CN102289802B - Method for selecting threshold value of image fusion rule of unmanned aerial vehicle based on wavelet transformation - Google Patents

Abstract

The invention discloses a method for selecting a threshold value of an image fusion rule of an unmanned aerial vehicle based on wavelet transformation and is applicable to image reconnaissance of the unmanned aerial vehicle. The method comprises the following steps of: firstly, performing a series of operation, such as fusion of area energy measures, presetting of the threshold value, determination of a fusion operator according to the threshold value and image fusion according to the fusion operator, on an image in the same sequence; secondly, determining a phase-position correlation value of the fused image; and finally, determining the optimum phase-position correlation value by traversing a threshold value range, and thus obtaining the optimum threshold value with which the sequence image is fused. The method is simple in implementation. By the method, the optimum threshold value can be obtained by automatically optimizing the threshold value. After the optimum threshold value of the same sequence is determined by using the method, the image in the same sequence is fused, so a good fusion effect can be achieved, and the method is applicable to the image reconnaissance of the unmanned aerial vehicle with large data quantity.

Description

A kind of system of selection of the unmanned plane Image Fusion Rule threshold value based on wavelet transformation

Technical field

The invention belongs to the fields such as satellite remote control engineering, image fusion technology, be specifically related to a kind of system of selection to unmanned plane reconnaissance image fusion rule threshold value of being used for based on wavelet transformation.

Background technology

Unmanned spacecraft is being brought into play significant effect with its accurate, efficient and nimble scouting, interference, deception, search, the multiple fight capability such as the school is penetrated and fight in modern war under informal condition.In order to obtain more reliable Image Intelligence (IMINT), unmanned plane has carried increasing sensor.In the face of numerous image informations, need to merge for the information of unmanned plane feature of image with a plurality of source images, with obtain more accurately, more comprehensive reliably iamge description, eliminate redundancy and contradiction between the allos information, strengthen transparency information in the image, improve precision, reliability and the utilization rate explained.Therefore, the unmanned plane image is merged accurately, applied research has important meaning for unmanned plane.

The multi-resolution Fusion analysis of wavelet transformation is the study hotspot in current demand signal and image co-registration field.It can resolve into original image a series of subimages with different spatial resolutions and frequency domain characteristic, the localized variation feature that fully reflects original image, original image is decomposed in a series of channels, utilize the pyramidal structure after decomposing, will be merged at a plurality of decomposition layers, a plurality of band utilization fusion rule by feature and the details that fused images is carried separately.Fusion rule choose the important component part that is based on wavelet transform fusion.Based on the fusion rule of pixel provincial characteristics be more suitable for being applied to this class source images of unmanned plane allos image can't accuracy registration, situation that the spectral signature difference is larger, for example existing small echo region energy is estimated fusion rule, but this rule-like is definite threshold by rule of thumb just often, can't accomplish automatic adjusting, it is limited to adapt to scene, and threshold value is chosen bad meeting and caused the problems such as unmanned plane image interfusion method poor effect, poor anti jamming capability.

Summary of the invention

The present invention is directed in the present unmanned plane image interfusion method and can't independently choose optimal threshold, can cause the problems such as unmanned plane image interfusion method poor effect, poor anti jamming capability, propose a kind of system of selection of the unmanned plane Image Fusion Rule threshold value based on wavelet transformation.

The system of selection of a kind of unmanned plane Image Fusion Rule threshold value based on wavelet transformation of the present invention may further comprise the steps:

Step 1: region energy is estimated fusion, may further comprise the steps:

Step 1.1, read in unmanned plane source images A and B;

Step 1.2, determine respectively the energy of regional area on image A and image B correspondence direction, the corresponding resolution according to following formula

With

$E_j^{\mathrm{\ϵ}}(x,y)=\underset{n\∈L,m\∈K}{\mathrm{\Σ}}{\mathrm{\ω}}^{\mathrm{\ϵ}}(n,m){\left[D_j^{\mathrm{\ϵ}}(x+n,y+m)\right]}^2,\mathrm{\ϵ}=\mathrm{1,2,3}$

