CN107451984A - A kind of infrared and visual image fusion algorithm based on mixing multiscale analysis - Google Patents

A kind of infrared and visual image fusion algorithm based on mixing multiscale analysis Download PDF

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CN107451984A
CN107451984A CN201710621620.0A CN201710621620A CN107451984A CN 107451984 A CN107451984 A CN 107451984A CN 201710621620 A CN201710621620 A CN 201710621620A CN 107451984 A CN107451984 A CN 107451984A
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fusion
image
infrared
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CN107451984B (en
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江泽涛
吴辉
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Guilin University of Electronic Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10052—Images from lightfield camera
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20064—Wavelet transform [DWT]

Abstract

The invention discloses a kind of infrared and visual image fusion algorithm based on mixing multiscale analysis, comprise the following steps：Step 1：NSCT decomposition is carried out with visible images to infrared, obtains low frequency sub-band and high-frequency sub-band；Step 2：Stationary wavelet transform is used to low frequency sub-band, obtains a low frequency sub-band and three high-frequency sub-bands, local energy is respectively adopted and takes big be combined to be merged with compressive sensing theory to low, high-frequency sub-band with absolute value；Step 3：Judge the definition of image to be fused, the LSCN enhancing number of plies is chosen according to decision rule；Step 4：Big fusion rule is taken using absolute value to top high-frequency sub-band, remaining subband is merged using PCNN models are improved；Step 5：NSCT inverse transformations are carried out to fusion results, obtain final fused images.Fused images edge that the present invention obtains protrudes, and contrast is high, and target protrudes, and the index such as the average gradient of algorithm, spatial frequency is above prior art.

