CN109461164A - A kind of infrared small target detection method based on direction nuclear reconstitution - Google Patents
A kind of infrared small target detection method based on direction nuclear reconstitution Download PDFInfo
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Abstract
The present invention provides a kind of infrared small target detection method based on direction nuclear reconstitution, and raw video is divided into several image blocks first, calculates the local direction nuclear expression of each pixel, obtains the feature vector of each image block；Centered on each image block, the adjacent features vector set of N number of image block around is obtained, design factor vector, according to adjacent features vector set and coefficient vector, the reconstruction features vector for calculating each image block is indicated；To obtain the confidence map of entire infrared image；It is split according to threshold value, infrared small target is finally partitioned into from confidence map.The present invention encodes infrared image block using local feature kernel method, robustly calculates image internal characteristics structure and solves pixel-level image noise and ambiguity.Meanwhile the present invention utilizes adjacent features vector reconstruction features vector, enhances contrast by residual computations, achievees the purpose that prominent infrared image target while inhibiting ambient noise, can be used for improving infrared small target detection problem.
Description
Technical field
The invention belongs to image processing methods, and in particular to a kind of infrared small target detection based on direction nuclear reconstitution
Method.
Background technique
Infrared small target detection plays a crucial role in infrared monitoring and early warning system.However it is pre- for early stage
In alert situation, farther out due to distance, the pixel of target is too small in infrared image.Additionally due to infrared energy is with range attenuation
Larger, this causes the noise of infrared target relatively low.Meanwhile there are certain picture noises and a variety of dry for infrared remote sensing image
It disturbs.Therefore, infrared image is usually second-rate, and traditional characteristics of image descriptor is caused to be unable to satisfy infrared small target detection
Demand.
In recent years, two kinds are generally divided into for the detection method of infrared small target at present: first is that by sequence infrared image,
Target is confirmed according to the priori knowledge of the target characteristics of motion and intensity profile form；Second is that the spy for passing through single-frame images
Sign, regards infrared small target as Gauss speck, inhibits background using suitable method.However due to the mesh of infrared sequence image
Mark, is not available conventional feature descriptor and matching process usually, so that extracting time information is still from infrared sequence image
It is so extremely challenging.And it is many to the Processing Algorithm of single-frame images at present, there are mean filter, median filtering, based on Mathematical Morphology
Method, the method based on image entropy and the method based on extreme value theory.In addition, from the angle of vision attention, also
Infrared small target detection method based on conspicuousness.However most of infrared small target detection methods based on significant property have height
It computation complexity and is difficult to carry out parallel optimization.Currently, the method that existing certain methods use piecemeal, passes through simply vector
Change original pixel intensities to calculate the feature descriptor of infrared image block.However, being described by the feature that original pixel intensities calculate
Symbol is directly influenced by picture quality.Therefore, the infrared small target detection based on piecemeal needs to carry out robust to infrared image block
Expression.
Summary of the invention
The present invention mainly solves second-rate infrared image present in the prior art and infrared target and background contrast
Spend weaker, infrared image the problems such as there are certain picture noises；It provides a kind of based on regularization local direction nuclear reconstitution
Infrared small target detection method can identify the target in infrared image simultaneously and inhibit ambient noise.
The technical scheme adopted by the invention is that: a kind of infrared small target detection method based on direction nuclear reconstitution, including
Following steps:
Step 1, raw video is divided into each and every one several image blocks first, the pixel size for including in each image block is P；
Step 2, according to local direction core, the local direction nuclear expression of each pixel is calculated, each image block is obtained
Feature vector；
Step 3, centered on each image block, the adjacent features vector set of N number of image block around, design factor are obtained
Vector, according to adjacent features vector set and coefficient vector, the reconstruction features vector for calculating each image block is indicated；
Step 4, by the residual error of reconstruction features vector and original image block eigenvector, calculate each center image block with
Local contrast between adjacent image block obtains the confidence level of each pixel in image block, finally obtains entire infrared image
Confidence map；
Step 5, it is split according to threshold value, infrared small target is partitioned into from confidence map.
Further, the specific implementation that the feature vector of image block is calculated in step 2 is as follows,
Step 2.1, before calculating local direction core, the gradient in global field is normalized first；
Step 2.2, the feature vector for calculating image block is assessed according to local direction:
Wherein, with xiCentered on image block can indicate W (xi)={ x1,…,xi,…,xP, in xiCoordinate on position
Intensity can be expressed as I (xi), xjAnd xiRefer to different location of pixels, xj∈W(xi), K (xi,xj) refer to xiCentered on
The feature vector of image block, h are global smoothing parameter, covariance matrix CjBy with coordinate vectorCentered on neighbour
Estimated by the set of near space gradient vector,
Wherein M indicates the number of pixels of proximity space, I1() and I2() is I () along the one of trunnion axis and vertical axis
Order derivative.