Wherein, j is the unmanned plane image resolution ratio; ε is the direction subscript, ε=1,2, and 3 represent respectively level, vertical and three directions in diagonal angle;

For under the j resolution, on the ε direction, the energy of local area of position centered by coordinate points (x, y);

For in the high fdrequency component under the j resolution, on the ε direction; For with Corresponding weight function; L, K are the size of regional area;

Step 1.3, determine the matching degree of regional area on image A and image B correspondence direction, the corresponding resolution

$M_{j,\mathrm{AB}}^{\mathrm{\ϵ}}(x,y)=\frac{2\underset{n\∈L,m\∈K}{\mathrm{\Σ}}{\mathrm{\ω}}^{\mathrm{\ϵ}}(n,m)D_{j,A}^{\mathrm{\ϵ}}(x+n,y+m)D_{j,B}^{\mathrm{\ϵ}}(x+n,y+m)}{E_{j,A}^{\mathrm{\ϵ}}(x,y)+E_{j,B}^{\mathrm{\ϵ}}(x,y)}$

Wherein, With

Be respectively image A and image B in the high fdrequency component under the j resolution, on the ε direction;

Step 1.4, predetermined threshold value t.

Step 2: determine to merge operator S according to threshold value t ₁With S ₂:

If $M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)<t,$ Then

$\left\{\begin{array}{cc}{S}_1=1,S_2=0& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)\&GreaterEqual;E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\\ {\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="HDA0000067088490000021.png"\; state="\{\{state\}\}">S</figure-callout>}}_1=0,S_2=1& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)<E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}$

If $M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)\&GreaterEqual;t,$ Then

$\left\{\begin{array}{cc}{S}_1=W_{j,\max }^{\mathrm{\&epsiv;}}(x,y),S_2=W_{j,\min }^{\mathrm{\&epsiv;}}(x,y)& E_{j,A}^{\mathrm{\&epsiv;}}\left(x,y\right)\&GreaterEqual;E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\\ S_1=W_{j,\min }^{\mathrm{\&epsiv;}}(x,y),S_2=W_{j,\max }^{\mathrm{\&epsiv;}}(x,y)& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)<E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}$

In the formula (4)

Be defined as:

$\left\{\begin{array}{c}{W}_{j,\min }^{\mathrm{\&epsiv;}}(x,y)=\frac{1}{2}-\frac{1}{2}\left[\frac{1-M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)}{1-t}\right]\\ W_{j,\max }^{\mathrm{\&epsiv;}}(x,y)=1-W_{j,\min }^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}.$

Step 3: image co-registration specifically may further comprise the steps:

Step 3.1, two width of cloth source images are carried out wavelet transform, make up small echo gold tower;

Step 3.2, utilization are merged operator the wavelet pyramid that obtains are merged;

$D_{j,F}^{\mathrm{\&epsiv;}}(x,y)=S_1{D}_{j,A}^{\mathrm{\&epsiv;}}(x,y)+S_2{D}_{j,B}^{\mathrm{\&epsiv;}}(x,y)$

Wherein,

Locate the value of pixel at (x, y) at the component under the j resolution, on the ε direction for the fused images F that obtains after merging;

Step 3.3, fused images F is carried out the discrete wavelet inverse transformation.

