CN106405546B - Rapid correlation imaging system and method based on compressed speckles - Google Patents

Rapid correlation imaging system and method based on compressed speckles Download PDF

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CN106405546B
CN106405546B CN201610979352.5A CN201610979352A CN106405546B CN 106405546 B CN106405546 B CN 106405546B CN 201610979352 A CN201610979352 A CN 201610979352A CN 106405546 B CN106405546 B CN 106405546B
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speckles
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时东锋
王英俭
黄见
苑克娥
胡顺星
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Hefei Institutes of Physical Science of CAS
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Abstract

The invention discloses a rapid correlation imaging system and method based on compressed speckles, which combine a compression sampling technology, a correlation imaging technology and a sparse information restoration technology, utilize the compressed speckles to perform compression sampling on scene information, and utilize the compressed speckles and detection intensity to restore the scene compressed information. The data volume is reduced by compressing the speckle, and the imaging speed of the associated imaging system is improved. Finally, the complete and accurate scene information is restored by utilizing the scene compression information through compressed sensing, and the method has wide application value in the field of associated imaging.

Description

Rapid correlation imaging system and method based on compressed speckles
Technical Field
The invention relates to the technical field of correlated imaging, in particular to a rapid correlated imaging system and method based on compressed speckles.
Background
In recent years, correlation imaging technology has been studied, and a traditional correlation imaging system irradiates a scene with speckle light, acquires a scene echo signal by using a single-pixel detection and acquisition system, and acquires scene image information by performing correlation operation by using speckle light distribution information and detection intensity information. The system is developing towards practical engineering application, but the system needs many times of illumination and collection of different speckle lights to effectively acquire scene image information, so that the real-time imaging capability of the system is greatly restricted. Currently, improving the imaging speed and quality of associated imaging systems is one of the research directions in this field. By utilizing the characteristic that most natural scene image information has sparsity, the invention utilizes the compressed speckles to carry out compressed sampling on the scene information, reduces the data volume of the associated imaging system, can improve the operation speed of the associated imaging system, and has wide application prospect in the field of associated imaging.
Disclosure of Invention
The invention aims to provide a quick correlation imaging system and method based on compressed speckles so as to improve the imaging speed of the correlation imaging system.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the fast correlation imaging system and method based on the compressed speckle are characterized in that: including light source, beam expander, light modulator DMD, projection lens, convergent lens, single pixel detector and data acquisition system, the light that the light source sent shines on light modulator DMD behind the beam expander, light modulator DMD modulates light for the speckle that the modulation light field produced has the compression sparse form, can realize the compression sampling to the scene, the compression speckle that the modulation light field produced shines the scene through projection lens, scene reflection light assembles the single pixel detector through convergent lens on, single pixel detector is connected with data acquisition system, obtain the total reflection light intensity information of scene through the single pixel detector by data acquisition system.
The rapid correlation imaging system and method based on the compressed speckle are characterized in that: the light modulator DMD can modulate a light field, can generate compressed speckles, can accurately acquire the speckle distribution form through modulation information, and can detect total reflected light intensity information through the single-pixel detector.
The rapid correlation imaging system and method based on the compressed speckle are characterized in that: the compressed speckle is adopted to carry out compressed sampling on the object information, so that the data volume in the correlated imaging is effectively reduced, and the rapid correlated imaging is realized.
The quick correlation imaging method of the system is characterized in that: the method comprises the following steps:
