CN107833182A - The infrared image super resolution ratio reconstruction method of feature based extraction - Google Patents

The infrared image super resolution ratio reconstruction method of feature based extraction Download PDF

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CN107833182A
CN107833182A CN201711158675.9A CN201711158675A CN107833182A CN 107833182 A CN107833182 A CN 107833182A CN 201711158675 A CN201711158675 A CN 201711158675A CN 107833182 A CN107833182 A CN 107833182A
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刘雪超
杨婷
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Xian University of Architecture and Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4053—Scaling of whole images or parts thereof, e.g. expanding or contracting based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • 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
    • G06T2207/20036—Morphological image processing
    • 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
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

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Abstract

The present invention discloses a kind of infrared image super resolution ratio reconstruction method of feature based extraction, including：The first step, nonsingular decomposition is carried out to the original infrared image of collection, filters out the noise jamming in original infrared image；Second step, image after denoising is used feature extraction is carried out based on Gabor filter, and the feature after extraction is further enhanced with based on morphologic method；3rd step, enhanced characteristic pattern is subjected to Its Sparse Decomposition, sparse coefficient is solved using method of Lagrange multipliers, and coefficient reconstruction required by use goes out the super-resolution image of original infrared image.The present invention can effectively avoid picture noise when carrying out super-resolution rebuilding to infrared image to rebuilding the influence of effect, meanwhile, because the characteristic pattern that is extracted has preferably openness, also further improve the quality of reconstruction image.

Description

The infrared image super resolution ratio reconstruction method of feature based extraction
Technical field
The invention belongs to technical field of image processing, more particularly to a kind of super-resolution rebuilding side for infrared image Method.
Background technology
Infrared imagery technique because its with perceive object radiation infrared ray degree so that be imaged the characteristics of, can effectively overcome because Misty rain, block, the drawbacks of objective factor such as illumination deficiency causes that target can not be observed, can carry out having for round-the-clock 24 hours Effect monitoring, therefore be used widely.
In imaging process, image can because of optical lens torsional deformation, atmosphere fuzzy, sensor be fuzzy, optical dimming and A series of influence of disturbing factors such as motion blur and noise, can only obtain comparatively low resolution image.Tradition improves infrared The method of picture quality is limited in by raising contrast more makes image detail visible.However, as digital picture is more and more Be applied to every field, demand more and more higher of the people to high-definition picture.Such as, with medical image, satellite remote sensing, video Each application field based on monitoring etc..Picture element density in high-definition picture is higher, and low-resolution image is compared under identical size More details can be provided, these are indispensable in many practical applications, and these details are only to improve not obtaining under contrast .However, because of the limitation of technical matters level, development cost and volume requirement etc., cause red from hardware aspect raising Outer image resolution ratio is subject to many limitations, and the resolution ratio of existing passive type infrared detector is also universal relatively low.Therefore, should be from software Angle, which is set out, improves infrared image resolution ratio, while is also one of important channel for improving infrared image quality.
Super-resolution rebuilding concept is proposed the sixties in last century by Harris and Goodman.Its meaning is by soft The mode of part improves image resolution ratio, breaks through image intrinsic information and system hardware condition limitation, is set not replacing existing imaging Spatial resolution that is standby, not increasing raising initial data under the premise of system cost, overcomes because of image degradation and image discretization institute Caused resolution ratio distortion, the deficiency of original image spatial resolution is made up, improves the visual effect of image, is advantageous to image Further analysis, processing and identification.Therefore, super-resolution rebuilding technology is applied in the imaging process of infrared system to have The level of hardware limitation of existing infrared imaging system is broken through on effect ground, improves the spatial resolution of scope imaging, strengthens image The characteristic informations such as edge, scope are further improved to recognition capability and accuracy of identification, favorably it is in military and civilian The application in each field.
From after Harris in 1964 sets up the theoretical foundation of super-resolution rebuilding technology, domestic and foreign scholars carry in the field Go out all multi-methods.
2006, F.Sroubek and J.Flusser propose by MAP estimate based on method, using multiframe low resolution Image carries out fusion and realizes super-resolution rebuilding, overcomes the defect information of the low-resolution image collected and interframe unpredictable Error.
2007, Carlos Miravet et al. proposed the super resolution ratio reconstruction method based on study, designed a kind of nerve The structured training sampled images that network and genetic method are combined, then linear filtering obtain high-definition picture, this method Efficiency is greatly improved compared with the method based on MAP.