Step 4: determine the phase place correlation, specifically may further comprise the steps:

Step 4.1, fused images F done discrete Fourier transformation obtain frequency domain figure as U (ξ):

$U\left(\mathrm{\&xi;}\right)=\underset{x}{\mathrm{\&Sigma;}}F\left(x\right)e^{-\frac{2\mathrm{i\&pi;}}{n}<x,\mathrm{\&xi;}>}$

Wherein, n is the sampling point number in the fused images,＜x, ξ〉be the inner product of x and ξ, x is the independent variable of fused images F time domain, ξ is the independent variable of frequency domain figure picture;

Frequency domain distribution after the fused images F discrete Fourier transformation is:

Wherein | U (ξ) | be the amplitude of fused images F discrete Fourier transformation,

It is the phase place of fused images F discrete Fourier transformation;

Step 4.2, generation add the N width of cloth image of random phase, and N is the number of times of Monte Carlo simulation:

Each time emulation is all with phase place

Increase a random offset δ S, obtain a random phase

Wherein δ is a fixed value, and S is the equally distributed stochastic variable that satisfies the independent same distribution condition at (π, π); Then add random phase and carry out the reconstructed image spatial domain H that the discrete fourier inversion obtains according to formula _ψ(x) be:

$H_{\mathrm{\&psi;}}\left(x\right)=\frac{1}{n^2}\underset{\mathrm{\&xi;}}{\mathrm{\&Sigma;}}\left|U\left(\mathrm{\&xi;}\right)\right|\&CenterDot;e^{\frac{2\mathrm{i\&pi;}}{n}<x,\mathrm{\&xi;}>+\mathrm{i\&psi;}\left(\mathrm{\&xi;}\right)}$

Step 4.3, obtain phase place correlation G:

$G=-{\log }_{10}\mathrm{\&Phi;}\left(\frac{\mathrm{\&mu;}-\mathrm{TV}\left(H_i\right)}{\mathrm{\&sigma;}}\right)$

Wherein, Φ is the distribution function of normal distribution, according to Determine; TV (H _i) be the i width of cloth image H of N width of cloth reconstructed image _iTotal variation, TV (H _i) be:

Be divergence operator, μ is TV (H _i) average, σ is TV (H _i) variance.

Step 5: whether judge the current phase place correlation that obtains greater than standard value, if, then current phase place correlation is made as standard phase place correlation, then execution in step six, if not, then directly enter step 6 and carry out; Described standard phase place correlation is initially 0.

Step 6: increase threshold value t take 0.01 as step-length, and whether judgment threshold t is greater than 1, if do not have, turning step 2 carries out, if it is the corresponding threshold value of standard phase place correlation that current threshold value then is set, this threshold value is exactly the optimal threshold that carries out image co-registration with the image in the sequence.Obtain just can adopting this optimal threshold that homotactic image is carried out image co-registration behind this optimal threshold.

Advantage and the good effect of the inventive method are:

(1) estimates fusion rule with respect to existing small echo region energy, the present invention utilizes the phase place correlation values detection, can automatically carry out threshold optimization to it, get optimal threshold, thereby so that the image in the same sequence can obtain better image syncretizing effect according to this optimal threshold;

(2) with respect to the image interfusion method that has employing experience definite threshold now, adopt the inventive method to determine to carry out again image co-registration behind the homotactic optimal threshold, be applicable to have the unmanned plane reconnaissance image of big data quantity, and can realize good syncretizing effect, every objective fusion mass evaluation index such as entropy, Y-PSNR (, Peak-to-peak Signal-to-Noise Ratio, be called for short PSNR), root-mean-square error (Root Mean Square Error, be called for short RMSE) etc. all be better than conventional images fusion method such as SiDWT (shift invariant DWT) fusion method, and the inventive method also is easy to realize.

Description of drawings

Fig. 1 is the overall flow synoptic diagram of the system of selection of threshold value of the present invention;

Fig. 2 is the schematic flow sheet that region energy is estimated fusion in the system of selection step 1 of threshold value of the present invention;

Fig. 3 is the schematic flow sheet of determining the phase place correlation in the system of selection step 3 of threshold value of the present invention;

Fig. 4 is for adopting the inventive method to obtain carrying out image syncretizing effect figure behind the optimal threshold: (a) for unmanned plane Visible Light Reconnaissance image, (b) being unmanned plane infrared reconnaissance image, (c) is fused images.

Embodiment

The present invention is further illustrated below in conjunction with accompanying drawing and implementation example.