(1) firstly fixing a compression matrix, then sequentially carrying out different sparse sampling on the basis of the compression matrix and loading the sparse sampling on a light modulator DMD to realize compression speckles, irradiating a scene by using the compression speckles, and detecting a scene reflection signal by using a single-pixel detector; the acquisition system acquires and stores reflection signals of a plurality of different compressed speckle irradiation scenes; setting a compression matrix B which is a matrix with 0,1 in the size of M multiplied by M, wherein the number of points of effective sampling 1 is N multiplied by N, and satisfying the condition M>N, data compression ratio γ being equal to N2/M2(ii) a Assume that the jth utilization sparsenessSampling matrix SjThe compressed speckles after the action with the compressed matrix B illuminate the scene, and the light intensity information acquired by the acquisition system is i, then the process can be expressed as shown in formula (1):
i＝∑M×MBSjR， (1)，
where R represents scene reflectivity information, ∑M×MMeans to sum the elements of the M x M matrix;
(2) matrix adjustment is carried out on speckles of an irradiated scene by using an effective sampling position index in the compression matrix, non-sampling points in data, namely 0-value points in the compression matrix, are removed, the data volume is greatly reduced, the operation speed of associated imaging can be effectively improved, and compressed scene information can be obtained after algorithm operation is carried out; the speckle is compressed by adopting a compression matrix B, only 1 position of a sampling point is selected, and the formula (1) is rewritten according to a matrix NxN as shown in a formula (2):
i＝∑N×NSj′R′， (2)，
wherein S isjIs' BSjThrough the rearrangement form, R' is the rearrangement form of R sampling points, and the size is NxN, namely the compression form of the original information; form k according to equation (2)1The process of sub-speckle illumination acquisition is represented in a matrix as shown in equation (3):
Figure BDA0001147783980000021
wherein the content of the first and second substances,
Figure BDA0001147783980000022
is k1The light intensity information form of the secondary collection is k1×1，
Figure BDA0001147783980000023
Is a column vector representation of R',
Figure BDA0001147783980000024
is k1Matrix representation of the second most different compressed speckles, largeSmall is k1×N2(ii) a Through the analysis of the formula, the size of the data matrix needing to be processed is N2×k1The compression ratio of the data is gamma; meanwhile, the number of the unknown numbers of R' required to be solved is much less than that of R, so that the speckle irradiation times can be reduced, and the compression degree of data quantity in an actual system is smaller than gamma;
(3) arranging the compressed scene information obtained in the step according to the effective sampling position index to obtain sparse information with the size consistent with that of the original scene, and obtaining complete and accurate scene information by adopting a sparse information restoration technology; according to the effective index information of the compression matrix
Figure BDA0001147783980000031
Sparse scene information R adjusted to size MxMB(ii) a Using compressed sensing sparse information restoration techniques from RBAnd (3) recovering complete and accurate scene information R, and solving an optimal formula as follows:
argmin[||RB-BR||2+λΦTV(R)] (4)，
in the formula (4), phiTVRepresents the total variation model function, and λ represents the smoothing coefficient.
The light modulator DMD modulates light according to a compressed speckle form to generate compressed speckles, and can realize compressed sampling of a scene. Different from the traditional correlated imaging method for realizing full sampling of scene information by using speckles, the method realizes compressed sampling of the scene information, greatly reduces the data volume, and can improve the imaging speed of the correlated imaging.
The invention has the advantages that: the method combines a compression sampling technology, an associated imaging technology and a sparse information restoration technology, reduces data volume by using compressed speckles, realizes quick acquisition of scene compressed information by using information correlation, improves the imaging speed of an associated imaging system, and finally realizes restoration of complete and accurate scene information by compressed sensing.
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FIG. 1 is a schematic structural diagram of the present invention.
Fig. 2 is a graph of experimental results of the system, wherein:
fig. 2a is an object image to be imaged in a specific implementation experiment, fig. 2b is an object sparse information map obtained by using compressed matrix information, and fig. 2c is an information map obtained by restoring sparse information by using compressed sensing.
Detailed Description
As shown in fig. 1, the device of the compressed speckle-based rapid correlation imaging system includes a light source 1, a beam expander 2, a light modulator DMD3, a projection lens 4, a converging lens 5, a single-pixel detector 6 and a data acquisition and processing system 7;
