CN111416980B - High-resolution camera imaging method based on compressed coded aperture - Google Patents
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Abstract
The invention discloses a high-resolution camera imaging method based on a compressed coded aperture, which is used for solving the technical problem of lower imaging resolution of the existing high-resolution imaging method. The technical scheme includes that a set of frequency domain complementary coding aperture templates which enable the evaluation index to be maximum is searched by constructing the evaluation index of the frequency domain complementary coding aperture diaphragm and utilizing a template sequence to perform the processes of selection, intersection and variation, and the templates are mutually complementary among frequency domains, so that images shot through different designed coding aperture diaphragms are guaranteed to retain high-frequency detail information of different components in a scene, and support is provided for detail information recovery. The invention optimally designs a group of coding aperture combinations with complementary frequency domains to replace a single Gaussian coding form at the aperture diaphragm of the traditional optical system, thereby not only widening the spectral range of the aperture of the camera, but also removing redundant information of the coding aperture in frequency response, realizing the acquisition of the maximum information image at the sensing stage and improving the imaging resolution of the image.
Description
Technical Field
The invention relates to a high-resolution imaging method, in particular to a high-resolution camera imaging method based on a compressed coded aperture.
Background
In the camera imaging process, the point spread function formed by the traditional camera aperture is generally of a gaussian-like shape, which shows that high-frequency components are lost in a frequency domain and more points with zero frequency domain are contained. Scene information is incident on the detector through the aperture, and due to the low-pass characteristic of the aperture frequency domain, high-frequency information in the scene can be filtered out, so that the resolution capability of acquiring image details is reduced. However, only the high-frequency information lost in the image capturing stage is recovered by the back-end reconstruction method, which often injects unreal information into the reconstructed image, even causes reconstruction errors such as information confusion. Therefore, it is necessary to effectively design the aperture diaphragm of the camera and an effective reconstruction method, and to recover more high-frequency detail information of the scene by a computational imaging means. At present, the mainstream camera coding aperture design mainly obtains a relatively excellent coding aperture template through random or subjective judgment, and widens the spectrum range of a filter, however, frequency domain response of the coding aperture designed through random or subjective judgment has more redundant information, mutual frequency complementarity is not considered, and the retention capability of scene high-frequency information cannot be maximized. In addition, image reconstruction through sparse representation of an external dictionary generally requires a large amount of external data to train the dictionary, and correlation information between images inside the images and images of coded sequences is not considered, so that reconstruction accuracy is reduced.
The document "Robust All-in-focus Super-Resolution for Focal Stack lithography [ J ]. IEEE Transactions on Image Processing,2016: 1-1" discloses a focus Stack-based fully focused high Resolution imaging method. The method is based on the data requirement of the back end, the scene sequence image is obtained by utilizing complementary information between aperture frequency domains of cameras with different sizes, in the process of reconstructing the image at the back end, triple interpolation parametric fuzzy kernel projection is utilized, random transformation is applied to reconstruct defocusing fuzzy kernels with any depth, meanwhile, the L1 norm is used for suppressing noise, high-resolution image reconstruction is carried out on the sequence image, and the image resolution is effectively improved. Compared with the traditional single camera aperture, the method can retain richer high-frequency information in a scene, but the apertures with different sizes have larger redundancy on the frequency spectrum response of the scene information, so that more high-frequency detail information of the scene cannot be retained, and the method does not consider the non-local similar characteristics between sequence images and in the back-end reconstruction process, so that the high-frequency detail information in the scene is lost, and the resolution of an imaging image is lower.