The system of selection based on the unmanned plane Image Fusion Rule threshold value of wavelet transformation that the present invention proposes, unmanned plane Image Fusion Rule based on wavelet transformation, utilize the method for phase place correlation values detection, automatically carry out threshold optimization, allow to obtain better image and melt effect.As shown in Figure 1, the inventive method specifically comprises following 6 steps.

Step 1: region energy is estimated fusion, specifically comprises following step 1.1～step 1.4.

Step 1.1: read in two width of cloth unmanned plane source images A, B.

Step 1.2: through type (1) calculates respectively two width of cloth image A, the energy of regional area on B correspondence direction, the corresponding resolution

$E_j^{\mathrm{\&epsiv;}}(x,y)=\underset{n\&Element;L,m\&Element;K}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}^{\mathrm{\&epsiv;}}(n,m){\left[D_j^{\mathrm{\&epsiv;}}(x+n,y+m)\right]}^2,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(1\right)$

In the formula, j is the unmanned plane image resolution ratio, and ε is the direction subscript, ε=1,2, and 3 represent respectively level, vertical and three directions in diagonal angle;

For under the j resolution, on the ε direction, the energy of local area of position centered by (x, y);

For in the high fdrequency component under the j resolution, on the ε direction; ω ^ε(n, m) be with

Corresponding weight function; L, K are the size of regional area, and normally used range size is 3*3,5*5 or 7*7, and unit is pixel; M, the variation range of n is in L, K.Take certain type unmanned plane as example, the value that can select L, K all is 5 pixels.

Step 1.3: determine two width of cloth image A, the matching degree of regional area on B correspondence direction, the corresponding resolution

$M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)=\frac{2\underset{n\&Element;L,m\&Element;K}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}^{\mathrm{\&epsiv;}}(n,m)D_{j,A}^{\mathrm{\&epsiv;}}(x+n,y+m)D_{j,B}^{\mathrm{\&epsiv;}}(x+n,y+m)}{E_{j,A}^{\mathrm{\&epsiv;}}(x,y)+E_{j,B}^{\mathrm{\&epsiv;}}(x,y)}---\left(2\right)$

Wherein,

And

Obtain by formula (1),

Be respectively image A, B in the high fdrequency component under the j resolution, on the ε direction.

Step 1.4: predetermined threshold value t, general t gets 0.5～1, predetermined threshold value t=0.5

Step 2: determine to merge operator S ₁With S ₂:

If $M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)<t,$ Then

$\left\{\begin{array}{cc}{\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="HDA0000067088490000021.png"\; state="\{\{state\}\}">S</figure-callout>}}_1=1,S_2=0& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)\&GreaterEqual;E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\\ {\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="HDA0000067088490000021.png"\; state="\{\{state\}\}">S</figure-callout>}}_1=0,S_2=1& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)<E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(3\right)$

If $M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)\&GreaterEqual;t,$ Then

$\left\{\begin{array}{cc}{\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="HDA0000067088490000021.png"\; state="\{\{state\}\}">S</figure-callout>}}_1=W_{j,\max }^{\mathrm{\&epsiv;}}(x,y),S_2=W_{j,\min }^{\mathrm{\&epsiv;}}(x,y)& E_{j,A}^{\mathrm{\&epsiv;}}\left(x,y\right)\&GreaterEqual;E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\\ {\mathrm{<figure-callout\; id="1"\; label="S"\; filenames="HDA0000067088490000021.png"\; state="\{\{state\}\}">S</figure-callout>}}_1=W_{j,\min }^{\mathrm{\&epsiv;}}(x,y),S_2=W_{j,\max }^{\mathrm{\&epsiv;}}(x,y)& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)<E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(4\right)$

S in the formula ₁, S ₂Be respectively the weight of source images A and B in the image co-registration,

Be defined as:

$\left\{\begin{array}{c}{W}_{j,\min }^{\mathrm{\&epsiv;}}(x,y)=\frac{1}{2}-\frac{1}{2}\left[\frac{1-M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)}{1-t}\right]\\ W_{j,\max }^{\mathrm{\&epsiv;}}(x,y)=1-W_{j,\min }^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(5\right)$

Step 3: image co-registration; Detailed process is: step 3.1: two width of cloth source images are carried out wavelet transform, make up respectively small echo gold tower; Step 3.2: utilize the fusion operator that wavelet pyramid is merged, obtain fused images F; Step 3.3: fused images F is carried out the discrete wavelet inverse transformation.

