CN102063615A - Beacon light optimizing and recognizing denoising method based on spot noise distribution topological characteristic - Google Patents

Abstract

The invention discloses a beacon light optimizing and recognizing denoising method based on a spot noise distribution topological characteristic, relating to a beacon light optimizing and recognizing denoising method and solving the problem of poor real time caused by large computation amount of the traditional beacon light optimizing and recognizing denoising method. The method comprises the steps of: picking up a beacon light image of the current time by adopting a CCD (Charge Coupled Device) and carrying out threshold segmentation to obtain a light spot centroid distributing image; calculating a semi-circle area discrimination amount delta S3 of a characteristic value vector of the centroid distribution of each light spot, an arc length discrimination amount delta L3 of the characteristic value vector and a centroid coordinate change rate discrimination amount delta C; and judging. Therefore, background light and beacon light are continuously tracked, recognized and denoised. The invention is suitable for optimizing, recognizing and denoising beacon light.

Description

A kind of beacon beam optimization identification noise-reduction method based on spot noise distributed topology characteristic

Technical field

The present invention relates to a kind of beacon beam optimization identification noise-reduction method.

Background technology

The photoelectric image identification of satellite laser communications system need be carried out under the fixed star background of space, and fixed star forms some pictures on photodetector, and detector need be screened the light beam spot picture that actual needs is followed the tracks of in all multiple spot pictures, and these light beam spots look like to be spot noise.These spot noises can make the identification of the beacon beam difficulty that becomes, can make the sensing deviation of satellite optical communication system when serious, influences the foundation of link and stablize.The method of eliminating the common employing of spot noise influence is the inquiry star catalogue, and detailed process is that spot noise and fixed star background are mated, judge whether it is the fixed star bias light, the shortcoming of this method is that calculated amount is bigger, and real-time is relatively poor, is not easy to use in satellite optical communication.

Summary of the invention

The present invention causes the relatively poor problem of real-time in order to solve existing beacon beam optimization identification noise-reduction method greatly owing to calculated amount, thereby a kind of beacon beam optimization identification noise-reduction method based on spot noise distributed topology characteristic is provided.

A kind of beacon beam optimization identification noise-reduction method based on spot noise distributed topology characteristic, its method is:

Adopt the CCD camera to begin the continuous acquisition multiple image from initial time, the sampling period of CCD camera is n, and the sampling rate of CCD camera is f _CCD

With next of initial time constantly as current time, with image that this moment collected image as current time;

Step 1, the image of current time is carried out Threshold Segmentation handle, obtain the facula mass center distributed image;

Step 2, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{S_3^{\′}}{S_3}=1+\mathrm{\Δ}{S}_3$

Calculate the semicircle area differentiation amount Δ S of the feature value vector that the barycenter of each hot spot distributes ₃In the formula: S ' ₃And S ₃The semicircle area parameter of the feature value vector that the spot noise of the CCD camera institute images acquired that is respectively the previous moment of current time and inscribes back a period of time distributes;

Step 3, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{L_3^{\′}}{L_3}=1+\mathrm{\Δ}{L}_3$

Calculate the arc length differentiation amount Δ L of the feature value vector that the barycenter of each hot spot distributes ₃In the formula: L ' ₃And L ₃Be respectively the arc length parameter of the feature value vector of the previous moment of current time and the CCD camera institute images acquired mid point noise profile that inscribe for the moment the back;

Step 4, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{\sqrt{{(x_{i1}-x_{i2})}^2+{(y_{i1}-y_{i2})}^2}}{n\·\frac{1}{f_{\mathrm{CCD}}}}=1+\mathrm{\ΔC}$

Calculate the center-of-mass coordinate rate of change differentiation amount Δ C of this hot spot; In the formula: (x _I1, y _I1) be the center-of-mass coordinate of i hot spot initial time, (x _I2, y _I2) be the facula mass center coordinate that obtains in n sampling period, K is the hot spot number in the CCD camera ken, i, n are positive integer;

Each hot spot in step 5, the traversal facula mass center distributed image carries out beacon beam identification, to the identifying of each hot spot is: the semicircle area differentiation amount Δ S that judges the feature value vector that the barycenter of this hot spot distributes ₃, calculate the arc length differentiation amount Δ L of the feature value vector that facula mass center distributes ₃Whether all less than 5 pixels, if judged result is for being, execution in step May Day then; If judged result is that then execution in step five or two;

Step May Day, judge that whether the center-of-mass coordinate rate of change differentiation amount Δ C of this hot spot is less than five pixel/p.s.s, if judged result is for being, then this hot spot is a fixed star background hot spot, when all hot spots of traversal are fixed star background hot spot, there is not the beacon beam hot spot, execution in step six in the image of expression current time; If judged result is that then execution in step five or two;

Step 5 two, the center-of-mass coordinate rate of change differentiation amount Δ C of all hot spots is compared, wherein the pairing hot spot of center-of-mass coordinate rate of change differentiation amount Δ C the maximum is the beacon beam hot spot of current time, realizes beacon beam identification noise reduction, and execution in step six;

Step 6, image that the next one is constantly corresponding are returned step 1 and are proceeded beacon beam identification as the image of current time.