Disclosure of Invention
In order to overcome the defect that the imaging resolution of the conventional high-resolution imaging method is low, the invention provides a high-resolution camera imaging method based on a compressed coded aperture. The method comprises the steps of constructing a frequency domain complementary coding aperture diaphragm evaluation index, utilizing a template sequence to carry out selection, intersection and variation processes, searching a group of frequency domain complementary coding aperture template sets which enable the evaluation index to be maximum, wherein the templates are mutually complementary among frequency domains, so that images shot through different designed coding aperture diaphragms are guaranteed to retain high-frequency detail information of different components in a scene, detail information contained in different image sequences are mutually complementary, and support is provided for detail information recovery. The invention optimally designs a group of coding aperture combinations with complementary frequency domains to replace a single Gaussian coding form at the aperture diaphragm of the traditional optical system, thereby not only widening the spectral range of the aperture of the camera, but also removing redundant information of the coding aperture in frequency response and realizing the acquisition of the maximum information image at the sensing stage. The method fully utilizes the correlation between different coded images and the correlation of image internal blocks to establish a back-end high-resolution image self-similarity reconstruction model, thereby improving the reconstruction precision. The limitation of physical imaging in the aspects of resolution ratio and the like is broken through, and the imaging resolution ratio of the image is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows: a high-resolution camera imaging method based on compressed coded aperture is characterized by comprising the following steps:
step one, designing a coded aperture template.
Randomly initializing M L multiplied by N binary coding template sequences, stretching each L multiplied by N binary coding template into L multiplied by N length row vectors according to a row sequence to obtain kMAnd then repeating the iteration steps 1) -3) G times. After the iteration processing is finished, selecting the L multiplied by N coding template with the maximum complementarity and the number of 0 in the binary codes being more than 50 percent of the total number as the optimal coding template combination K in the step 1)M. Where M is 4000, L is 4, N is 169, and G is 1000.
1) And (4) selecting a template sequence.
For each input LxN binary coded line vector kMFirst, it is decomposed into L vectors k of size 1 XNMiTransforming it to frequency domain by Fourier transformation to obtain KMi. For L pieces of KMiMethod for calculating frequency domain complementary size R (K) of coding template by using formula (1)M) Wherein, σ is the noise term value of 0.001, and A is the 1/f image frequency domain prior obtained by carrying out average statistics on the frequency domains of the T images.
From the calculated M R (K)M) The first P coding template combinations K corresponding to the binary codes with the maximum frequency domain complementarity and the number of 0 being more than 50 percent of the total number are selectedP400, the P coding template combinations are transformed into the space domain by inverse fourier transform, resulting in kP。
2) The template sequences are crossed.
Combining k with the P coding templates selected in the step 1)PRandomly pick 2 sequences k from themi、kjAligning the two sequences according to bits, performing operations on the two sequences bit by bit from left to right, firstly generating a random number r1 of 0-1 before each bit is operated, exchanging the numerical value of the bit of the two sequences if the random number r1 is less than q1, and otherwise, operating the next bit. After each bit of the two sequences is operated from left to right, the operation result is reserved. Repeating the sequence crossing process in the step 2) until the number of sequences is increased from P to M. Q1 is taken to be 0.2.
3) Template sequence variation.
For M sequences k obtained in step 2)MEach bit in the sequence is processed from left to right, a random number r2 of 0-1 is generated before each bit is processed, if r2 is less than q2, the bit is inverted, otherwise, the bit is not processed and is transferred to the next bit for processing until the whole sequence is processed. Q2 is taken to be 0.05.
And step two, processing the space-time coding camera system.
According to the optimal LxN coding template aperture set K obtained in the step oneMPerforming inverse Fourier transform on the k-space to obtain kMIt is decomposed into L N-length coding templates kMiL, each encoding template k is assigned to one of the other encoding templates kMiIs converted into
Two-dimensional matrix H ofiL,. 1. Cutting copper sheets with the same size according to the radius V of the aperture diaphragm of the camera lens, and designing L coded aperture templates H for the copper sheetsiThe pattern is precisely processed, and a template HiPunching the copper sheet at the point with the median value of 1, wherein the side length of each hole is
Square of (2). And respectively fixing the processed copper sheets at the aperture diaphragm of the lens of the L cameras to form the coded aperture camera.