Wherein, step 3.2 specifically utilizes the fusion operator that wavelet pyramid is merged according to formula (6);

$D_{j,F}^{\mathrm{\&epsiv;}}(x,y)=S_1{D}_{j,A}^{\mathrm{\&epsiv;}}(x,y)+S_2{D}_{j,B}^{\mathrm{\&epsiv;}}(x,y)---\left(6\right)$

In the formula,

The value of locating at (x, y) in the high fdrequency component under the j resolution, on the ε direction for the wavelet pyramid of image A;

Locate the value of pixel at (x, y) in the high fdrequency component under the j resolution, on the ε direction for the wavelet pyramid of image B;

Locate the value of pixel at (x, y) in the high fdrequency component under the j resolution, on the ε direction for fused images F.

Step 4: phase place correlation value calculation; Specifically comprise following three steps:

Step 4.1: discrete Fourier transformation conversion.

The discrete Fourier transformation of fused images F (DFT) obtains following frequency domain figure as U (ξ):

$U\left(\mathrm{\&xi;}\right)=\underset{x}{\mathrm{\&Sigma;}}F\left(x\right)e^{-\frac{2\mathrm{i\&pi;}}{n}<x,\mathrm{\&xi;}>}---\left(7\right)$

In the formula, n is the sampling point number in the fused images,＜x, ξ〉be the inner product of x and ξ, x is the independent variable of fused images F time domain, ξ is the independent variable of the U that obtains after discrete Fourier transformation of fused images F.So just, can obtain the frequency domain distribution after the discrete Fourier transformation of fused images F

Wherein | U (ξ) | be the amplitude after the fused images F discrete Fourier transformation,

It is the phase place after the fused images F discrete Fourier transformation;

Step 4.2: generate the N width of cloth image that adds random phase;

With phase place

Increase a random offset δ S, obtain a new phase function

ψ (ξ) is called random phase, and δ is a fixed value, and S is the equally distributed stochastic variable that satisfies the independent same distribution condition at (π, π).If the number of times with Monte Carlo simulation is N, then need phase place Do respectively N skew, and carry out the reconstructed image spatial domain H that inverse discrete Fourier transform obtains according to formula (8) _ψ(x) be:

$H_{\mathrm{\&psi;}}\left(x\right)=\frac{1}{n^2}\underset{\mathrm{\&xi;}}{\mathrm{\&Sigma;}}\left|U\left(\mathrm{\&xi;}\right)\right|\&CenterDot;e^{\frac{2\mathrm{i\&pi;}}{n}<x,\mathrm{\&xi;}>+\mathrm{i\&psi;}\left(\mathrm{\&xi;}\right)}---\left(8\right)$

Step 4.3: obtain the phase place correlation;

The i width of cloth image H of N width of cloth reconstructed image _iTotal variation (ROF model) TV (H _i) be:

$\mathrm{TV}\left(H_i\right)=\underset{x}{\mathrm{\&Sigma;}}|\&dtri;H_i\left(x\right)|---\left(9\right)$

In the formula

It is divergence operator.Obtain TV (H _i) after can further calculate TV (H _i) average μ and variances sigma, then obtain phase place correlation G according to formula (10):

$G=-{\log }_{10}\mathrm{\&Phi;}\left(\frac{\mathrm{\&mu;}-\mathrm{TV}\left(H_i\right)}{\mathrm{\&sigma;}}\right)---\left(10\right)$

Wherein Φ is the distribution function of normal distribution:

$\mathrm{\&phi;}\left(x\right)={\left(2\mathrm{\&pi;}\right)}^{-1/2}{\&Integral;}_x^{+\&infin;}{e}^{-t^2/2}\mathrm{dt}---\left(11\right)$

Step 5: whether judge the current phase place correlation that obtains greater than standard value, if, then current phase place correlation is made as standard phase place correlation, then execution in step six, if not, then directly enter step 6 and carry out.Described standard phase place correlation is initially 0.