2010, Liu Yang et al. proposed the Super-resolution Image Restoration algorithm mixed based on Wiener filtering and POCS, effectively The defects of suppressing to be also easy to produce ringing effect and amplification noise in classical POCS algorithms.
2013, Yuzhang Chen et al. proposed to be based on MAP methods for submarine target observation, while by point spread function Number and regularization are combined, and further improve image resolution ratio and the robustness of algorithm.
2015, the method that Kwok-Wai Hung et al. propose the Wiener filtering based on FIR, only to single frames low resolution Image carries out super-resolution rebuilding, although parameter uses empirical value in algorithm, application in a variety of contexts can obtain well Effect.
2016, nonredundancy sub-pixs of the Y.Tao et al. based on original image proposed GPT methods, and is applied to remote sensing Resolution ratio is improved five times by image.
But the super-resolution rebuilding technical research for infrared image in numerous theoretical methods is less and to be reconstructed Image run-of-the-mill it is preferable.In recent years, the method for reconstructing development based on study is very fast, and this method is believed to supplement original The information of low-resolution image missing.Wherein, using rarefaction representation as representative, by establishing the super complete of height-low-resolution image Wordbook, realize the super-resolution rebuilding to image.But in process of reconstruction because using complete dictionary, also easily because of information redundancy Influence to rebuild effect.
The content of the invention
It is above-mentioned existing to overcome it is an object of the invention to provide a kind of super resolution ratio reconstruction method of feature based extraction The problem of infrared image is easily by noise jamming and bad image quality in technology.The inventive method utilizes feature extraction and sparse table Show the mode being combined, by extracting characteristics of image, improve influence that is openness, reducing redundancy, improve infrared image Super-resolution rebuilding effect.
To achieve the above object, the present invention uses following technical scheme：
The infrared image super resolution ratio reconstruction method of feature based extraction, comprises the following steps：
The first step, unusual decomposition is carried out to the original infrared image of collection, filters out the noise jamming in original infrared image；
Second step, image after denoising is used feature extraction is carried out based on Gabor filter, and with being based on morphologic side Method further enhances to the feature after extraction；
3rd step, enhanced characteristic pattern is subjected to Its Sparse Decomposition, sparse coefficient is solved using method of Lagrange multipliers, and Go out the super-resolution image of original infrared image with required coefficient reconstruction.
Further, in the first step, original infrared image Y sizes are m × n；Obtained after singular value decomposition (SVD) by L Singular value matrix ∑=the diag (σ 1, σ 2 ..., σ L) for the diagonal form that individual singular value is formed, wherein L are figure Y order, and unusual It is worth descending arrangement；Given threshold σth, the singular value that will be less than threshold value casts out, with the singular value that remains and corresponding strange Incorgruous amount rebuilds image, realizes denoising；
Further, adaptive threshold σthChoosing method be
Reconstruction image after denoising is Y 'm×n≈Um×m∑′m×nVn×n T, wherein singular value matrix be ∑ 'm×n=diag (σ1,...σth)。
Further, in second step, when carrying out feature extraction using Gabor filter, Orientation Features extraction is carried out； Meanwhile the information that each characteristic pattern is covered is considered when being merged to these characteristic patterns, merged using different weight coefficients.
Further, two-dimentional gabor wave filters are
Wherein, (i, j) is space pixel, σiAnd σjIt is standard deviation, f0It is spatial domain frequency；If convert former coordinate system for i '= Icos θ+jsin θ, j '=- isin θ+jcos θ then obtain multiple directions gabor wave filters by adjusting angle parameter theta.
Further, 6 different angle direction structure gabor wave filters are chosen, and are rolled up respectively with image after denoising Product computing, obtains series of features figure F (i, j)={ Fθ(i, j) | θ=0 °, 30 °, 60 °, 90 °, 120 °, 150 ° }；
Wherein, Fθ(i, j)=Y'(i, j) * gθ(i, j), gθIt is the gabor wave filters that angle is θ；6 different angle difference For 0 °, 30 °, 60 °, 90 °, 120 ° and 150 °.
