CN103679670B - A kind of PCNN multisource image anastomosing method based on improved model - Google Patents
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Abstract
The present invention relates to a kind of PCNN multisource image anastomosing method based on improved model.Have in improvement: in PCNN, the feed back input of each neuron only receives outside stimulus input;In link field, the value of each parameter is the most identical to all neurons;In variable threshold value function, the value of each parameter is the most identical to all neurons;Introduce threshold value look-up table and index map, threshold value look-up table have recorded the threshold value corresponding with network operation number of times, and these threshold values can precalculate before the network operation and obtain, it is to avoid the exponent arithmetic in the network operation, accelerates the operation of network.Index map have recorded the duration of ignition of whole pixel, is the integrated results of the similar pixel that space is adjacent in input picture, and embodiment is the overall visual signature of input picture.Invention introduces the threshold value look-up table that the index map of the duration of ignition recording whole pixel is corresponding with network operation number of times with record, have employed fusion rule based on index map, achieve than traditional more preferable effect of wavelet transform fusion.
Description
Technical field
The present invention relates to a kind of PCNN multisource image anastomosing method based on improved model, particularly relate to
A kind of be suitable for that visible ray, medium wave and three wave bands of LONG WAVE INFRARED merge simultaneously based on improved model
PCNN multisource image anastomosing method.
Background technology
Artificial neural network is at a kind of novel calculating attempting imitation biological nervous system information processing manner
Reason model.One neutral net is made up of Multilevel method unit or node, and various method can be used to carry out
Interconnection.Some scholar is own carries out multi-source image fusion through application artificial neural network.At present, nerve net
Network application in image co-registration mainly has: bimodal neuroid (Bimodal Neurons), multilayer
Perceptron (Multi-layered Perceptron) and Pulse Coupled Neural Network (Pulse-coupled Neural
Network, PCNN) etc..Wherein PCNN is a kind of new neural network proposed in recent years, international
Upper referred to as third generation artificial neural network.
1981, E.A.Newman, P.H.Hartline etc. proposed 6 kinds different types of pair
Mode neuron (include AND, OR, Visible-Enhanced Infrared,
Visible-Suppressed-Infrared, Infrared-Enhanced-Visible and
Infrared-Suppressed-Visible) for visible ray and the fusion of infrared image.Nineteen ninety-five, Fechner
Image interfusion method based on multilayer perceptron neutral net is proposed with Godlewski.Many by training
Pixel interested in layer perceptron identification prebiotic synthesis, is incorporated in visible images.From 20
The nineties in century, existing to the visual cortex nerve impulse string synchronized oscillation of cat, monkey by Eckhorn etc.
The research of elephant, has obtained mammalian nervous meta-model, and thus development has defined pulse coupled neural net
Network model.This model has the advantages that to be grouped the pixel that two-dimensional image spatial similarity, gray scale are similar,
And image local gray scale difference value can be reduced, make up the small interruption of image local.1999, Brocssard
Etc. R.P. the spark rate of PCNN neuron is demonstrated with the relation of gradation of image it was confirmed PCNN
Feasibility for image co-registration.Based on this model, relevant scholar proposes various improved model, and will
It is for the fusion of various images.
At present, the image interfusion method of Based PC NN is studied and is mainly concentrated the following aspects:
The automation of network parameter is chosen: the parameter related to due to PCNN network is more, and different ginseng
Numerical value all can affect final result.Automatically the key in PCNN network is calculated by householder method
Parameter, can obtain more preferable result.
Improvement to PCNN basic model: according to realizing function, process object and the mode of thinking are not
With, different researchers successively propose different improved models.
Therefore a kind of novel PCNN multisource image anastomosing method based on improved model of offer is provided badly.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of raising multi-source image syncretizing effect, after making fusion
Image object feature becomes apparent from, is more beneficial for the PCNN multi-source image based on improved model of target identification
Fusion method.