Step 6: increase threshold value t take 0.01 as step-length, then whether judgment threshold t, turns step 2 and carries out if do not have greater than 1, if then the corresponding threshold value of standard phase place correlation this moment is best, it is the corresponding threshold value of standard phase place correlation that current threshold value is set.After obtaining optimal threshold, according to this thresholding to carrying out image co-registration with the image in the sequence.

The image sequence that the unmanned plane image is normally a large amount of, after utilizing the inventive method to obtain optimal threshold in the fusion process with the piece image in the sequence, this threshold value goes for the arbitrary image in the sequence, so get final product directly adopting this threshold value to merge with other images in the sequence, do not need to carry out again the selection of optimal threshold, the method that merges can be according to the present invention step 1.1～step 1.3 obtain the matching degree of image, then determine to merge operator according to step 2, then carry out image co-registration according to step 3; Also resulting optimal threshold can be applied in the image that comes these to be in same sequence in the present Wavelet Fusion method merges.

Under the application background of a large amount of unmanned plane image sequences, carry out again image co-registration behind employing the inventive method acquisition optimal threshold and carry out image co-registration with respect to adopting existing method rule of thumb to be worth, the image syncretizing effect of acquisition is more excellent.The below has provided and has adopted the inventive method and the existing methodical contrast of adopting empirical value.

The existing three kinds of image interfusion methods that carry out image co-registration with the employing empirical value that adopts the inventive method to do contrast in the embodiment of the invention are: LAP (Laplacian) pyramid fusion method, MORPH (morphological) pyramid fusion method and SiDWT fusion method.LAP pyramid fusion method is a kind of fusion method of utilizing pyramid decomposition, it is a kind of multiple dimensioned, multi-Resolution Image Fusion method, fusion process can be carried out respectively on different scale, different spatial resolutions, different decomposition layer, basic thought is: each width of cloth source images is carried out the LAP pyramid decomposition, then by selecting coefficient to consist of the fusion pyramid from the original image pyramid, will merge again pyramid and carry out inverse transformation and can obtain fused images.The basis of MORPH pyramid fusion method is the morphology sampling policy, the image point set at first carries out pre-service by morphology opening operation or closed operation, then set up image pyramid by the morphology sampling, at the tower layer certain feature selecting strategy is set and also caves in step by step, at last by morphology dual operations reconstructed image.The SiDWT fusion method is not by adopting down-sampled process can obtain having the wavelet transformation (SiDWT) of translation invariance.Merge threshold value in these three kinds of fusion methods and all choose the threshold value 0.75 of usually selecting.Adopt again the inventive method to obtain to obtain according to the invention described above step 1.1～step 1.3 again behind the optimal threshold matching degree of image, determine to merge operator according to step 2, then carry out image co-registration according to step 3.

As shown in table 1, for the inventive method and above-mentioned three kinds of existing methods are applied to respectively same sequence unmanned plane image is merged the fusion performance evaluation table that obtains, (PSNR) is higher for Y-PSNR, illustrate that syncretizing effect and quality are better, (RMSE) is less for root-mean-square error, illustrates that fused images and ideal image are more approaching, and syncretizing effect and quality are better, adopt as can be seen from Table 1 image co-registration result's the Y-PSNR of the inventive method the highest, root-mean-square error is minimum.