Further, in second step, the characteristics of image contained by the characteristic pattern under different directions is different, melts carrying out feature During conjunction, using different weight coefficients；Coefficient is
Wherein, M (Fθ(i, j)) it is characterized figure FθThe mean square deviation of (i, j)；
Characteristic pattern fusion results are
Further, in second step, image is handled using expansion and erosion operation, method is
Wherein,Expansion, erosion operation are represented with Θ；B is morphology operations template, and u and v are template interior elements；Meanwhile Gradient information is considered during morphological transformation, wherein F " (i-u, j-v)=(F+ Δs F ') (i-u, j-v) is in artwork Increase gradient information；F " (i+u, j+v)=(F- Δs F ') (i+u, j+v) is that gradient information is subtracted in artwork；
It is F " to obtain enhanced characteristic pattern.
Further, the 3rd step specifically includes：
To enhanced characteristic pattern F " in low-resolution dictionary DlIn decomposed；Using L1Norm solves, such as following formula：
min||αl||1 s.t.||Dlαl-F”||≤ξ
Wherein, αlFor the sparse coding obtained from low resolution characteristic pattern；S.t. constraint is represented；ξ is the first of setting Arithmetic number.
To ensure the continuity and uniformity of image after rebuilding, above formula is optimized, obtained
min||α||1 s.t.||Dlα-F”||≤ξ1
||PDhα-Q||≤ξ2
Wherein, Q is and the previous equitant part of reconstruction image block；P is that extraction is adapted with lap size Dictionary size；α is sparse coding to be asked；ξ1For the second arithmetic number of setting；ξ2For the 3rd arithmetic number of setting；DhFor high-resolution Rate dictionary, introduce method of Lagrange multipliers and carry out solving to obtain following formula
Wherein,λ=0.1 is relaxation factor；
Factor alpha is solved, through x=Dhα rebuilds high-definition picture block；Image block is combined again, forms complete high-resolution Rate image.
Relative to prior art, the invention has the advantages that：
The present invention extracts original low-resolution infrared image using single-frame images as input, using the method for view-based access control model mechanism Feature, improve image sparse；Meanwhile the characteristic pattern of extraction is strengthened using the method for morphology combination gradient, further Key message in prominent image.It is that input is trained collection study to enhanced characteristic pattern, reduced superfluous in complete dictionary Influence of the remaining information in process of reconstruction, overcome lack priori while, without additional samples storehouse, improve high after rebuilding Image in different resolution quality.
Brief description of the drawings
The super resolution ratio reconstruction method overall flow figure of Fig. 1 feature baseds extraction；
The super resolution ratio reconstruction method particular flow sheet of Fig. 2 feature baseds extraction.
Embodiment
The method combination accompanying drawing that reference will now be made in detail the present invention is described in further detail to embodiments of the invention.
Refer to shown in Fig. 1 and Fig. 2, a kind of infrared image super resolution ratio reconstruction method of feature based extraction of the present invention, Including：
The first step, to the original Infrared Image Denoising of collection：The original infrared image collected often contains much noise, First to image denoising.
Singular value decomposition (SVD) is used as a kind of matrix disassembling method in linear algebra, can reflect the inherent algebraically sheet of image Matter, can be as the important method of pattern classification.
Original noisy low resolution infrared image Y is decomposed into Y through SVDm×n=Um×m∑m×nVn×n T。
Wherein, image Y sizes are m × n；The diagonal form for obtaining being made up of L singular value after singular value decomposition it is strange Different value matrix ∑=diag (σ 1, σ 2 ..., σ L), wherein L are figure Y order, and the descending arrangement of singular value.Singular value it is big The heterogeneity and feature of small reflection image, larger singular value are related to signal in image；By contrast, less singular value Noise in correspondence image.Pass through given threshold σth, the singular value that will be less than threshold value casts out, with the singular value that remains and right The singular vector answered rebuilds image, realizes denoising.Compared with other denoising methods, this method can be compared with while denoising The edge feature of good reservation image information.
Adaptive threshold σthChoosing method be
Remain larger than and be equal to σthSingular value, reconstruction image realizes denoising；Reconstruction image after denoising is Y 'm×n≈Um×m ∑′m×nVn×n T, wherein singular value matrix be ∑ 'm×n=diag (σ1,...σth)。
Second step, feature extraction is carried out to original low-resolution image after denoising.The present invention uses and combines Gabor filter The process is realized with the method for morphological change；Specifically include：
2.1), the feature of Gabor filter and visual perception are similar, to extract characteristics of image.