The whole set of space-time coding camera system comprises a coded aperture camera at a collecting end and a main control computer at a processing end. The operation process of the system is to transmit the compressed coded image collected by the processed coded aperture camera to a main control computer at the rear end, and the coded image is subjected to perception decoding reconstruction through the main control computer to obtain a high-resolution image.
And step three, collecting compressed coded data.
Fixing the encoding camera on a tripod, adjusting relevant shooting parameters of the camera and keeping the parameters unchanged, replacing the processed z-th encoding lens, wherein the z is 1. The scene is marked as I, the coding template adopted by the z-th coding lens is kzThen the image acquisition process is
Where D is the down-sampling matrix of the camera, BzIn order to encode the image for the compression that is acquired,
representing the convolution operation and n is the sensor noise.
Respectively collecting L compressed coding image sequences B by using L coding lenses during each shootingzL, then the sequence of images acquired and the set k of encoding templates employedzAnd transmitting the data to a main control computer for decoding and reconstructing.
And fourthly, high-resolution perception reconstruction.
For compressed coded image sequence B transmitted to main control computerzAnd (5) carrying out step 4) -6) iteration processing, wherein the iteration number is Q-40.
4) And (4) PCA dictionary learning.
For image sequence BzExtracting image blocks with the size of n multiplied by n, clustering the image blocks into C classes by using a K-means algorithm, and establishing a PCA dictionary base psi for each classC. Wherein C is 70.
5) Non-local block sparse prior.
To BzProcessing the image to extract image blocks rho with the size of n multiplied by ntFinding an image block ρtIn clustering, using PCA dictionary base psi of the class in which the cluster is locatedCFinding corresponding sparse representation coefficients
ΨC' representing dictionary base ΨCThe transposing of (1). For image block rhotAccording to exp (- | ρ) in the image where it is locatedt-ρt,qI | l) find q, q ═ 1,., 12 similar image blocks ρt,qForm a set omegat. For q similar image blocks ρt,qCorresponding sparsity alphat,qWeighting and summing according to the formulas (2) and (3) to obtain non-local sparse prior betat. Wherein n is 7.
6) And solving sparse coefficients.
For each image BzOptimizing and solving sparse representation coefficient alpha according to formula (4)ZBased on the obtained coefficient alphaZThen, the image is reconstructed
If the number of iterations<Q, then go to step 4) and order
Wherein phiZFor coding a template kzThe convolution is converted into a matrix multiplication expression, and lambda is equal to gamma is equal to 0.5.
The invention has the beneficial effects that: the method comprises the steps of constructing a frequency domain complementary coding aperture diaphragm evaluation index, utilizing a template sequence to carry out selection, intersection and variation processes, searching a group of frequency domain complementary coding aperture template sets which enable the evaluation index to be maximum, wherein the templates are mutually complementary among frequency domains, so that images shot through different designed coding aperture diaphragms are guaranteed to retain high-frequency detail information of different components in a scene, detail information contained in different image sequences are mutually complementary, and support is provided for detail information recovery. The invention optimally designs a group of coding aperture combinations with complementary frequency domains to replace a single Gaussian coding form at the aperture diaphragm of the traditional optical system, thereby not only widening the spectral range of the aperture of the camera, but also removing redundant information of the coding aperture in frequency response and realizing the acquisition of the maximum information image at the sensing stage. The method fully utilizes the correlation between different coded images and the correlation of image internal blocks to establish a back-end high-resolution image self-similarity reconstruction model, thereby improving the reconstruction precision. The limitation of physical imaging in the aspects of resolution ratio and the like is broken through, and the imaging resolution ratio of the image is improved.
The present invention will be described in detail with reference to the following embodiments.
Detailed Description
The high-resolution camera imaging method based on the compressed coded aperture specifically comprises the following steps:
step one, designing a coded aperture template.