Description

A kind of infrared and visual image fusion algorithm based on mixing multiscale analysis
Technical field
The invention belongs to technical field of image processing, and in particular to it is a kind of based on mixing multiscale analysis it is infrared with it is visible Light image blending algorithm.
Background technology
Image interfusion method based on wavelet transformation is a kind of classical blending algorithm, but small echo can only represent isotropism Object, for features such as image center line, edges, small echo is not a kind of preferable representational tool.Contourlet melts in image Application in conjunction is relatively broad, by can be good at capturing to the multiple dimensioned of image, multi-direction decomposition, Contourlet Minutia in image, it compensate for the deficiency of small echo this respect.But due to employing down-sampled behaviour in contourlet transformation Make, it is not possessed translation invariance, Pseudo-Gibbs artifacts are easily produced in image procossing.
Nonsubsampled contourlet transform (the nonsubsampled Contourlet of the propositions such as A.L.Cunha Transform, NSCT) possess translation invariance, it can fully retain the effective information of image, produce preferably fusion effect Fruit, but also there is low frequency part image sparse is poor, the problems such as being unfavorable for feature extraction.
The content of the invention
In view of the shortcomings of the prior art, problem solved by the invention is that how to solve in infrared and visual image fusion Existing contrast is not high, and marginal information retains the problems such as not abundant enough.
In order to solve the above technical problems, the technical solution adopted by the present invention be it is a kind of based on mixing multiscale analysis it is red Outside with visual image fusion algorithm, comprise the following steps：
Step 1：NSCT decomposition is carried out respectively with visible images to infrared, obtains low frequency sub-band LJ(x, y) and high frequency Band Hj,r(x, y), wherein J are Decomposition order, and j, r represent decomposition scale and direction number.
Step 2：Stationary wavelet transform is used to low frequency sub-band, obtains a low frequency sub-band and three high-frequency sub-bands, respectively Take big be combined to be merged with compressive sensing theory to low, high-frequency sub-band with absolute value using local energy, then carry out small echo Inverse transformation obtains the low frequency sub-band of NSCT reconstruct.
It is described to take big be combined to be merged with compressive sensing theory to low frequency sub-band with absolute value using local energy, its Specific method is as follows：
EN is energy of local area in formula, and it is defined as：
It is described to take big be combined to be merged with compressive sensing theory to high-frequency sub-band with absolute value using local energy, its Comprise the following steps that：
1) high-frequency sub-band images by size for m × nWithResolve into non-overlapping copies and size phase Same sub-block, wherein j=1,2,3, rarefaction is carried out to each piece of sub-image using sym8 wavelet basis；
2) calculation matrix Φ is designed, sampling is measured to the high-frequency sub-band coefficient of input using calculation matrix, surveyed Amount vectorWithWherein k=1,2 ..., m × n；
3) measurement vector is calculatedWithStandard deviation SDkWith definition EAVk, using based on regional standard is poor, region is clear The fusion rule that clear degree and S function are combined, the measurement vector merged, i.e.,：
Graphics standard difference formula is：
Wherein
Image definition formula is：
Weight coefficient ω is obtained by S function, the S function used for：
Wherein,
F is the contraction factor of S function, and f is more than or equal to 1, takes f=5；
4) to the measurement vector of fusionSparse reconstruction is carried out, algorithm for reconstructing uses OMP, so as to obtain the height of fused images Frequency subband
The SW that will be obtainedFWithStationary wavelet is carried out to reconstruct to obtain the low frequency sub-band eventually for NSCT reconstruct.
Step 3：Judge the definition of image to be fused, the LSCN enhancing number of plies is chosen according to decision rule, its specific side Method is as follows：
Image definition formula is：
Image definition is calculated and by it compared with threshold value λ according to formula (8), high frequency coefficient enhancing is determined according to comparative result The number of plies, i.e.,：
Wherein J is Decomposition order, and S is the synthesis definition of source images, takes α1=α2=0.5, λ=27.
Step 4：Big fusion rule is taken using absolute value to top high-frequency sub-band, remaining subband is using improvement PCNN Model is merged, and specific fusion rule is as follows：
It is improved to remaining subband in addition to top high-frequency sub-band n, use in order to improve the vision perception of image PCNN models are merged, and determine fusion coefficients by comparing the igniting amplitude sum of PCNN neurons, i.e.,：
Wherein Mij(n) for PCNN output pulse firing amplitude summation, j=1,2 ..., n-1, ε be self-defined threshold value, take ε=0.002.
Because traditional PCNN output uses hard-limiting function, it is impossible to reflect the amplitude difference of neuron firing, this hair It is bright using output of the Sigmoid functions as PCNN, can preferably portray difference when lock-out pulse excites in igniting amplitude Different, PCNN output is defined as follows：
In order to preferably represent the marginal information of image, from improved Laplce's energy (SML) and local space frequency Outside input and link coefficient of the rate respectively as PCNN.SML is defined as follows：
Spatial frequency is：
Wherein RF, CF, MDF and SDF represent line frequency, row frequency, main diagonal frequencies and secondary diagonal frequencies, its formula respectively It is as follows：
Step 5：Obtained low frequency sub-band will be merged and high-frequency sub-band carries out NSCT inverse transformations, obtain final fusion figure Picture.
The fused images edge obtained using technical scheme is protruded, and contrast and brightness are higher, and target protrudes, The method that average gradient, spatial frequency, standard deviation and the comentropy of algorithm are above prior art, both effectively remained infrared Target, and can effectively obtain the spatial-domain information of source images, have obtained preferable syncretizing effect.
Brief description of the drawings
Fig. 1 is flow chart of the present invention；
Fig. 2 is the source infrared image of embodiment one；
Fig. 3 is the source visible images of embodiment one；
Fig. 4 is the fused images that the document 6 of embodiment one obtains；
Fig. 5 is the fused images that the document 8 of embodiment one obtains；
Fig. 6 is the fused images that the document 12 of embodiment one obtains；
Fig. 7 is the fused images that the inventive algorithm of embodiment one obtains；
Fig. 8 is the source infrared image of embodiment two；
Fig. 9 is the source visible images of embodiment two；
Figure 10 is the fused images that the document 6 of embodiment two obtains；
Figure 11 is the fused images that the document 8 of embodiment two obtains；