Two-dimentional gabor wave filters are
Wherein, (i, j) is space pixel, σiAnd σjIt is standard deviation, f0It is spatial domain frequency.If convert former coordinate system for i '= Icos θ+jsin θ, j '=- isin θ+jcos θ then can obtain multiple directions gabor wave filters by adjusting angle parameter theta.
The present invention chooses 6 different angle direction structure gabor wave filters, and carries out convolution fortune with image after denoising respectively Calculate, obtain series of features figure F (i, j)={ Fθ(i, j) | θ=0 °, 30 °, 60 °, 90 °, 120 °, 150 ° }.
Wherein Fθ(i, j)=Y'(i, j) * gθ(i, j), gθThe gabor wave filters for being θ for angle.
Characteristic pattern under different directions is merged, using different weight coefficients.
Coefficient is
Wherein, M (Fθ(i, j)) it is characterized figure FθThe mean square deviation of (i, j).
Characteristic pattern fusion results are
2.2), combining form method further enhances to the characteristic pattern after extraction.
Characteristic pattern is further enhanced using based on morphological method.
Method is
Wherein,Expansion, erosion operation are represented with Θ；B is morphology operations template, and u and v are template interior elements；Meanwhile Gradient information is considered during morphological transformation, wherein F " (i-u, j-v)=(F+ Δs F ') (i-u, j-v) is in artwork Increase gradient information；F " (i+u, j+v)=(F- Δs F ') (i+u, j+v) is that gradient information is subtracted in artwork；
It is F " to obtain enhanced characteristic pattern.
3rd step, sparse expression coefficient is drawn with the characteristic pattern training of low-resolution image, passes through excessively complete dictionary training Study obtains high-definition picture.
To from enhanced characteristic pattern F " in low-resolution dictionary DlIn decomposed.Because characteristic pattern F " has enough It is openness, therefore use L1Norm solves, such as following formula.
min||αl||1 s.t.||Dlαl-F”||≤ξ
Wherein, αlFor the sparse coding obtained from low resolution characteristic pattern；S.t. constraint is represented；ξ is the first of setting Arithmetic number.
To ensure the continuity and uniformity of image after rebuilding, above formula is optimized, obtained
min||α||1 s.t.||Dlα-F”||≤ξ1
||PDhα-Q||≤ξ2
Wherein, Q is and the previous equitant part of reconstruction image block；P is that extraction is adapted with lap size Dictionary size；α is sparse coding to be asked；ξ1For the second arithmetic number of setting；ξ2For the 3rd arithmetic number of setting；DhFor high-resolution Rate dictionary, introduce method of Lagrange multipliers and carry out solving to obtain following formula
Wherein,λ=0.1 is relaxation factor；
Factor alpha is solved, through x=Dhα rebuilds high-definition picture block；Image block is combined again, forms complete high-resolution Rate image.
Above-described specific descriptions, the purpose, technical scheme and beneficial effect of invention are carried out further specifically It is bright, it should be understood that the specific embodiment that the foregoing is only the present invention, the protection model being not intended to limit the present invention Enclose, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., should be included in the present invention Protection domain within.

Claims (8)

1. the infrared image super resolution ratio reconstruction method of feature based extraction, it is characterised in that comprise the following steps：
The first step, unusual decomposition is carried out to the original infrared image of collection, filters out the noise jamming in original infrared image；
Second step, image after denoising is used feature extraction is carried out based on Gabor filter, and with being based on morphologic method pair Feature after extraction further enhances；
3rd step, Its Sparse Decomposition is carried out by enhanced characteristic pattern, solves sparse coefficient using method of Lagrange multipliers, and use institute Coefficient reconstruction is asked to go out the super-resolution image of original infrared image.
2. the infrared image super resolution ratio reconstruction method of feature based extraction according to claim 1, it is characterised in that：The In one step, original infrared image Y sizes are m × n；The diagonal form for obtaining being made up of L singular value after singular value decomposition Singular value matrix ∑=diag (σ 1, σ 2 ..., σ L), wherein L are figure Y order, and the descending arrangement of singular value；Given threshold σth, the singular value that will be less than threshold value casts out, and image is rebuild with the singular value and corresponding singular vector that remain, realizes Denoising；
Adaptive threshold σthChoosing method be
Reconstruction image after denoising is Y 'm×n≈Um×m∑′m×nVn×n T, wherein singular value matrix be ∑ 'm×n=diag (σ1,... σth)。
3. the infrared image super resolution ratio reconstruction method of feature based extraction according to claim 1, it is characterised in that：The In two steps, when carrying out feature extraction using Gabor filter, Orientation Features extraction is carried out；Meanwhile these characteristic patterns are entered Consider the information that each characteristic pattern is covered during row fusion, merged using different weight coefficients.
