CN107833182A - The infrared image super resolution ratio reconstruction method of feature based extraction - Google Patents
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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
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a super-resolution reconstruction method for an infrared image.
Background
The infrared imaging technology is characterized by sensing the infrared ray radiation degree of an object and then imaging, can effectively overcome the defect that the target cannot be observed due to objective factors such as rain fog, shielding, insufficient illumination and the like, can effectively monitor for 24 hours in all weather, and is widely applied.
In the imaging process, images can only obtain relatively low-resolution images due to the influence of a series of interference factors such as optical lens distortion, atmospheric blurring, sensor blurring, optical blurring, motion blurring and noise. Conventional methods for improving infrared image quality are often limited to making image details visible by improving contrast. However, as digital images are increasingly applied to various fields, the demand for high resolution images is increasing. Such as medical images, satellite remote sensing, video monitoring, etc. The higher density of pixels in a high resolution image provides more detail than a low resolution image of the same size, which is essential in many practical applications, and which cannot be obtained with only improved contrast. However, due to the limitations of the technical process level, the development cost, the volume requirement, and the like, there are many limitations on improving the resolution of the infrared image from the aspect of hardware, and the resolution of the existing passive infrared detector is generally low. Therefore, the resolution of the infrared image should be improved from a software perspective, and the improvement of the quality of the infrared image is also one of important ways.
The super-resolution reconstruction concept was proposed by Harris and Goodman in the last 60 th century. The method has the advantages that the image resolution is improved in a software mode, the limitation of inherent information of the image and system hardware conditions is broken through, the spatial resolution of original data is improved on the premise of not replacing the conventional imaging equipment and not increasing the system cost, the resolution distortion caused by image degradation and image discretization is overcome, the defect of the spatial resolution of the original image is made up, the visual effect of the image is improved, and the method is favorable for further analyzing, processing and identifying the image. Therefore, when the super-resolution reconstruction technology is applied to the imaging process of the infrared system, the hardware level limitation of the existing infrared imaging system can be effectively broken through, the imaging spatial resolution of the observation equipment is improved, the characteristic information such as image edges is enhanced, the image identification capability and the identification precision of the observation equipment are further improved, and the application of the super-resolution reconstruction technology to various fields of military affairs and civil use is facilitated.
Since the theoretical basis of the super-resolution reconstruction technology established by Harris in 1964, scholars at home and abroad put forward a plurality of methods in the field.
In 2006, a method based on MAP estimation is proposed by f.sroubek and j.flusser, and multi-frame low-resolution images are fused to realize super-resolution reconstruction, so that the information defect of the acquired low-resolution images and the unpredictable error between frames are overcome.
In 2007, Carlos Miravet et al propose a super-resolution reconstruction method based on learning, design a structure training sampling image combining a neural network and a genetic method, and then obtain a high-resolution image through linear filtering.
In 2010, Liuyang et al propose a super-resolution image restoration algorithm based on wiener filtering and POCS mixing, and effectively suppress the defects of ringing effect and amplified noise easily generated in the classical POCS algorithm.
In 2013, Yuzhang Chen et al propose a MAP-based method for underwater target observation, and simultaneously combine a point spread function and regularization to further improve the image resolution and the robustness of the algorithm.
In 2015, Kwok-Wai Hung et al propose a FIR-based wiener filtering method, which only performs super-resolution reconstruction on a single-frame low-resolution image, and although parameters in the algorithm adopt empirical values, the method can achieve good effects when applied in various environments.
In 2016, y.tao et al proposed a GPT method based on non-redundant sub-pixels of the original image and applied to remote sensing images to improve resolution by a factor of five.
However, among many theoretical methods, there are few studies on super-resolution reconstruction techniques for infrared images, and the quality of the image to be reconstructed is generally good. In recent years, learning-based reconstruction methods have been developed more rapidly, which are believed to supplement the missing information of the original low-resolution images. The super-resolution reconstruction of the image is realized by establishing a super-complete dictionary set of the high-low resolution image by taking sparse representation as a representative. However, the reconstruction effect is easily affected by information redundancy due to the adoption of the over-complete dictionary in the reconstruction process.