the light source 1 emits light, the light has large cross section distribution after passing through the beam expander 2, then the light beam irradiates on the light modulator DMD3 to generate compressed speckles, the compressed speckles irradiate a scene through the projection lens 4, the scene reflected light reaches the single-pixel detector 6 through the converging lens 5, and then data acquisition, storage and processing are carried out through the data acquisition and processing system 7.
Firstly, fixing a compression matrix, then sequentially carrying out different sparse sampling on the basis of the compression matrix to realize compression sparse speckles, irradiating a scene by using the compression sparse speckles, and detecting a scene reflection signal by using a single-pixel detector. The acquisition system acquires and stores the reflection signals of a plurality of different compressed sparse speckle irradiation scenes. A compression matrix B is set, which is a matrix of M × M size containing 0,1, where the number of points of valid samples 1 is N × N, satisfying the condition (M × M)>N), data compression ratio γ ═ N2/M2. Suppose that the jth utilizes a sparse sampling matrix SjThe compressed speckles acting on the compressed matrix B illuminate the scene, and the light intensity information acquired by the acquisition system is i, so the process can be expressed as:
i＝∑M×MBSjR， (1)，
where R represents scene reflectivity information, ∑M×MRepresenting the summation of the elements of an M x M matrix.
The effective sampling position index in the compression matrix is utilized to perform matrix adjustment on the relevant compression speckles of the irradiation scene, non-sampling points in the data, namely 0-value points in the compression matrix, are removed, the data volume is greatly reduced, the operation speed of the correlation imaging can be effectively improved, and the compression scene information can be obtained after algorithm operation is performed. The speckle is compressed by adopting a compression matrix B, only 1 position of a sampling point is selected, and the rewriting of the formula (1) according to a matrix NxN is as follows:
i＝∑N×NSj′R′， (2)，
wherein S isjIs' BSjAfter the rearrangement form, R' is the rearrangement form of R sampling points, and the size is NXN, namely the compression form of the original information. Form k according to equation (2)1The process of sub-speckle illumination acquisition is represented by a matrix as:
Figure BDA0001147783980000041
wherein the content of the first and second substances,
Figure BDA0001147783980000042
is k1The light intensity information form of the secondary collection is k1×1，
Figure BDA0001147783980000043
Is a column vector representation of R',
Figure BDA0001147783980000044
is k1Matrix representation of sub-different compressed sparse speckles of size k1×N2. Through the analysis of the formula, the size of the data matrix needing to be processed is M when the traditional method is adopted to carry out algorithm solution2×k1And the size of the data matrix to be processed by using the method is N2×k1The compression ratio of the data is γ. Meanwhile, the number of the unknown numbers of R' required to be solved is much smaller than that of R, so that the speckle irradiation times can be reduced, and the compression degree of the data volume in an actual system is smaller than gamma.
Utilizing the compressed scene information obtained in the above steps to index into the scene according to the effective sampling positionThe line arrangement obtains sparse information with the same size as the original scene, and complete and accurate scene information can be obtained by adopting a sparse information restoration technology. According to the effective index information of the compression matrix
Figure BDA0001147783980000045
Sparse scene information R adjusted to size MxMB. Using compressed sensing sparse information restoration techniques from RBAnd (3) recovering complete and accurate scene information R, and solving an optimal formula as follows:
argmin[||RB-BR||2+λΦTV(R)] (4)，
here,. phi.,. phi.TVRepresents the Total Variation (TV) model function and λ represents the smoothing coefficient.
It will be apparent to those skilled in the art that modifications and variations can be made in the compressed speckle correlation imaging system to which the present invention relates without departing from the spirit or scope of the invention. Thus, it is intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.
Results of the experiment
In order to verify the feasibility of the invention, a system was designed and an experiment was performed, and the experimental results are shown in fig. 2. The object to be imaged in the experiment is shown in fig. 2a, with an imaging resolution of 80 x 80. The compression matrix is used with an effective number of samples of 4096, i.e. a compression ratio of 0.64. The object is irradiated 3400 times with compressed sparse speckle, and the object sparse information obtained with the compressed matrix information is shown in fig. 2 b. Finally, information obtained by restoring the sparse information by adopting compressed sensing is shown in fig. 2c, and the result shows that the method can effectively acquire object information.

