuzzy image quality evaluation method based on Weibull statistical characteristics |
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