Disclosure of Invention
The invention aims to provide a super-resolution reconstruction method based on feature extraction, which aims to solve the problems that an infrared image is easily interfered by noise and the imaging quality is poor in the prior art. The method provided by the invention has the advantages that the mode of combining feature extraction and sparse representation is utilized, and the image features are extracted, so that the sparsity is improved, the influence of redundant information is reduced, and the super-resolution reconstruction effect of the infrared image is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
the infrared image super-resolution reconstruction method based on feature extraction comprises the following steps:
performing singular decomposition on an acquired original infrared image, and filtering noise interference in the original infrared image;
secondly, extracting the features of the denoised image by adopting a Gabor-based filter, and further enhancing the extracted features by using a morphology-based method;
and thirdly, performing sparse decomposition on the enhanced characteristic diagram, solving a sparse coefficient by adopting a Lagrange multiplier method, and reconstructing a super-resolution image of the original infrared image by using the solved coefficient.
Further, in the first step, the size of the original infrared image Y is mxn; after Singular Value Decomposition (SVD), obtaining a diagonal singular value matrix sigma-diag (sigma 1, sigma 2, …, sigma L) consisting of L singular values, wherein L is the rank of a graph Y, and the singular values are arranged from large to small; setting a threshold value sigmathRemoving the singular value lower than the threshold value, and reconstructing an image by using the retained singular value and the corresponding singular vector to realize denoising;
further, an adaptive threshold σthIs selected by
The reconstructed image after being denoised is Y'm×n≈Um×m∑′m×nVn×n TWherein the singular value matrix is sigma'm×n＝diag(σ1,...σth)。
Further, in the second step, when a Gabor filter is used for feature extraction, multi-directional feature extraction is carried out; meanwhile, information covered by each characteristic diagram is considered when the characteristic diagrams are fused, and different weight coefficients are adopted for fusion.
Further, the two-dimensional gabor filter is
Where (i, j) is the spatial pixel, σiAnd σjIs the standard deviation, f0Is a spatial domain frequency; if the original coordinate system i 'icos theta + jssin theta and j' isin theta + jcos theta are transformed, a plurality of directional gabor filters are obtained by adjusting the angle parameter theta.
Further, 6 different angle directions are selected to construct a gabor filter, and convolution operation is performed on the gabor filter and the denoised image respectively to obtain a series of characteristic images F (i, j) ═ Fθ(i,j)|θ＝0°,30°,60°,90°,120°,150°}；
Wherein, Fθ(i,j)＝Y'(i,j)*gθ(i,j)，gθIs a gabor filter at an angle θ; the 6 different angles are 0 °, 30 °, 60 °, 90 °, 120 ° and 150 °, respectively.
Furthermore, in the second step, the image characteristics contained in the characteristic diagrams in different directions are different, and different weight coefficients are adopted during characteristic fusion; coefficient of
Wherein, M (F)θ(i, j)) is a feature map Fθ(ii) the mean square error of (i, j);
the feature map fusion result is
Furthermore, in the second step, the image is processed by adopting expansion and corrosion operation
Wherein,and Θ represents expansion, corrosion operations; b is a morphological operation template, u and v are intra-template elements; meanwhile, gradient information is considered in the morphological transformation process, wherein F ″ (i-u, j-v) ═ F + Δ F') (i-u, j-v) is used for adding the gradient information in the original image; f "(i + u, j + v) ═ F- Δ F') (i + u, j + v) is to subtract the gradient information from the original image;
the enhanced signature is F ".