Figure 12 is the fused images that the document 12 of embodiment two obtains；
Figure 13 is the fused images that the inventive algorithm of embodiment two obtains.
Embodiment
The embodiment of the present invention is further described with reference to the accompanying drawings and examples, but is not to this hair Bright restriction.
Fig. 1 shows flow of the present invention, a kind of infrared and visual image fusion algorithm based on mixing multiscale analysis, Comprise the following steps：
Step 1：NSCT decomposition is carried out respectively with visible images to infrared, obtains low frequency sub-band LJ(x, y) and high frequency Band Hj,r(x, y), wherein J are Decomposition order, and j, r represent decomposition scale and direction number.
Step 2：Stationary wavelet transform is used to low frequency sub-band, obtains a low frequency sub-band and three high-frequency sub-bands, respectively Take big be combined to be merged with compressive sensing theory to low, high-frequency sub-band with absolute value using local energy, then carry out small echo Inverse transformation obtains the low frequency sub-band of NSCT reconstruct.
It is described to take big be combined to be merged with compressive sensing theory to low frequency sub-band with absolute value using local energy, its Specific method is as follows：
EN is energy of local area in formula, and it is defined as：
It is described to take big be combined to be merged with compressive sensing theory to high-frequency sub-band with absolute value using local energy, its Comprise the following steps that：
1) high-frequency sub-band images by size for m × nWithResolve into non-overlapping copies and size phase Same sub-block, wherein j=1,2,3, rarefaction is carried out to each piece of sub-image using sym8 wavelet basis；
2) calculation matrix Φ is designed, sampling is measured to the high-frequency sub-band coefficient of input using calculation matrix, surveyed Amount vectorWithWherein k=1,2 ..., m × n；
3) measurement vector is calculatedWithStandard deviation SDkWith definition EAVk, using based on regional standard is poor, region is clear The fusion rule that clear degree and S function are combined, the measurement vector merged, i.e.,：
Graphics standard difference formula is：
Wherein
Image definition formula is：
Weight coefficient ω is obtained by S function, the S function used for：
Wherein,
F is the contraction factor of S function, and f is more than or equal to 1, takes f=5；
4) to the measurement vector of fusionSparse reconstruction is carried out, algorithm for reconstructing uses OMP, so as to obtain the height of fused images Frequency subband
The SW that will be obtainedFWithStationary wavelet is carried out to reconstruct to obtain the low frequency sub-band eventually for NSCT reconstruct.
Step 3：Judge the definition of image to be fused, the LSCN enhancing number of plies is chosen according to decision rule, its specific side Method is as follows：
Image definition formula is：
Image definition is calculated and by it compared with threshold value λ according to formula (8), high frequency coefficient enhancing is determined according to comparative result The number of plies, i.e.,：
Wherein J is Decomposition order, and S is the synthesis definition of source images, takes α1=α2=0.5, λ=27.
Step 4：Big fusion rule is taken using absolute value to top high-frequency sub-band, remaining subband is using improvement PCNN Model is merged, and specific fusion rule is as follows：
It is improved to remaining subband in addition to top high-frequency sub-band n, use in order to improve the vision perception of image PCNN models are merged, and determine fusion coefficients by comparing the igniting amplitude sum of PCNN neurons, i.e.,：
Wherein Mij(n) for PCNN output pulse firing amplitude summation, j=1,2 ..., n-1, ε be self-defined threshold value, take ε=0.002.
Because traditional PCNN output uses hard-limiting function, it is impossible to reflect the amplitude difference of neuron firing, this hair It is bright using output of the Sigmoid functions as PCNN, can preferably portray difference when lock-out pulse excites in igniting amplitude Different, PCNN output is defined as follows：
In order to preferably represent the marginal information of image, from improved Laplce's energy (SML) and local space frequency Outside input and link coefficient of the rate respectively as PCNN.SML is defined as follows：
Spatial frequency is：
Wherein RF, CF, MDF and SDF represent line frequency, row frequency, main diagonal frequencies and secondary diagonal frequencies, its formula respectively It is as follows：
Step 5：Obtained low frequency sub-band will be merged and high-frequency sub-band carries out NSCT inverse transformations, obtain final fusion figure Picture.
Infrared image have recorded the infra-red radiation information of target object, under low-light (level) or camouflage target have it is stronger Recognition capability, but it changes not enough sensitivity to brightness.Visible images are larger by illumination effect, can provide target scene Detailed information.Therefore merged infrared with visible images, respective advantage can be combined, obtain a width clear background, The complementary image that target protrudes, so as to which the person of facilitating look at carries out more accurate, comprehensive description to the scene.
First, the experimental data of second embodiment is as follows：
Fig. 2 be first embodiment source infrared image, Fig. 3 be first embodiment source visible images, Fig. 4 data For the document 6 of table 1, Fig. 5 data are the document 8 of table 1, and Fig. 6 data are the document 12 of table 1, and Fig. 7 data are the sheet of table 1 Invention algorithm.
Fig. 8 be second embodiment source infrared image, Fig. 9 be second embodiment source visible images, Figure 10 data For the document 6 of table 2, Figure 11 data are the document 8 of table 2, and Figure 12 data are the document 12 of table 2, and Figure 13 data are table 2 Inventive algorithm.
Objective evaluation is analyzed, it can be seen that the method items evaluation index that the present embodiment proposes is better than it from table 1,2 Its method, learn that the present embodiment syncretizing effect more meets the visually-perceptible of the mankind more than.
1 first group of image co-registration evaluation of result of table：
2 second groups of image co-registration evaluation of result of table：
The fused images edge obtained using technical scheme is protruded, and contrast and brightness are higher, and target protrudes, The method that average gradient, spatial frequency, standard deviation and the comentropy of algorithm are above prior art, both effectively remained infrared Target, and can effectively obtain the spatial-domain information of source images, have obtained preferable syncretizing effect.
Embodiments of the present invention are made that with detailed description above in association with accompanying drawing, but the present invention be not limited to it is described Embodiment.To those skilled in the art, without departing from the principles and spirit of the present invention, these are implemented Mode carries out various change, modification, replacement and modification and still fallen within protection scope of the present invention.