Table 1: use the fusion performance evaluation table that 4 kinds of methods are carried out the unmanned plane image co-registration

Fusion method	PSNR	RMSE
			LAP	42.193	3.9776
MORPH	37.6076	6.7436
			The SiDWT fusion method	41.7474	4.187
The inventive method	43.0585	3.6004

As shown in Figure 4, (a) be unmanned plane Visible Light Reconnaissance image, (b) be unmanned plane infrared reconnaissance image, (c) for will image shown in (a) with (b) shown in image adopt method of the present invention to merge the fused images that obtains.(c) can find out that employing the inventive method obtains to carry out image co-registration behind the optimal threshold again from Fig. 4, and the fused images effect of acquisition is relatively good.

Claims (3)

1. the system of selection based on the unmanned plane Image Fusion Rule threshold value of wavelet transformation is characterized in that, may further comprise the steps:

Step 1: region energy is estimated fusion, may further comprise the steps:

Step 1.1, read in unmanned plane source images A and B;

Step 1.2, determine respectively the energy of regional area on image A and image B correspondence direction, the corresponding resolution according to formula (1)

With

$E_j^{\mathrm{\&epsiv;}}(x,y)=\underset{n\&Element;L,m\&Element;K}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}^{\mathrm{\&epsiv;}}(n,m){\left[D_j^{\mathrm{\&epsiv;}}(x+n,y+m)\right]}^2,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(1\right)$

Wherein, j is the unmanned plane image resolution ratio; ε is the direction subscript, ε=1,2, and 3 represent respectively level, vertical and three directions in diagonal angle; For under the j resolution, on the ε direction, the energy of local area of position centered by coordinate points (x, y);

For in the high fdrequency component under the j resolution, on the ε direction; ω ^ε(n, m) be with Corresponding weight function; L, K are the size of regional area;

Step 1.3, determine the matching degree of regional area on image A and image B correspondence direction, the corresponding resolution

$M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)=\frac{2\underset{n\&Element;L,m\&Element;K}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}^{\mathrm{\&epsiv;}}(n,m)D_{j,A}^{\mathrm{\&epsiv;}}(x+n,y+m)D_{j,B}^{\mathrm{\&epsiv;}}(x+n,y+m)}{E_{j,A}^{\mathrm{\&epsiv;}}(x,y)+E_{j,B}^{\mathrm{\&epsiv;}}(x,y)}---\left(2\right)$

Wherein,

With Be respectively image A and image B in the high fdrequency component under the j resolution, on the ε direction;

Step 1.4, predetermined threshold value t;

Step 2: determine to merge operator S according to threshold value t ₁With S ₂:

If

Then

$\left\{\begin{array}{cc}{S}_1=1,S_2=0& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)\&GreaterEqual;E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\\ S_1=0,S_2=1& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)<E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(3\right)$

If

Then

$\left\{\begin{array}{cc}{S}_1=W_{j,\max }^{\mathrm{\&epsiv;}}(x,y),S_2=W_{j,\min }^{\mathrm{\&epsiv;}}(x,y)& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)\&GreaterEqual;E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\\ S_1=W_{j,\min }^{\mathrm{\&epsiv;}}(x,y),S_2=W_{j,\max }^{\mathrm{\&epsiv;}}(x,y)& E_{j,A}^{\mathrm{\&epsiv;}}(x,y)<E_{j,B}^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(4\right)$

In the formula (4)

Be defined as:

$\left\{\begin{array}{c}{W}_{j,\min }^{\mathrm{\&epsiv;}}(x,y)=\frac{1}{2}-\frac{1}{2}\left[\frac{1-M_{j,\mathrm{AB}}^{\mathrm{\&epsiv;}}(x,y)}{1-t}\right]\\ W_{j,\max }^{\mathrm{\&epsiv;}}(x,y)=1-W_{j,\min }^{\mathrm{\&epsiv;}}(x,y)\end{array}\right.,\mathrm{\&epsiv;}=\mathrm{1,2,3}---\left(5\right)$