For solving above-mentioned technical problem, a kind of PCNN multi-source image based on improved model of the present invention merges
Method, comprises the following steps successively:
Step one, to input three width original image A, B and C spatially carry out pixel level registration,
Ensure that three width image sizes are X × Y;
Step 2, setting network parameter W, VL, β, Vθ, αθValue with Δ t;
VLAnd VθIt is respectively Lij[n] and θijIntrinsic electromotive force in [n], θij[n] is dynamic threshold, Lij[n] is
Linearly connected inputs;
αθFor θijThe damping time constant of [n];Δ t is time sampling interval;β is bonding strength between cynapse
Constant;Yij[n] is PCNN pulse output;Ykl[n-1] is PCNN last time pulse output;Inside connects
Meet the w in matrix WijklCorresponding LijY in [n]klThe weight coefficient of [n-1];
N is the number of run of network, n=1,2 ..., N-1, N, N are maximum number of run;
Step 3, in every width input picture search Sij_max、Sij_min;Sij_max< Vθ, Sij_min> 0;
Step 4, obtaining network maximum number of run N and threshold value look-up table LT (s), s is the letter of LT (s)
Number variable;
In formula: t1And t2It is respectively gray value maximum pixel and the autogenous ignition time of minimum pixel in image;
Step 5, utilize following equation moving model;
Fij[n]=Sij
Lij[n]=VL∑wijklYkl[n-1]
Uij[n]=Fij[n](1+βLij[n])
Iij[n]=N-n
In formula: Uij[n] is internal activity item, Yij[n] is PCNN pulse output, Iij[n] is index value;
As n=1, Lij[1]=0, then Uij[1]=Fij[1]=Sij, θij[1]=LT(N-1)=Sij_max, corresponding
Feed back input intermediate value is Sij_maxNeuron by autogenous ignition;After neuron firing, export Yij[1]=1,
θij[2] V is becomeθ, the index value of igniting neuron is labeled as Iij=N-1;
By that analogy, when the network operation to n=N, threshold θij[N]=LT(0)=Sij_min, defeated for feedback
Enter for Sij_minNeuron autogenous ignition, igniting neuron index value be labeled as Iij=0;
Step 6, respectively obtain the index map I of three width original image A, B and CA、IBAnd IC;
Work as IA、IBAnd ICThe absolute value of the mutual difference of index value of respective pixel is both less than equal to representative value
E, then the pixel value of fused images takes the weighted average of three width image respective pixel;
Work as IA、IBAnd ICThe absolute value of the mutual difference of index value of respective pixel has more than representative value e's
During situation, if wherein the mutual difference of index value of two width image respective pixel is less than or equal to representative value e, then
The pixel value of fused images takes the weighted average of above-mentioned two width image respective pixel;
In the case of other, then the pixel value of fused images takes the pixel value that index value is bigger.
In step 2, it is ensured that the feed back input F of dendronij[n] only receives outside input stimulus signal Sij;Ensure
W、VL、β、Vθ、αθThe most identical to all neurons with the value of Δ t.
e=2。
Three width original image A, B and C are respectively visible ray, medium-wave infrared and LONG WAVE INFRARED image.
Basic model, on the basis of analyzing PCNN image interfusion method, is simplified by the present invention accordingly
And improvement, obtain the PCNN image interfusion method of a kind of new improvement.Have in wherein simplifying and improving:
1. in PCNN, the feed back input of each neuron only receives outside stimulus input;2. each parameter in link field
Value the most identical to all neurons;3. in variable threshold value function, the value of each parameter is to all neurons all
Identical;4. introducing threshold value look-up table and index map, threshold value look-up table have recorded corresponding with network operation number of times
Threshold value, these threshold values can precalculate before the network operation and obtain, it is to avoid the finger in the network operation
Number computing, accelerates the operation of network.Index map have recorded the duration of ignition of whole pixel, is input figure
The integrated results of the similar pixel that space is adjacent in Xiang, embodiment is the overall visual signature of input picture.
The present invention introduce in improved model the duration of ignition recording whole pixel index map and record with
The threshold value look-up table that network operation number of times is corresponding, under the conditions of identical fusion rule, becomes than traditional small echo
Change fusion method and have more preferable effect.
The indices of the present invention is better than the indices of the former fusion rule of WT+ to a certain extent, especially
It is the index such as average and standard variance, improves clearly, illustrate the validity of improved method.
Accompanying drawing explanation
Fig. 1 is the PCNN fusion method schematic diagram of three width images.
Fig. 2 is certain seashore original image and fusion results of Based PC NN.
Detailed description of the invention
The present invention is further detailed explanation with embodiment below in conjunction with the accompanying drawings.
The basic thought of the present invention is: two width or several original images to input are respectively adopted PCNN mould
Type is calculated the index map of correspondence, then uses index map and original image and merges decision-making accordingly,
Finally give fused images.The PCNN fusion method schematic diagram of three width images is as shown in Figure 1.