Claims (5)

  1. A kind of 1. infrared and visual image fusion algorithm based on mixing multiscale analysis, it is characterised in that：Including following step Suddenly：
    Step 1：NSCT decomposition is carried out respectively with visible images to infrared, obtains low frequency sub-band LJ(x, y) and high-frequency sub-band Hj,r (x, y), wherein J are Decomposition order, and j, r represent decomposition scale and direction number；
    Step 2：Stationary wavelet transform is used to low frequency sub-band, a low frequency sub-band and three high-frequency sub-bands is obtained, is respectively adopted Local energy takes big be combined to be merged with compressive sensing theory to low, high-frequency sub-band with absolute value, then carries out small echo inversion Get the low frequency sub-band of NSCT reconstruct in return；
    Step 3：Judge the definition of image to be fused, the LSCN enhancing number of plies is chosen according to decision rule；
    Step 4：Big fusion rule is taken using absolute value to top high-frequency sub-band, remaining subband enters using PCNN models are improved Row fusion；
    Step 5：Obtained low frequency sub-band will be merged and high-frequency sub-band carries out NSCT inverse transformations, obtain final fused images.
  2. 2. the infrared and visual image fusion algorithm according to claim 1 based on mixing multiscale analysis, its feature It is：In step 2, it is described to take big be combined to be carried out with compressive sensing theory to low frequency sub-band with absolute value using local energy Fusion, its specific method are as follows：
    EN is energy of local area in formula, and it is defined as：
  3. 3. the infrared and visual image fusion algorithm according to claim 1 based on mixing multiscale analysis, its feature It is：In step 2, it is described to take big be combined to be carried out with compressive sensing theory to high-frequency sub-band with absolute value using local energy Fusion, it is comprised the following steps that：
    1) high-frequency sub-band images by size for m × nWithResolve into non-overlapping copies and size identical Sub-block, wherein j=1,2,3, rarefaction is carried out to each piece of sub-image using sym8 wavelet basis；
    2) design calculation matrix Φ, sampling measured to the high-frequency sub-band coefficient of input using calculation matrix, obtain measuring to AmountWithWherein k=1,2 ..., m × n；
    3) measurement vector is calculatedWithStandard deviation SDkWith definition EAVk, using based on regional standard is poor, region definition The fusion rule being combined with S function, the measurement vector merged, i.e.,：
    Graphics standard difference formula is：
    Wherein
    Image definition formula is：
    Weight coefficient ω is obtained by S function, the S function used for：
    Wherein,
    F is the contraction factor of S function, and f is more than or equal to 1, takes f=5；
    4) to the measurement vector of fusionSparse reconstruction is carried out, algorithm for reconstructing uses OMP, so as to obtain the high frequency of fused images Band
    The SW that will be obtainedFWithStationary wavelet is carried out to reconstruct to obtain the low frequency sub-band eventually for NSCT reconstruct.
  4. 4. the infrared and visual image fusion algorithm according to claim 1 based on mixing multiscale analysis, its feature It is：In step 3, specific method is as follows：
    Image definition formula is：
    Image definition is calculated and by it compared with threshold value λ according to formula (8), the layer of high frequency coefficient enhancing is determined according to comparative result Number, i.e.,：
    Wherein J is Decomposition order, and S is the synthesis definition of source images, takes α1=α2=0.5, λ=27.
  5. 5. the infrared and visual image fusion algorithm according to claim 1 based on mixing multiscale analysis, its feature It is：In step 4, specific fusion rule is as follows：
    In order to improve the vision perception of image, to remaining subband in addition to top high-frequency sub-band n, using improved PCNN moulds Type is merged, and determines fusion coefficients by comparing the igniting amplitude sum of PCNN neurons, i.e.,：
    Wherein Mij(n) for PCNN output pulse firing amplitude summation, j=1,2 ..., n-1, ε be self-defined threshold value, take ε= 0.002；
    Because traditional PCNN output uses hard-limiting function, it is impossible to reflect the amplitude difference of neuron firing, use Output of the Sigmoid functions as PCNN, it can preferably portray difference when lock-out pulse excites in igniting amplitude, PCNN Output be defined as follows：
    In order to preferably represent the marginal information of image, distinguished from improved Laplce's energy SML and local spatial frequencies Outside input and link coefficient as PCNN；
    SML is defined as follows：
    Spatial frequency is：
    Wherein RF, CF, MDF and SDF represent line frequency, row frequency, main diagonal frequencies and secondary diagonal frequencies respectively, and its formula is such as Under：
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