Further, the third step specifically includes:
for the enhanced feature map F' in the low-resolution dictionary DlDecomposing; by using L1The norm is solved as follows:
min||αl||1s.t.||Dlαl-F”||≤ξ
wherein, αlFor sparse coding from the low resolution profile, s.t. represents constraints, ξ is the first positive real number set.
The above formula is optimized to ensure the continuity and consistency of the reconstructed images
min||α||1s.t.||Dlα-F”||≤ξ1
||PDhα-Q||≤ξ2
Wherein Q is the part overlapped with the previous reconstructed image block, P is the size of a dictionary which is extracted and is adaptive to the size of the overlapped part, α is the sparse code to be solved, ξ1ξ for a set second positive real number2Is a set third positive real number; dhFor a high-resolution dictionary, a Lagrange multiplier method is introduced for solving to obtain the following formula
Wherein,λ ═ 0.1 is the relaxation factor;
solving for the coefficient α, via x ═ Dhα reconstructing high-resolution image blocks and combining the image blocks to form a complete high-resolution image.
Compared with the prior art, the invention has the following beneficial effects:
the method takes a single-frame image as input, adopts a method based on a visual mechanism to extract the characteristics of an original low-resolution infrared image, and improves the image sparsity; meanwhile, the extracted characteristic diagram is enhanced by adopting a morphology combined gradient method, and key information in the image is further highlighted. The enhanced feature map is used for learning a training set, the influence of redundant information in an over-complete dictionary in the reconstruction process is reduced, the lack of prior knowledge is overcome, an additional sample base is not needed, and the quality of the reconstructed high-resolution image is improved.
Drawings
FIG. 1 is an overall flow chart of a super-resolution reconstruction method based on feature extraction;
fig. 2 is a specific flowchart of a super-resolution reconstruction method based on feature extraction.
Detailed Description
Reference will now be made in detail to the present method for further illustrating the embodiments of the present invention with reference to the accompanying drawings.
Referring to fig. 1 and 2, the method for reconstructing super-resolution infrared image based on feature extraction of the present invention includes:
firstly, denoising an acquired original infrared image: the collected original infrared image often contains a large amount of noise, and the image is denoised firstly.
Singular Value Decomposition (SVD) is a matrix decomposition method in linear algebra, can reflect the intrinsic algebra essence of images, and can be used as an important method for mode classification.
Decomposing original noisy low-resolution infrared image Y into Y through SVDm×n＝Um×m∑m×nVn×n T。
Wherein the size of the image Y is mxn; after singular value decomposition, a diagonal singular value matrix sigma-diag (sigma 1, sigma 2, …, sigma L) composed of L singular values is obtained, wherein L is the rank of the graph Y, and the singular values are arranged from large to small. The size of the singular value reflects different components and characteristics of the image, and the larger singular value is related to a signal in the image; in contrast, smaller singular values correspond to noise in the image. By setting a threshold value sigmathAnd eliminating the singular value lower than the threshold value, and reconstructing the image by using the reserved singular value and the corresponding singular vector to realize denoising. Compared with other denoising methods, the method can better retain the edge characteristics of image information while denoising.
Adaptive threshold σthIs selected by
Reserve greater than or equal to σthReconstructing an image to realize denoising according to the singular value of the image; the reconstructed image after being denoised is Y'm×n≈Um×m∑′m×nVn×n TWherein the singular value matrix is sigma'm×n＝diag(σ1,...σth)。
And secondly, extracting the characteristics of the denoised original low-resolution image. The invention adopts a method combining a Gabor filter and morphological change to realize the process; the method specifically comprises the following steps:
2.1), the characteristics of the Gabor filter are similar to the human visual mechanism, and the Gabor filter is used for extracting the image characteristics.
The two-dimensional gabor filter is
Where (i, j) is the spatial pixel, σiAnd σjIs the standard deviation, f0Is the spatial domain frequency. If the original coordinate system i 'icos θ + jssin θ is transformed, and j' isin θ + jcos θ, a plurality of directional gabor filters can be obtained by adjusting the angle parameter θ.