Specifically, the present invention comprises the following steps successively:
Step one, to input three width original image A, B and C spatially carry out pixel level registration,
Ensure that three width image sizes are X × Y;Three width original image A, B and C be respectively visible ray, in
Ripple is infrared and LONG WAVE INFRARED image;
Step 2, setting network parameter W, VL, β, Vθ, αθValue with Δ t;Ensure the feedback of dendron
Input Fij[n] only receives outside input stimulus signal Sij;Ensure each parameter W, V in link fieldL, the taking of β
It is worth the most identical to all neurons;Ensure each parameter V in variable threshold value functionθ、αθWith the value of Δ t to all
Neuron is the most identical;
VLAnd VθIt is respectively Lij[n] and θijIntrinsic electromotive force (amplification coefficient) in [n], θij[n] is dynamic threshold
Value, Lij[n] is linearly connected input;
αθFor θijThe damping time constant of [n];Δ t is time sampling interval;β is bonding strength between cynapse
Constant;Yij[n] is PCNN pulse output;Ykl[n-1] is PCNN last time pulse output;Inside connects
Meet the w in matrix WijklCorresponding LijY in [n]klThe weight coefficient of [n-1];
N is the number of run of network, n=1,2 ..., N-1, N, N are maximum number of run;
Step 3, in every width input picture search Sij_max、Sij_min;Vθ> Sij_max;If Sij_minLittle
In equal to 0, by Serial regulation Sij_max、Sij_minAnd input picture, make Sij_minMore than 0;
Step 4, obtaining network maximum number of run N and threshold value look-up table LT (s), s is the letter of LT (s)
Number variable;
In formula: t1And t2It is respectively gray value maximum pixel and the autogenous ignition time of minimum pixel in image;
Step 5, utilize following equation moving model;
Fij[n]=Sij
Lij[n]=VL∑wijklYkl[n-1]
Uij[n]=Fij[n](1+βLij[n])
Iij[n]=N-n
In formula: Uij[n] is internal activity item, Yij[n] is PCNN pulse output, Iij[n] is index value;
As n=1, Lij[1]=0, then Uij[1]=Fij[1]=Sij, θij[1]=LT(N-1)=Sij_max, corresponding
Feed back input intermediate value is Sij_maxNeuron by autogenous ignition;After neuron firing, export Yij[1]=1,
θij[2] V is becomeθSo that igniting neuron will not be the most ignited.Meanwhile, the rope of igniting neuron
Draw value and be labeled as Iij=N-1, no longer changes.Along with the increase of the number of run n of network, threshold θij[n] by
The least, the neuron of igniting will encourage adjacent neuron by link field.When the network operation to n=N
Time, threshold θij[N]=LT(0)=Sij_min, it is S for feed back inputij_minNeuron, even if there is no phase
In the case of adjacent neuron excitation, also will autogenous ignition, the index value of igniting neuron is labeled as Iij=0;
I.e. by the network operation of most n times, all of neuron will ignited and also only once.
If it can be seen that neuron SijLight a fire when n-th is run, then its index map Iij[n] is solid
It is set to N-n, no longer changes along with the operation of network.Index map Iij[n] have recorded all neurons
The duration of ignition, is the result of input picture space-time integration.Additionally, due to the variable threshold value in the network operation is
Obtained by look-up table mode, it is not necessary to complicated exponent arithmetic, shorten the time of the network operation.Net
Network runs the automatic acquisition of maximum times, not only ensure that the certainty of the network operation, and makes each
Neuron firing is only once;
Step 6, use corresponding convergence strategy, obtain fused images.The convergence strategy that this method uses
As follows: owing to the index map of every width original image represents the overall visual signature of original image, therefore to lead to
The index map crossing three width original images carrys out synthetic setting convergence strategy.
Obtain the index map I of three width original image A, B and CA、IBAnd IC;
Work as IA、IBAnd ICThe absolute value of the mutual difference of index value of respective pixel is both less than equal to representative value
E, then the pixel value of fused images takes the weighted average of three width image respective pixel;
Work as IA、IBAnd ICThe absolute value of the mutual difference of index value of respective pixel has more than representative value e's
During situation, if wherein the mutual difference of index value of two width image respective pixel is less than or equal to representative value e, then
The pixel value of fused images takes the weighted average of above-mentioned two width image respective pixel;
In the case of other, then the pixel value of fused images takes the pixel value that index value is bigger.
Preferably e=2.