Further, being implemented as follows for image block reconstruction features vector is calculated in step 3,
Step 3.1, according to step 2.2 feature vector calculated, with central feature vector fc∈RP×1Centered on it is N number of
Adjacent feature vector set can be expressed as F=[f1,…,fN], w ∈ RN×1For linear combination coefficient, optimization object function is obtained
To coefficient vector w=(FT·F+λ·I)-1·FT·fc,
Wherein λ is regularization term, is set as 10；
Step 3.2, according to adjacent features vector F=[f1,…,fN] and coefficient vector w, archicenter feature vector fcIt can
To be redeveloped into fr,
fr=Fw=F (FT·F+λ·I)-1·FT·fc(formula three)
Wherein, FTIt is the transposition of F.
Further, the specific implementation of the confidence level of each pixel is as follows in step 4 calculating image block,
As unit of pixel, successively calculates local direction core image block characteristics centered on current pixel and its is neighbouring
Local direction core image block characteristics, obtain the confidence level of each pixel:
ALCM-LSK(fc, F)=| | fc-fr||2(formula
=| | fc-F·(FT·F+λ·I)-1·FT·fc||2
Wantonly).
Further, step 5 is split using adaptive threshold according to the following formula,
Th=u+k σ (formula 5)
Wherein u is the mean value of confidence map, and σ is the standard deviation of confidence map, and k is an empirical, is set as 10.With it is existing
Technology is compared, the advantages of the present invention: since the quality of infrared image is usually poor (for example, infrared image is frequent
By infrared sensor noise pollution), we encode infrared image block using local feature kernel method, and local feature core can
Robustly to calculate image internal characteristics structure and solve pixel-level image noise and ambiguity.Meanwhile the present invention is using neighbouring
Feature vector reconstruction features vector enhances contrast by residual computations, reaches prominent infrared image target while inhibiting background
The purpose of noise can be used for improving infrared small target detection problem.
Detailed description of the invention
Fig. 1: for the overview flow chart of the embodiment of the present invention；Wherein, Dark grey small circle includes infrared small target, light grey
Great circle indicates ambient noise.1st, the 3rd and the 5th image show original image, confidence map and final detection result respectively.2nd,
The 3-D view of the 1st, the 3rd and the 5th image is presented in 4th and the 6th image respectively.
Fig. 2: for the reconstruction local direction core feature schematic diagram of the embodiment of the present invention.
Specific embodiment
Understand for the ease of those of ordinary skill in the art and implement the present invention, with reference to the accompanying drawings and embodiments to this hair
It is bright to be described in further detail, it should be understood that implementation example described herein is merely to illustrate and explain the present invention, not
For limiting the present invention.
Referring to Fig.1, a kind of infrared small target detection method based on direction nuclear reconstitution provided by the invention, including following step
It is rapid:
Step 1: raw video being divided into several image blocks first, the pixel size for including in each image block is P, such as
Shown in Fig. 2, wherein s is tile size, and t is the overlapping dimension of image block.
The position of each pixel of infrared image is by coordinate vectorIt indicates, in xiCoordinate intensity on position
It can be expressed as I (xi), with xiCentered on image block can indicate W (xi)={ x1,…,xi,…,xP}。
Step 2: calculating the local direction nuclear expression of each pixel according to local direction core see Fig. 2, obtain each
The feature vector of a image block.
Step 2.1: before calculating local direction core, normalizing being carried out to the gradient in global field (i.e. entire image) first
Change.
Step 2.2: the feature vector for calculating image block is assessed according to local direction:
Wherein with xiCentered on image block can indicate W (xi)={ x1,…,xi,…,xP, in xiCoordinate on position
Intensity can be expressed as I (xi), xjAnd xiRefer to different location of pixels, xj∈W(xi), K (xi,xj) refer to xiCentered on
The feature vector of image block, h are global smoothing parameter, are set as 0.2, covariance matrix CjBy with positionFor in
Estimated by the set of the proximity space gradient vector of the heart,
Wherein M indicates the number of pixels of proximity space, I1() and I2() is I () along the one of trunnion axis and vertical axis
Order derivative.Step 3: centered on each image block, obtaining the adjacent features vector set of N number of image block around, design factor
Vector, according to adjacent features vector set and coefficient vector, the reconstruction features vector for calculating each image block is indicated.
Step 3.1: according to step 2.2 feature vector calculated, with fc∈RP×1Centered on N number of adjacent feature vector
Set can be expressed as F=[f1,…,fN], w ∈ RN×1For linear combination coefficient, optimization object function obtains coefficient vector w=
(FT·F+λ·I)-1·FT·fc:
Wherein λ is regularization term, it controls the tradeoff between estimated bias and the variance of model of fit.
Step 3.2: according to adjacent features vector F=[f1,…,fN] and coefficient vector w, archicenter feature vector fcIt can
To be redeveloped into fr:
fr=Fw=F (FT·F+λ·I)-1·FT·fc(formula three)；
Wherein, FTIt is the transposition of F.
Step 4: by the residual error of reconstruction features vector and original image block eigenvector, calculate each center image block with
Local contrast between adjacent image block obtains the confidence level of each pixel in image block, finally obtains entire infrared image
Confidence map.