4. the infrared image super resolution ratio reconstruction method of feature based extraction according to claim 3, it is characterised in that：Two Tieing up gabor wave filters is
Wherein, (i, j) is space pixel, σiAnd σjIt is standard deviation, f0It is spatial domain frequency；If it is i '=icos θ to convert former coordinate system + jsin θ, j '=- isin θ+jcos θ then obtain multiple directions gabor wave filters by adjusting angle parameter theta.
5. the infrared image super resolution ratio reconstruction method of feature based extraction according to claim 3, it is characterised in that：Choosing 6 different angle direction structure gabor wave filters are taken, and carry out convolution algorithm with image after denoising respectively, obtain a series of spies Sign figure F (i, j)={ Fθ(i, j) | θ=0 °, 30 °, 60 °, 90 °, 120 °, 150 ° }；
Wherein, Fθ(i, j)=Y'(i, j) * gθ(i,j)；gθIt is the gabor wave filters that angle is θ；6 different angles are respectively 0 °, 30 °, 60 °, 90 °, 120 ° and 150 °.
6. the infrared image super resolution ratio reconstruction method of feature based extraction according to claim 3, it is characterised in that：The In two steps, the characteristics of image contained by the characteristic pattern under different directions is different, when carrying out Fusion Features, using different weights Coefficient, coefficient are
Wherein, M (Fθ(i, j)) it is characterized figure FθThe mean square deviation of (i, j)；
Characteristic pattern fusion results are
7. the infrared image super resolution ratio reconstruction method of feature based extraction according to claim 1, it is characterised in that：The In two steps, image is handled using expansion and erosion operation, method is
Wherein, ⊕ and Θ represents expansion, erosion operation；B is morphology operations template, and u and v are template interior elements；Meanwhile in shape Gradient information is considered in state conversion process, wherein F " (i-u, j-v)=(F+ Δs F ') (i-u, j-v) is to increase in artwork Gradient information；F " (i+u, j+v)=(F- Δs F ') (i+u, j+v) is that gradient information is subtracted in artwork；
F " is the characteristic pattern obtained after strengthening.
8. the infrared image super resolution ratio reconstruction method of feature based extraction according to claim 1, it is characterised in that：The Three steps specifically include：
To enhanced characteristic pattern F " in low-resolution dictionary DlIn decomposed；Using L1Norm solves, such as following formula：
min||αl||1s.t.||Dlαl-F”||≤ξ
Wherein, αlFor the sparse coding obtained from low resolution characteristic pattern；S.t. constraint is represented；ξ is that first set is positive real Number；
To ensure the continuity and uniformity of image after rebuilding, above formula is optimized, obtained
min||α||1 s.t.||Dlα-F”||≤ξ1
||PDhα-Q||≤ξ2
Wherein, Q is and the previous equitant part of reconstruction image block；P is the dictionary that extraction is adapted with lap size Size；α is sparse coding to be asked；ξ1For the second arithmetic number of setting；ξ2For the 3rd arithmetic number of setting；DhFor high-resolution word Allusion quotation, introduce method of Lagrange multipliers and carry out solving to obtain following formula：
<mrow> <munder> <mi>min</mi> <mi>&amp;alpha;</mi> </munder> <mo>|</mo> <mo>|</mo> <msup> <mi>D</mi> <mo>&amp;prime;</mo> </msup> <mi>&amp;alpha;</mi> <mo>-</mo> <mi>G</mi> <mo>|</mo> <msubsup> <mo>|</mo> <mn>2</mn> <mn>2</mn> </msubsup> <mo>+</mo> <mi>&amp;lambda;</mi> <mo>|</mo> <mo>|</mo> <mi>&amp;alpha;</mi> <mo>|</mo> <msub> <mo>|</mo> <mn>1</mn> </msub> <mo>;</mo> </mrow>
Wherein,λ=0.1 is relaxation factor；
Factor alpha is solved, through x=Dhα rebuilds high-definition picture block；Image block is combined again, forms complete high resolution graphics Picture.
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