In order to verify the performance based on improvement PCNN model image fusion method of proposition, select certain sea
The three width visible rays, LONG WAVE INFRARED and the medium-wave infrared figure that are registered of bank (image size is 320 × 256)
As image to be fused.The (a) and (b) of Fig. 2 and (c) are respectively the to be fused original of certain seashore can
Seeing light, LONG WAVE INFRARED and medium-wave infrared image, (d) of Fig. 2 is to use 4 layers of wavelet transformation (WT),
Low frequency coefficient is averaged, and high frequency coefficient uses the fusion results that the fusion rule of region energy operator obtains;
(e) of Fig. 2 is the fusion results using and improving PCNN model.In order to preferably fusion results be entered
Row com-parison and analysis, have employed the product of average, standard deviation, entropy and structural information and transinformation contentObjective evaluation criteria calculated, the result obtained is as shown in table 1.It can be seen that
Based on improve PCNN model fusion results local detail, profile (personage on seashore limit) and
Fusion results based on the former fusion rule of WT+ it is better than on overall brightness.From the three of objective evaluation tables
It can be seen that be better than the former fusion of WT+ to a certain extent based on the indices improving PCNN model
The indices of rule, the particularly index such as average and standard variance, improves clearly, illustrates to change
Enter the validity of method.
Certain seashore fusion results objective evaluation of table 1 Based PC NN
Claims (4)
1. a PCNN multisource image anastomosing method based on improved model, comprises the following steps successively:
Step one, to input three width original image A, B and C spatially carry out pixel level registration,
Ensure that three width image sizes are X × Y;
Step 2, setting network parameter W, VL, β, Vθ, αθValue with Δ t;
VLAnd VθIt is respectively Lij[n] and θijIntrinsic electromotive force in [n], θij[n] is dynamic threshold, Lij[n] is
Linearly connected inputs;
αθFor θijThe damping time constant of [n];Δ t is time sampling interval;β is bonding strength between cynapse
Constant;Yij[n] is PCNN pulse output;Ykl[n-1] is PCNN last time pulse output;Inside connects
Meet the w in matrix WijklCorresponding LijY in [n]klThe weight coefficient of [n-1];
N is the number of run of network, n=1,2 ..., N-1, N, N are maximum number of run;
Step 3, in every width input picture search Sij_max、Sij_min;Sij_max< Vθ, Sij_min> 0;
Step 4, obtaining network maximum number of run N and threshold value look-up table LT (s), s is the letter of LT (s)
Number variable;
In formula: t1And t2It is respectively gray value maximum pixel and the autogenous ignition time of minimum pixel in image;
Step 5, utilize following equation moving model;
Fij[n]=Sij
Lij[n]=VL∑wijklYkl[n-1]
Uij[n]=Fij[n](1+βLij[n])
Iij[n]=N-n
In formula: Uij[n] is internal activity item, Yij[n] is PCNN pulse output, Iij[n] is index value;
As n=1, Lij[1]=0, then Uij[1]=Fij[1]=Sij, θij[1]=LT(N-1)=Sij_max, corresponding
Feed back input intermediate value is Sij_maxNeuron by autogenous ignition;After neuron firing, export Yij[1]=1,
θij[2] V is becomeθ, the index value of igniting neuron is labeled as Iij=N-1;
By that analogy, when the network operation to n=N, threshold θij[N]=LT(0)=Sij_min, defeated for feedback
Enter for Sij_minNeuron autogenous ignition, igniting neuron index value be labeled as Iij=0;
Step 6, respectively obtain the index map I of three width original image A, B and CA、IBAnd IC;
Work as IA、IBAnd ICThe absolute value of the mutual difference of index value of respective pixel is both less than equal to representative value
E, then the pixel value of fused images takes the weighted average of three width image respective pixel;
Work as IA、IBAnd ICThe absolute value of the mutual difference of index value of respective pixel has more than representative value e's
During situation, if wherein the mutual difference of index value of two width image respective pixel is less than or equal to representative value e, then
The pixel value of fused images takes the weighted average of above-mentioned two width image respective pixel;
In the case of other, then the pixel value of fused images takes the pixel value that index value is bigger.
A kind of PCNN multisource image anastomosing method based on improved model the most according to claim 1,
It is characterized in that: in described step 2, it is ensured that the feed back input F of dendronij[n] only receives outside input stimulus
Signal Sij;Ensure W, VL、β、Vθ、αθThe most identical to all neurons with the value of Δ t.
A kind of PCNN multisource image anastomosing method based on improved model the most according to claim 1,
It is characterized in that: e=2.
A kind of PCNN multisource image anastomosing method based on improved model the most according to claim 1,
It is characterized in that: three width original image A, B and C are respectively visible ray, medium-wave infrared and LONG WAVE INFRARED
Image.
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CN104463821A (en) * | 2014-11-28 | 2015-03-25 | 中国航空无线电电子研究所 | Method for fusing infrared image and visible light image |
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CN113228046B (en) | 2018-12-27 | 2024-03-05 | 浙江大华技术股份有限公司 | System and method for image fusion |
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