The method selects 6 different angle directions to construct a gabor filter, and performs convolution operation on the gabor filter and the denoised image respectively to obtain a series of characteristic graphs F (i, j) ═ Fθ(i,j)|θ＝0°,30°,60°,90°,120°,150°}。
Wherein Fθ(i,j)＝Y'(i,j)*gθ(i,j)，gθA gabor filter at an angle theta.
And fusing the characteristic graphs in different directions, and adopting different weight coefficients.
Coefficient of
Wherein, M (F)θ(i, j)) is a feature map Fθ(i, j) mean square error.
The feature map fusion result is
2.2) combining a morphological method to further enhance the extracted characteristic map.
The feature map is further enhanced using a morphology-based approach.
The method comprises
Wherein,and Θ represents expansion, corrosion operations; b is a morphological operation template, u and v are intra-template elements; meanwhile, gradient information is considered in the morphological transformation process, wherein F ″ (i-u, j-v) ═ F + Δ F') (i-u, j-v) is used for adding the gradient information in the original image; f "(i + u, j + v) ═ F- Δ F') (i + u, j + v) is to subtract the gradient information from the original image;
the enhanced signature is F ".
And thirdly, training by using a feature map of the low-resolution image to obtain a sparse expression coefficient, and training and learning by using an over-complete dictionary to obtain a high-resolution image.
For the feature map F' after enhancement in the low-resolution dictionary DlThe decomposition is carried out. Since the feature map F' has sufficient sparsity, L is adopted1The norm is solved as follows.
min||αl||1s.t.||Dlαl-F”||≤ξ
Wherein, αlFor sparse coding from the low resolution profile, s.t. represents constraints, ξ is the first positive real number set.
The above formula is optimized to ensure the continuity and consistency of the reconstructed images
min||α||1s.t.||Dlα-F”||≤ξ1
||PDhα-Q||≤ξ2
Wherein Q is the part overlapped with the previous reconstructed image block, P is the size of a dictionary which is extracted and is adaptive to the size of the overlapped part, α is the sparse code to be solved, ξ1ξ for a set second positive real number2Is a set third positive real number; dhFor a high-resolution dictionary, a Lagrange multiplier method is introduced for solving to obtain the following formula
Wherein,λ ═ 0.1 is the relaxation factor;
solving for the coefficient α, via x ═ Dhα reconstructing high-resolution image blocks and combining the image blocks to form a complete high-resolution image.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. The infrared image super-resolution reconstruction method based on feature extraction is characterized by comprising the following steps:
performing singular decomposition on an acquired original infrared image, and filtering noise interference in the original infrared image;
secondly, extracting the features of the denoised image by adopting a Gabor-based filter, and further enhancing the extracted features by using a morphology-based method;
and thirdly, performing sparse decomposition on the enhanced characteristic diagram, solving a sparse coefficient by adopting a Lagrange multiplier method, and reconstructing a super-resolution image of the original infrared image by using the solved coefficient.
2. The infrared image super-resolution reconstruction method based on feature extraction as claimed in claim 1, wherein: in the first step, the size of an original infrared image Y is mxn; obtaining a diagonal singular value matrix sigma-diag (sigma 1, sigma 2, …, sigma L) consisting of L singular values after singular value decomposition, wherein L is the rank of a graph Y, and the singular values are arranged from large to small; setting a threshold value sigmathRemoving the singular value lower than the threshold value, and reconstructing an image by using the retained singular value and the corresponding singular vector to realize denoising;
adaptive threshold σthIs selected by
The reconstructed image after being denoised is Y'm×n≈Um×m∑′m×nVn×n TWherein the singular value matrix is sigma'm×n＝diag(σ1,...σth)。
3. The infrared image super-resolution reconstruction method based on feature extraction as claimed in claim 1, wherein: in the second step, when a Gabor filter is used for feature extraction, multi-directional feature extraction is carried out; meanwhile, information covered by each characteristic diagram is considered when the characteristic diagrams are fused, and different weight coefficients are adopted for fusion.