As unit of pixel, successively calculates local direction core image block characteristics centered on current pixel and its is neighbouring
Local direction core image block characteristics, obtain the confidence level of each pixel:
ALCM-LSK(fc, F)=| | fc-fr||2(formula is wantonly)；
=| | fc-F·(FT·F+λ·I)-1·FT·fc||2
Step 5: being split according to threshold value, infrared small target is detected from confidence map.According to the following formula using adaptive
Threshold value is split:
Th=u+k σ (formula 5)；
Wherein u is the mean value of confidence map, and σ is the standard deviation of confidence map, and k is an empirical, is set as 10.
It should be understood that the part that this specification does not elaborate belongs to the prior art.
It should be understood that the above-mentioned description for preferred embodiment is more detailed, can not therefore be considered to this
The limitation of invention patent protection range, those skilled in the art under the inspiration of the present invention, are not departing from power of the present invention
Benefit requires to make replacement or deformation under protected ambit, fall within the scope of protection of the present invention, this hair
It is bright range is claimed to be determined by the appended claims.
Claims (5)
1. a kind of infrared small target detection method based on direction nuclear reconstitution, which comprises the steps of:
Step 1, raw video is divided into each and every one several image blocks first, the pixel size for including in each image block is P；
Step 2, according to local direction core, the local direction nuclear expression of each pixel is calculated, the spy of each image block is obtained
Levy vector；
Step 3, centered on each image block, obtain the adjacent features vector set of N number of image block around, design factor to
Amount, according to adjacent features vector set and coefficient vector, the reconstruction features vector for calculating each image block is indicated；
Step 4, by the residual error of reconstruction features vector and original image block eigenvector, calculate each center image block with it is neighbouring
Local contrast between image block obtains the confidence level of each pixel in image block, finally obtains setting for entire infrared image
Letter figure；
Step 5, it is split according to threshold value, infrared small target is partitioned into from confidence map.
2. a kind of infrared small target detection method based on direction nuclear reconstitution as described in claim 1, it is characterised in that: step
The specific implementation that the feature vector of image block is calculated in 2 is as follows,
Step 2.1, before calculating local direction core, the gradient in global field is normalized first；
Step 2.2, the feature vector for calculating image block is assessed according to local direction:
Wherein, with xiCentered on image block can indicate W (xi)={ x1,…,xi,…,xP, in xiCoordinate intensity on position
It can be expressed as I (xi), xjAnd xiRefer to different location of pixels, xj∈W(xi), K (xi,xj) refer to xiCentered on image
The feature vector of block, h are global smoothing parameter, covariance matrix CjBy with coordinate vectorCentered on it is neighbouring empty
Between gradient vector set estimated by,
Wherein M indicates the number of pixels of proximity space, I1() and I2() is that I () is led along the single order of trunnion axis and vertical axis
Number.
3. a kind of infrared small target detection method based on direction nuclear reconstitution as claimed in claim 2, it is characterised in that: step
Being implemented as follows for image block reconstruction features vector is calculated in 3,
Step 3.1, according to step 2.2 feature vector calculated, with central feature vector fc∈RP×1Centered on it is N number of adjacent
Feature vector set can be expressed as F=[f1,…,fN], w ∈ RN×1For linear combination coefficient, optimization object function is
Number vector w=(FT·F+λ·I)-1·FT·fc,
Wherein λ is regularization term；
Step 3.2, according to adjacent features vector F=[f1,…,fN] and coefficient vector w, archicenter feature vector fcIt can weigh
It builds as fr,
fr=Fw=F (FT·F+λ·I)-1·FT·fc(formula three)
Wherein, FTIt is the transposition of F.
4. a kind of infrared small target detection method based on direction nuclear reconstitution as claimed in claim 3, it is characterised in that: step
The specific implementation of the confidence level of each pixel is as follows in 4 calculating image blocks,
As unit of pixel, the local direction core image block characteristics centered on current pixel and its neighbouring office are successively calculated
Portion direction core image block characteristics, obtain the confidence level of each pixel:
5. a kind of infrared small target detection method based on direction nuclear reconstitution as claimed in claim 4, it is characterised in that: step
5 are split using adaptive threshold according to the following formula,
Th=u+k σ (formula 5)
Wherein u is the mean value of confidence map, and σ is the standard deviation of confidence map, and k is an empirical.
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| CN112037145A (en) * | 2020-08-31 | 2020-12-04 | 成都信息工程大学 | Medical MRI (magnetic resonance imaging) image up-sampling method based on self-adaptive local steering nucleus |
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Cited By (4)
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| CN110796028A (en) * | 2019-10-11 | 2020-02-14 | 武汉大学 | Unmanned aerial vehicle image small target detection method and system based on local adaptive geometric transformation |
| CN110796028B (en) * | 2019-10-11 | 2021-08-17 | 武汉大学 | Unmanned aerial vehicle image small target detection method and system based on local adaptive geometric transformation |
| CN113888450A (en) * | 2020-07-02 | 2022-01-04 | 中强光电股份有限公司 | Image segmentation method and electronic device |
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