Randomly initializing M L multiplied by N binary coding template sequences (only containing 0,1), stretching each L multiplied by N binary coding template into L multiplied by N length row vectors according to a row sequence to obtain kMAnd then repeating the iteration steps 1) -3) G times. After the iteration processing is finished, selecting the L multiplied by N coding template with the maximum complementarity and the number of 0 in the binary codes being more than 50 percent of the total number as the optimal coding template combination K in the step 1)M. Here, M is 4000, L is 4, N is 169, and G is 1000.
1) And (4) selecting a template sequence.
For each input LxN binary coded line vector kMIt is first decomposed into L vectors k of size 1 XNMiTransforming it to frequency domain by Fourier transformation to obtain KMi. For L pieces of KMiMethod for calculating frequency domain complementary size R (K) of coding template by using formula (1)M) Wherein, σ is the noise term value of 0.001, and A is the 1/f image frequency domain prior obtained by carrying out average statistics on the frequency domains of the T images.
From the calculated M R (K)M) The first P coding template combinations K corresponding to the binary codes with the maximum frequency domain complementarity and the number of 0 being more than 50 percent of the total number are selectedP(P1.., 400), the P coding template combinations are transformed into the spatial domain by inverse fourier transform, resulting in kP(P＝1,...,400)。
2) The template sequences are crossed.
Combining k with the P coding templates selected in the step 1)P(P1.., 400), from which 2 sequences k were randomly choseni、kjAligning the two sequences according to bits, performing operations on the two sequences bit by bit from left to right, firstly generating a random number r1 of 0-1 before each bit is operated, and if the random number r1 is less than q1, intersecting the random number r1 with the random number r1And (4) converting the value of the bit of the two sequences, and otherwise, operating on the next bit. After each bit of the two sequences is operated from left to right, the operation result is reserved. Repeating the sequence crossing process in the step 2) until the number of sequences is increased from P to M. Q1 is taken to be 0.2.
3) Template sequence variation.
For M sequences k obtained in step 2)MEach bit in the sequence is processed from left to right, a random number r2 of 0-1 is generated before each bit is processed, if r2 is less than q2, the bit is inverted, otherwise, the bit is not processed and is transferred to the next bit for processing until the whole sequence is processed. Q2 is taken to be 0.05.
And step two, processing the space-time coding camera system.
According to the optimal LxN coding template aperture set K obtained in the step oneMPerforming inverse Fourier transform on the k-space to obtain kMIt is decomposed into L N-length coding templates kMi(i ═ 1.. L), each encoding template k is codedMi(i ═ 1.. L) to
Two-dimensional matrix H ofiL (i ═ 1.. times). Cutting copper sheets with the same size according to the radius V of the aperture diaphragm of the camera lens, and designing L coded aperture templates H for the copper sheetsi(i ═ 1.. L) pattern was subjected to precision machining, and a template H was formedi(i ═ 1.. L) points with a median value of 1 were punched into the copper sheet, each hole having a side length of 1
Square of (2). And respectively fixing the processed copper sheets at the aperture diaphragm of the lens of the L cameras to form the coded aperture camera.
The whole set of space-time coding camera system comprises a coded aperture camera at a collecting end and a main control computer at a processing end. The operation process of the system is to transmit the compressed coded image collected by the processed coded aperture camera to a main control computer at the rear end, and the coded image is subjected to perception decoding reconstruction through the main control computer to obtain a high-resolution image.
And step three, collecting compressed coded data.
Fixing the encoding camera on a tripod, adjusting relevant shooting parameters of the camera and keeping the parameters unchanged, replacing a processed z (z is 1.. L) encoding lens, and shooting a real scene by using the encoding camera. The scene is denoted as I, and the encoding template adopted by the z (z ═ 1.. L) th encoding shot is kzThen the image acquisition process is
Where D is the down-sampling matrix of the camera, BzIn order to encode the image for the compression that is acquired,
representing the convolution operation and n is the sensor noise.