Beneficial effect: the present invention proposes a kind of beacon beam optimization identification noise-reduction method based on spot noise distributed topology characteristic, make full use of the eigenwert of ccd image spot noise distributed topology structure, realized accurate identification to beacon beam, because the calculated amount of this kind method is very little, so have good real time performance.

Description of drawings

Fig. 1 is embodiment one a mid point noise profile eigenvalue calculation synoptic diagram.

Embodiment

Embodiment one, a kind of beacon beam optimization identification noise-reduction method based on spot noise distributed topology characteristic, its method is:

Adopt the CCD camera to begin the continuous acquisition multiple image from initial time, the sampling period of CCD camera is n, and the sampling rate of CCD camera is f _CCD

With next of initial time constantly as current time, with image that this moment collected image as current time;

Step 1, the image of current time is carried out Threshold Segmentation handle, obtain the facula mass center distributed image;

Step 2, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{S_3^{\′}}{S_3}=1+\mathrm{\Δ}{S}_3$

Calculate the semicircle area differentiation amount Δ S of the feature value vector that the barycenter of each hot spot distributes ₃In the formula: S ' ₃And S ₃The semicircle area parameter of the feature value vector that the spot noise of the CCD camera institute images acquired that is respectively the previous moment of current time and inscribes back a period of time distributes;

Step 3, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{L_3^{\′}}{L_3}=1+\mathrm{\Δ}{L}_3$

Calculate the arc length differentiation amount Δ L of the feature value vector that the barycenter of each hot spot distributes ₃In the formula: L ' ₃And L ₃Be respectively the arc length parameter of the feature value vector of the previous moment of current time and the CCD camera institute images acquired mid point noise profile that inscribe for the moment the back;

Step 4, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{\sqrt{{(x_{i1}-x_{i2})}^2+{(y_{i1}-y_{i2})}^2}}{n\·\frac{1}{f_{\mathrm{CCD}}}}=1+\mathrm{\ΔC}$

Calculate the center-of-mass coordinate rate of change differentiation amount Δ C of this hot spot; In the formula: (x _I1, y _I1) be the center-of-mass coordinate of i hot spot initial time, (x _I2, y _I2) be the facula mass center coordinate that obtains in n sampling period, K is the hot spot number in the CCD camera ken, i, n are positive integer;

Each hot spot in step 5, the traversal facula mass center distributed image carries out beacon beam identification, to the identifying of each hot spot is: the semicircle area differentiation amount Δ S that judges the feature value vector that the barycenter of this hot spot distributes ₃, calculate the arc length differentiation amount Δ L of the feature value vector that facula mass center distributes ₃Whether all less than 5 pixels, if judged result is for being, execution in step May Day then; If judged result is that then execution in step five or two;

Step May Day, judge that whether the center-of-mass coordinate rate of change differentiation amount Δ C of this hot spot is less than five pixel/p.s.s, if judged result is for being, then this hot spot is a fixed star background hot spot, when all hot spots of traversal are fixed star background hot spot, there is not the beacon beam hot spot, execution in step six in the image of expression current time; If judged result is that then execution in step five or two;

Step 5 two, the center-of-mass coordinate rate of change differentiation amount Δ C of all hot spots is compared, wherein the pairing hot spot of center-of-mass coordinate rate of change differentiation amount Δ C the maximum is the beacon beam hot spot of current time, realizes beacon beam identification noise reduction, and execution in step six;

Step 6, image that the next one is constantly corresponding are returned step 1 and are proceeded beacon beam identification as the image of current time.

Repeat above-mentioned steps, realize tracking beacon beam.

Principle of work: semi arch closed curve length and each semicircle area summation of the whole hot spots of at first definition traversal ccd image are the feature value vector that hot spot distributes, the former is called the arc length parameter, the latter is called the semicircle parameter, and obviously, this feature value vector is a bivector.Shown in Fig. 1, each semi arch segment length sum is P ₀₁+ P ₂₀+ P ₁₂If, S ₁Be by hot spot 0 and hot spot 1 the section of being in line and the semicircle camber line P that is connected these two hot spots ₀₁The semi-disc area that surrounds, the understanding of all the other marks in like manner.So, all semicircle area summation is S ₁+ S ₂+ S ₃At this moment, the feature value vector of spot noise distribution is exactly bivector (P ₀₁+ P ₂₀+ P ₁₂, S ₁+ S ₂+ S ₃).If hot spot 1 moves on hot spot 0 and hot spot 2 formed straight-line segments, the feature value vector (P that spot noise distributes ₀₁+ P ₂₀+ P ₁₂, S ₁+ S ₂+ S ₃) will change, promptly feature value vector can embody the variation that spot noise distributes.

For the beacon beam that provides the eigenwert of utilizing the picture point noise profile is caught recognition methods, catch two differentiation amounts of identification beacon light earlier from two continuous frames sampled images spot noise distribution characteristics value structure.