4. The feature extraction-based infrared image super-resolution reconstruction method according to claim 3, characterized in that: the two-dimensional gabor filter is
Where (i, j) is the spatial pixel, σiAnd σjIs the standard deviation, f0Is a spatial domain frequency; if the original coordinate system i 'icos theta + jssin theta and j' isin theta + jcos theta are transformed, a plurality of directional gabor filters are obtained by adjusting the angle parameter theta.
5. The feature extraction-based infrared image super-resolution reconstruction method according to claim 3, characterized in that: selecting 6 different angle directions to construct a gabor filter, and performing convolution operation on the gabor filter and the denoised image respectively to obtain a series of characteristic graphs F (i, j) ═ Fθ(i,j)|θ＝0°,30°,60°,90°,120°,150°}；
Wherein, Fθ(i,j)＝Y'(i,j)*gθ(i,j)；gθIs a gabor filter at an angle θ; the 6 different angles are 0 °, 30 °, 60 °, 90 °, 120 ° and 150 °, respectively.
6. The feature extraction-based infrared image super-resolution reconstruction method according to claim 3, characterized in that: in the second step, the image characteristics contained in the characteristic diagrams in different directions are different, and different weight coefficients are adopted when the characteristics are fused, wherein the coefficient is
Wherein, M (F)θ(i, j)) is a feature map Fθ(ii) the mean square error of (i, j);
the feature map fusion result is
7. The infrared image super-resolution reconstruction method based on feature extraction as claimed in claim 1, wherein: in the second step, the image is processed by expansion and erosion operations, the method being
⊕ and theta represent expansion and corrosion operations, B is a morphological operation template, u and v are elements in the template, and gradient information is considered in the morphological transformation process, wherein F (i-u, j-v) ═ F + DeltaF ') (i-u, j-v) is to add gradient information in the original image, and F (i + u, j + v) ═ F') (i + u, j + v) is to subtract gradient information from the original image;
f' is a characteristic diagram obtained after enhancement.
8. The infrared image super-resolution reconstruction method based on feature extraction as claimed in claim 1, wherein: the third step specifically comprises:
for the enhanced feature map F' in the low-resolution dictionary DlDecomposing; by using L1The norm is solved as follows:
min||αl||1s.t.||Dlαl-F”||≤ξ
wherein, αlS.t. represents constraint, ξ is a first positive real number set;
the above formula is optimized to ensure the continuity and consistency of the reconstructed images
min||α||1s.t.||Dlα-F”||≤ξ1
||PDhα-Q||≤ξ2
Wherein Q is the part overlapped with the previous reconstructed image block, P is the size of a dictionary which is extracted and is adaptive to the size of the overlapped part, α is the sparse code to be solved, ξ1ξ for a set second positive real number2Is a set third positive real number; dhFor a high-resolution dictionary, a Lagrange multiplier method is introduced to solve to obtain the following formula:
<mrow> <munder> <mi>min</mi> <mi>&alpha;</mi> </munder> <mo>|</mo> <mo>|</mo> <msup> <mi>D</mi> <mo>&prime;</mo> </msup> <mi>&alpha;</mi> <mo>-</mo> <mi>G</mi> <mo>|</mo> <msubsup> <mo>|</mo> <mn>2</mn> <mn>2</mn> </msubsup> <mo>+</mo> <mi>&lambda;</mi> <mo>|</mo> <mo>|</mo> <mi>&alpha;</mi> <mo>|</mo> <msub> <mo>|</mo> <mn>1</mn> </msub> <mo>;</mo> </mrow>
wherein,λ ═ 0.1 is the relaxation factor;
solving for the coefficient α, via x ═ Dhα reconstructing high-resolution image blocks and combining the image blocks to form a complete high-resolution image.
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