Respectively collecting L compressed coding image sequences B by using L coding lenses during each shootingz(z 1.. L), and then the sequence of acquired images and the set of employed encoding templates kzAnd transmitting the data to a main control computer for decoding and reconstructing.
And fourthly, high-resolution perception reconstruction.
For compressed coded image sequence B transmitted to main control computerz(z ═ 1.. L) are subjected to steps 4) -6) of the iterative process, the number of iterations being Q ═ 40.
4) And (4) PCA dictionary learning.
For image sequence Bz(z ═ 1.. L) image blocks with the size of n multiplied by n are extracted, the image blocks are gathered into C classes by utilizing a K-means algorithm, and PCA dictionary bases Ψ are established for each classC. Wherein C is 70.
5) Non-local block sparse prior.
To BzProcessing an image (z ═ 1.. gtorel), and extracting an image block ρ of the image size n × ntFinding an image block ρtIn clustering, using PCA dictionary base psi of the class in which the cluster is locatedCFinding corresponding sparse representation coefficients
ΨC' meansDictionary base ΨCThe transposing of (1). For image block rhotAccording to exp (- | ρ) in the image where it is locatedt-ρt,q| | l) find q (q ═ 1.., 12) similar image blocks ρt,qForm a set omegat. For q (q ═ 1.., 12) similar image blocks ρt,qCorresponding sparse sparsity at,qWeighting and summing according to the formulas (2) and (3) to obtain non-local sparse prior betat. Wherein n is 7.
6) And solving sparse coefficients.
For each image Bz(z ═ 1.. cndot.l) sparse representation coefficient α is optimized and solved according to equation (4)ZBased on the obtained coefficient alphaZThen, the image is reconstructed
If the number of iterations<Q, then go to step 4) and order
Wherein phiZFor coding a template kzThe convolution is converted into a matrix multiplication expression, and lambda is equal to gamma is equal to 0.5.
Claims (1)
1. A high resolution camera imaging method based on compressed coded aperture, comprising the steps of:
designing a coded aperture template;
randomly initializing M L multiplied by N binary coding template sequences, and stretching each L multiplied by N binary coding template into L multiplied by N binary coding template sequences according to a line sequenceThe L x N length of the row vector yields kMThen repeating the steps 1) -3) G times; after the iteration processing is finished, selecting the L multiplied by N coding template with the maximum complementarity and the number of 0 in the binary codes being more than 50 percent of the total number as the optimal coding template combination K in the step 1)M(ii) a Where M is 4000, L is 4, N is 169, G is 1000;
1) selecting a template sequence;
for each input LxN binary coded line vector kMFirst, it is decomposed into L vectors k of size 1 XNMiTransforming it to frequency domain by Fourier transformation to obtain KMi(ii) a For L pieces of KMiMethod for calculating frequency domain complementary size R (K) of coding template by using formula (1)M) Wherein, the value of the noise term is 0.001 for sigma, and the frequency domain prior of the 1/f image obtained by carrying out average statistics on the frequency domains of the T images is A;
from the calculated M R (K)M) The first P coding template combinations K corresponding to the binary codes with the maximum frequency domain complementarity and the number of 0 being more than 50 percent of the total number are selectedP400, the P coding template combinations are transformed into the space domain by inverse fourier transform, resulting in kP(ii) a Wherein F (δ) represents the frequency of the pulse function;
2) crossing template sequences;
combining k with the P coding templates selected in the step 1)PRandomly pick 2 sequences k from themi、kjAligning the two sequences according to bits, performing bit-by-bit operation on the two sequences from left to right, firstly generating a random number r1 of 0-1 before each bit is operated, exchanging the numerical value of the bit of the two sequences if the random number r1 is less than q1, and otherwise, operating the next bit; after each bit of the two sequences is operated from left to right, the operation result is reserved; repeating the sequence crossing process in the step 2) until the number of sequences is increased from P to M; taking q1 as 0.2;
3) template sequence variation;