Order:

$\frac{S_3^{\′}}{S_3}=1+\mathrm{\Δ}{S}_3$

Wherein, S ' ₃And S ₃The semicircle area parameter of the feature value vector of two CCD images acquired gained spot noise distributions constantly before and after being respectively, Δ S ₃Be semicircle area differentiation amount, it is mainly determined by the moving jointly of composition error, spot noise and beacon beam of system.Order:

$\frac{L_3^{\′}}{L_3}=1+\mathrm{\Δ}{L}_3$

Wherein, L ' ₃And L ₃The arc length parameter of the feature value vector of two CCD images acquired gained spot noise distributions constantly before and after being respectively, Δ L ₃Be arc length differentiation amount, it is mainly determined by the moving jointly of composition error, spot noise and beacon beam of system.

$\frac{\sqrt{{(x_{i1}-x_{i2})}^2+{(y_{i1}-y_{i2})}^2}}{n\·\frac{1}{f_{\mathrm{CCD}}}}=1+\mathrm{\ΔC}(i=\mathrm{1,2,3},......,K)$

Wherein, (x _I1, y _I1) be the center-of-mass coordinate of i hot spot initial time, n is the sampling period of CCD, f _CCDBe the sampling rate of CCD, (x _I2, y _I2) center-of-mass coordinate when being n sampling period, Δ C be utilize spot noise distribute in the slow variation characteristic of center-of-mass coordinate carry out the changes in coordinates rate differentiation amount that beacon beam is caught identification, it is an additional differentiation amount, is mainly determined by the system synthesis error, and K is the hot spot number in the CCD ken.

Select the CCD camera of the MTV-1801 of Taiwan Min Tong company production in the present embodiment for use, major parameter is as follows: several 795 (H) * 596 (V) of pixel; Spectral response range 400nm～1100nm; Resolution 600TVL; Detection sensitivity 0.021lx; Signal to noise ratio (S/N ratio) is greater than 46dB; Working temperature is-10 ℃～50 ℃.And adopt video data acquiring card, image information is imported computing machine based on 1394 agreements.

Claims (1)

1. noise-reduction method is discerned in the beacon beam optimization based on spot noise distributed topology characteristic, and it is characterized in that: its method is:

Adopt the CCD camera to begin the continuous acquisition multiple image from initial time, the sampling period of CCD camera is n, and the sampling rate of CCD camera is f _CCD

With next of initial time constantly as current time, with image that this moment collected image as current time;

Step 1, the image of current time is carried out Threshold Segmentation handle, obtain the facula mass center distributed image;

Step 2, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{S_3^{\′}}{S_3}=1+\mathrm{\Δ}{S}_3$

Calculate the semicircle area differentiation amount Δ S of the feature value vector that the barycenter of each hot spot distributes ₃In the formula: S ' ₃And S ₃The semicircle area parameter of the feature value vector that the spot noise of the CCD camera institute images acquired that is respectively the previous moment of current time and inscribes back a period of time distributes;

Step 3, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{L_3^{\′}}{L_3}=1+\mathrm{\Δ}{L}_3$

Calculate the arc length differentiation amount Δ L of the feature value vector that the barycenter of each hot spot distributes ₃In the formula: L ' ₃And L ₃Be respectively the arc length parameter of the feature value vector of the previous moment of current time and the CCD camera institute images acquired mid point noise profile that inscribe for the moment the back;

Step 4, the facula mass center distributed image that obtains according to step 1, for each hot spot wherein, all pass through formula:

$\frac{\sqrt{{(x_{i1}-x_{i2})}^2+{(y_{i1}-y_{i2})}^2}}{n\·\frac{1}{f_{\mathrm{CCD}}}}=1+\mathrm{\ΔC}$

Calculate the center-of-mass coordinate rate of change differentiation amount Δ C of this hot spot; In the formula: (x _I1, y _I1) be the center-of-mass coordinate of i hot spot initial time, (x _I2, y _I2) be the facula mass center coordinate that obtains in n sampling period, K is the hot spot number in the CCD camera ken, i, n are positive integer;

Each hot spot in step 5, the traversal facula mass center distributed image carries out beacon beam identification, to the identifying of each hot spot is: the semicircle area differentiation amount Δ S that judges the feature value vector that the barycenter of this hot spot distributes ₃, calculate the arc length differentiation amount Δ L of the feature value vector that facula mass center distributes ₃Whether all less than 5 pixels, if judged result is for being, execution in step May Day then; If judged result is that then execution in step five or two;

Step May Day, judge that whether the center-of-mass coordinate rate of change differentiation amount Δ C of this hot spot is less than five pixel/p.s.s, if judged result is for being, then this hot spot is a fixed star background hot spot, when all hot spots of traversal are fixed star background hot spot, there is not the beacon beam hot spot, execution in step six in the image of expression current time; If judged result is that then execution in step five or two;

Step 5 two, the center-of-mass coordinate rate of change differentiation amount Δ C of all hot spots is compared, wherein the pairing hot spot of center-of-mass coordinate rate of change differentiation amount Δ C the maximum is the beacon beam hot spot of current time, realizes beacon beam identification noise reduction, and execution in step six;

Step 6, image that the next one is constantly corresponding are returned step 1 and are proceeded beacon beam identification as the image of current time.

Images