Step 3: image co-registration specifically may further comprise the steps:

Step 3.1, two width of cloth source images are carried out wavelet transform, make up small echo gold tower;

Step 3.2, utilization are merged operator the wavelet pyramid that obtains are merged;

$D_{j,F}^{\mathrm{\&epsiv;}}(x,y)=S_1{D}_{j,A}^{\mathrm{\&epsiv;}}(x,y)+S_2{D}_{j,B}^{\mathrm{\&epsiv;}}(x,y)---\left(6\right)$

Wherein,

Locate the value of pixel at (x, y) at the component under the j resolution, on the ε direction for the fused images F that obtains after merging;

Step 3.3, fused images F is carried out the discrete wavelet inverse transformation;

Step 4: determine the phase place correlation, specifically may further comprise the steps:

Step 4.1, fused images F done discrete Fourier transformation obtain frequency domain figure as U (ξ):

$U\left(\mathrm{\&xi;}\right)=\underset{x}{\mathrm{\&Sigma;}}F\left(x\right)e^{-\frac{2\mathrm{i\&pi;}}{n}<x,\mathrm{\&xi;}>}---\left(7\right)$

Wherein, n is the sampling point number in the fused images,＜x, and ξ＞be the inner product of x and ξ, x is the independent variable of fused images F time domain, ξ is the independent variable of frequency domain figure picture;

Frequency domain distribution after the fused images F discrete Fourier transformation is: Wherein | U (ξ) | be the amplitude of fused images F discrete Fourier transformation,

It is the phase place of fused images F discrete Fourier transformation;

Step 4.2, generation add the N width of cloth image of random phase, and N is the number of times of Monte Carlo simulation:

Each time emulation is all with phase place

Increase a random offset δ S, obtain a random phase

Wherein δ is a fixed value, and S is the equally distributed stochastic variable that satisfies the independent same distribution condition at (π, π); Then add random phase and carry out the reconstructed image spatial domain H that inverse discrete Fourier transform obtains _ψ(x) be:

$H_{\mathrm{\&psi;}}\left(x\right)=\frac{1}{n^2}\underset{\mathrm{\&xi;}}{\mathrm{\&Sigma;}}\left|U\left(\mathrm{\&xi;}\right)\right|\&CenterDot;e^{\frac{2\mathrm{i\&pi;}}{n}<x,\mathrm{\&xi;}>+\mathrm{i\&psi;}\left(\mathrm{\&xi;}\right)}---\left(8\right)$

Step 4.3, obtain phase place correlation G:

$G=-{\log }_{10}\mathrm{\&Phi;}\left(\frac{\mathrm{\&mu;}-\mathrm{TV}\left(H_i\right)}{\mathrm{\&sigma;}}\right)---\left(9\right)$

Wherein, φ is the distribution function of normal distribution, according to

Determine; TV (H _i) be the i width of cloth image H of N width of cloth reconstructed image _iTotal variation, TV (H _i) be:

Be divergence operator, μ is TV (H _i) average, σ is TV (H _i) variance;

Step 5: whether the current phase place correlation that judge to obtain greater than standard value, if, then current phase place correlation is made as standard phase place correlation, then execution in step six, if not, direct execution in step six then; Described standard phase place correlation is initially 0;

Step 6: increase threshold value t take 0.01 as step-length, and whether judgment threshold t is greater than 1, if do not have, execution in step two, if then the corresponding threshold value of standard phase place correlation is exactly optimal threshold, then adopt this optimal threshold to carrying out image co-registration with the image in the sequence.

2. the system of selection of a kind of unmanned plane Image Fusion Rule threshold value based on wavelet transformation according to claim 1 is characterized in that the big or small L of the described regional area of step 1.2 and K are 5 pixels.

3. the system of selection of a kind of unmanned plane Image Fusion Rule threshold value based on wavelet transformation according to claim 1 is characterized in that the described threshold value t of step 1.4 gets 0.5 ~ 1.

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