Claims (1)

1. The fast correlation imaging method based on the compressed speckle is characterized in that: the method is realized by a rapid correlation imaging system based on compressed speckles, the system comprises a light source, a beam expander, an optical modulator DMD, a projection lens, a convergent lens, a single-pixel detector and a data acquisition system, light emitted by the light source is irradiated onto the optical modulator DMD after passing through the beam expander, the optical modulator DMD modulates the light, so that the speckles generated by a modulated light field have a compressed sparse form, the compressed sampling of a scene can be realized, the compressed speckles generated by the modulated light field irradiate the scene through the projection lens, scene reflected light is converged onto the single-pixel detector through the convergent lens, the single-pixel detector is connected with the data acquisition system, and the data acquisition system acquires the total reflected light intensity information of the scene through the single-pixel detector;
the light modulator DMD can modulate the light field, can generate compressed speckles, can accurately acquire the distribution form of the speckles through modulation information, and can detect total reflected light intensity information through a single-pixel detector;
the compressed speckle is adopted to carry out compressed sampling on the object information, so that the data volume in the correlated imaging is effectively reduced, and the rapid correlated imaging is realized;
the rapid correlation imaging method based on the compressed speckles comprises the following steps:
(1) firstly fixing a compression matrix, then sequentially carrying out different sparse sampling on the basis of the compression matrix and loading the sparse sampling on a light modulator DMD to realize compression speckles, irradiating a scene by using the compression speckles, and detecting a scene reflection signal by using a single-pixel detector; the acquisition system acquires and stores reflection signals of a plurality of different compressed speckle irradiation scenes; setting a compression matrix B which is a matrix with 0,1 in the size of M multiplied by M, wherein the number of points of effective sampling 1 is N multiplied by N, and satisfying the condition M>N, data compression ratio γ being equal to N2/M2(ii) a Suppose that the jth utilizes a sparse sampling matrix SjThe compressed speckles after the action with the compressed matrix B illuminate the scene, and the light intensity information acquired by the acquisition system is i, then the process can be expressed as shown in formula (1):
i＝∑M×MBSjR， (1)，
where R represents scene reflectivity information, ∑M×MMeans to sum the elements of the M x M matrix;
(2) matrix adjustment is carried out on compressed speckles of an irradiation scene by using effective sampling position indexes in the compressed matrix, non-sampling points in data, namely 0-value points in the compressed matrix, are removed, the data volume is greatly reduced, the operation speed of correlated imaging can be effectively improved, and compressed scene information can be obtained after algorithm operation is carried out; the speckle is compressed by adopting a compression matrix B, only 1 position of a sampling point is selected, and the formula (1) is rewritten according to a matrix NxN as shown in a formula (2):
i＝∑N×NS′jR′， (2)，
wherein, S'jIs BSjThrough the rearrangement form, R' is the rearrangement form of R sampling points, and the size is NxN, namely the compression form of the original information; form k according to equation (2)1The process of sub-speckle illumination acquisition is represented in a matrix as shown in equation (3):
Figure FDA0002673750370000021
wherein the content of the first and second substances,
Figure FDA0002673750370000022
is k1The light intensity of the secondary collection is arranged in a line with the size of k1×1，
Figure FDA0002673750370000023
Is a column vector representation of R',
Figure FDA0002673750370000024
is k1Matrix representation of sub-different compressed speckles of size k1×N2(ii) a Through the analysis of the formula, the size of the data matrix needing to be processed is N2×k1The compression ratio of the data is gamma; meanwhile, the number of the unknown numbers of R' required to be solved is much less than that of R, so that the speckle irradiation times can be reduced, and the compression degree of data quantity in an actual system is smaller than gamma;
(3) and utilizing the compressed scene information obtained in the above steps to effectively acquire the scene informationSample position indexes are arranged to obtain sparse information with the size consistent with the original scene size, and complete and accurate scene information can be obtained by adopting a sparse information restoration technology; according to the effective index information of the compression matrix
Figure FDA0002673750370000025
Sparse scene information R adjusted to size MxMB(ii) a Using sparse information recovery techniques, from RBAnd (3) recovering complete and accurate scene information R, and solving an optimal formula as follows:
argmin[||RB-BR||2+λΦTV(R)] (4)，
in the formula (4), phiTVRepresents the total variation model function, and λ represents the smoothing coefficient.
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