to stepM sequences k obtained in step 2)MProcessing each bit from left to right, generating a random number r2 of 0-1 before each bit is processed, if r2 is less than q2, inverting the bit, otherwise, not processing the bit, and switching to the next bit for processing until the whole sequence is processed; taking q2 as 0.05;
step two, processing the space-time coding camera system;
according to the optimal LxN coding template aperture set K obtained in the step oneMPerforming inverse Fourier transform on the k-space to obtain kMIt is decomposed into L N-length coding templates kMiL, each encoding template k is assigned to one of the other encoding templates kMiIs converted into
Two-dimensional matrix H ofiL, · i ═ 1; cutting copper sheets with the same size according to the radius V of the aperture diaphragm of the camera lens, and designing L coded aperture templates H for the copper sheetsiThe pattern is precisely processed, and a template HiPunching the copper sheet at the point with the median value of 1, wherein the side length of each hole is
Square of (2); respectively fixing the processed copper sheets at the aperture diaphragms of the lens of the L cameras to form a coded aperture camera;
the whole set of space-time coding camera system comprises a coded aperture camera at an acquisition end and a main control computer at a processing end; the operation flow of the system is that the compressed coded image collected by the processed coded aperture camera is transmitted to a main control computer at the rear end, and the coded image is subjected to perception decoding reconstruction through the main control computer to obtain a high-resolution image;
thirdly, collecting compressed coded data;
fixing the encoding camera on a tripod, adjusting relevant shooting parameters of the camera and keeping the parameters unchanged, replacing the processed z-th encoding lens, wherein the z is 1.. L encoding lenses, and shooting a real scene by using the encoding camera; the scene is marked as I, the coding template adopted by the z-th coding lens is kzThen the image acquisition process is
Where D is the down-sampling matrix of the camera, BzIn order to encode the image for the compression that is acquired,
representing a convolution operation, n is sensor noise;
respectively collecting L compressed coding image sequences B by using L coding lenses during each shootingzL, then the sequence of images acquired and the set k of encoding templates employedzTransmitting to a main control computer for decoding and reconstruction processing;
step four, high-resolution perception reconstruction;
for compressed coded image sequence B transmitted to main control computerzCarrying out step 4) -6) iteration processing, wherein the iteration number is Q-40;
4) learning a PCA dictionary;
for image sequence BzExtracting image blocks with the size of n multiplied by n, clustering the image blocks into C classes by using a K-means algorithm, and establishing a PCA dictionary base psi for each classC(ii) a Wherein C is 70;
5) non-local block sparse prior;
to BzProcessing the image to extract image blocks rho with the size of n multiplied by ntFinding an image block ρtIn clustering, using PCA dictionary base psi of the class in which the cluster is locatedCFinding corresponding sparse representation coefficients
ΨC' representing dictionary base ΨCTransposing; for image block rhotAccording to exp (- | ρ) in the image where it is locatedt-ρt,qI | l) find q, q ═ 1,., 12 similar image blocks ρt,qForm a set omegat(ii) a For q similar image blocks ρt,qCorresponding sparsity alphat,qWeighting and summing according to the formulas (2) and (3) to obtain non-local sparse prior betat(ii) a Wherein n is 7;
6) solving sparse coefficients;
for each image BzOptimizing and solving sparse representation coefficient alpha according to formula (4)zBased on the obtained coefficient alphazThen, the image is reconstructed
Where Ψ denotes the image to be reconstructed
A corresponding PCA dictionary base which is obtained by corresponding the PCA dictionary base Ψ corresponding to each image blockCIs obtained by arranging according to rows; if the number of iterations<Q, then go to step 4) and order
L,. z is 1; wherein phizFor coding a template kzConversion from convolution to a representation of matrix multiplication, betazIs an image BzThe non-local sparse prior β of each image block in (1) is obtained by equation (2)tArranged in rows, λ ═ γ ═ 0.5;
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