CN109724534B - Threshold selection method and device for iterative correlation imaging - Google Patents
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Abstract
The invention discloses a threshold selecting method and a threshold selecting device for iterative correlation imaging, which belong to the technical field of correlation imaging. The invention introduces a microcontroller to drive a servo motor and a stepping motor to finish the preparation of the pseudo-thermal light source, and the threshold selection process is finished at a computer end. The method mainly uses a K-mean value clustering method to cluster noise interference items and reconstruction beneficial items in a specific reconstruction algorithm, so as to obtain an iteration threshold, uses a reconstruction result obtained by the reconstruction algorithm as an initial value to construct an assumed noise interference item, realizes approximation of actual noise interference through iterative operation, and finally makes a difference with the actual noise, so as to achieve the effect of inhibiting the actual noise interference. In the actual measurement process, the threshold selected by the method greatly improves the reconstruction quality, and the problem of threshold selection in iterative correlation imaging can be well solved.
Description
Technical Field
The invention relates to the technical field of correlated imaging, in particular to a threshold selection method and a threshold selection device for iterative correlated imaging.
Background
As a new imaging mode, the correlated imaging has the advantages of high resolution, strong anti-interference capability, non-locality and the like, and becomes one of the hot spots of research in the field of quantum optics for nearly thirty years. The correlated imaging adopts two detectors to carry out coincidence measurement on a light field, only detects the total light intensity of light reflected or transmitted by an object without detecting the spatial distribution of the light, detects the spatial intensity distribution of the light field irradiated to an object plane by a charge coupled device at a position which is spatially symmetrical to a target object about a pseudo-thermal light source, and finally reconstructs an image of the object through correlated operation, thereby realizing the separation of detection and imaging. The correlation imaging reconstruction algorithm is an important link in the correlation imaging and plays a great role in the practical process of the correlation imaging. In recent years, researchers provide a plurality of reconstruction algorithms far superior to the traditional algorithm, but the reconstruction result still has a large amount of background noise, and the reconstruction result can be further improved through an iterative algorithm. The iterative reconstruction algorithm selects a proper threshold value on the basis of a certain algorithm, then uses a reconstruction result obtained by the reconstruction algorithm as an initial value to construct an assumed noise interference item, and realizes approximation to actual noise interference through iterative operation. Finally, the difference is made with the actual noise, so that the effect of inhibiting the interference of the actual noise is achieved. However, the threshold selection of the iterative reconstruction algorithm has only a rough interval before, and no systematic selection method exists, so that an approximate threshold can be obtained only through multiple tests.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to apply the K-means clustering method to threshold selection, thereby providing a feasible method for selecting the threshold in iterative correlation imaging and well solving the problem of threshold selection in iterative correlation imaging.
The purpose of the invention is realized by the following technical scheme:
a device for selecting a threshold value for iterative correlation imaging comprises a laser 1, a light beam expander 2, ground glass 3, a light beam splitter 4, an object to be measured 5, a convex lens 6, a barrel detector 7, a photoelectric coupling device 8, a microcontroller 9, a servo motor driver 10, a stepping motor driver 11, a servo motor 12, a stepping motor 13, a touch screen 14 and a computer 15;
the laser 1 irradiates on the rotating ground glass 3 through a beam expander 2 to generate a pseudo-thermal light source, the pseudo-thermal light source is divided into two beams of light through a beam splitter 4, the two beams of light are an object arm beam and a reference arm beam respectively, the object arm beam irradiates on an object to be detected 5, the object arm beam is imaged through a convex lens 6 and then is recorded with total light intensity by a barrel detector 7, and the reference beam irradiates on a photoelectric coupler 8 at a position of the object to be detected 5 which is spatially symmetrical with respect to the pseudo-thermal light source and then is recorded with light field distribution; the microcontroller 9 controls the servo motor 12 and the stepping motor 13 through the servo motor driver 10 and the stepping motor driver 11 respectively, the servo motor 12 controls the ground glass 3 to rotate, the stepping motor 13 controls the ground glass 3 to move longitudinally, the rotation angular velocity of the servo motor 12 and the stepping velocity of the stepping motor 13 are arranged on the touch screen 14, and the microcontroller 9 drives the stepping motor 13 to move by one step length every time the servo motor 12 rotates by one circle, so that laser irradiates any position on the ground glass; after the laser irradiation of the frosted glass is completed, the microcontroller simultaneously transmits a pulse to the barrel detector and the photoelectric coupling device so as to indicate the barrel detector and the photoelectric coupling device to start to execute a recording process, and the threshold selection process is completed by the computer 15.
The invention also aims to provide a threshold selecting method for iterative correlation imaging, which is used for generating a matrix phi and a matrix transpose phi generated by the matrix phi for the speckle field light field distribution under the condition of knowing the speckle field light field distribution, the speckle field size and the sampling timesTOr and its pseudo inverse matrix
Clustering the product result, namely calling a part which causes the reconstruction quality to be poor as a noise interference item, and calling a part which is beneficial to the reconstruction quality as a reconstruction beneficial item, so as to select a threshold value; the threshold value selection can be completed by programming a threshold value selection function in a computer.
The invention is suitable for the iterative correlation imaging of a matrix type, and the steps of the method are mainly explained by using the traditional iterative correlation imaging and the iterative pseudo-inverse correlation imaging, and the specific steps are as follows:
(1) the laser light source irradiates rotating ground glass to obtain a pseudo-thermal light source, the servo motor controls the rotation of the ground glass, the stepping motor controls the longitudinal movement of the ground glass, and the stepping motor moves by one step length every time the servo motor rotates by one circle, so that different speckle fields are generated after the ground glass is irradiated by the laser at different moments;
(2) the pseudo-thermal light source is divided into an object arm light beam and a reference arm light beam by the beam splitter; assuming that the transmission function and the spatial distribution of the object to be measured are both expressed in a two-dimensional coordinate system, x and y respectively represent an abscissa and an ordinate. After the object arm beam irradiates the object to be detected with the transmission coefficient of T (x, y), the total light intensity of the object to be detected is recorded by the barrel detector, and the total light intensity obtained by the nth detection is marked as BnMeanwhile, the light field distribution of the reference arm light beam is received by the CCD, and the n-th measurement of the light field distribution obtains a speckle field marked as In(x,y)；
(3) Arranging speckle fields obtained by N times of measurement line by line to generate an observation matrix phi, and solving a pseudo-inverse matrix of the observation matrix phi;
the reconstruction formula of the pseudo-inverse correlation imaging is as follows:
wherein phi is an observation matrix generated by arranging speckle fields obtained by N times of measurement line by line,
the matrix form of the reconstruction formula of the traditional correlation imaging is expressed as
<Bn>Is the average of the total light intensity obtained by N measurementsA value;
(4) theoretically, the result phi of the product of the observation matrix and the transpose matrix thereofTPhi or observation matrix and pseudo-inverse matrix product result thereof
The diagonal elements in (1) play a key role in the imaging quality, and the off-diagonal elements contain a large amount of noise interference. So that phi isTPhi and
written separately as follows:
ΦTΦ＝s+n
wherein s is phiTPhi or
Is a diagonal matrix of diagonal elements, n is phiTPhi or
A noise interference term matrix composed of the non-diagonal elements of (a);
the reconstruction formula of the traditional correlation imaging is
The reconstruction formula of the pseudo-inverse correlation imaging is
(5) In order to approach actual noise interference, a traditional correlation reconstruction result and a pseudo-inverse correlation imaging reconstruction result are respectively used as initial values, and an assumed noise interference item is constructed
And
then the reconstruction formulas of the traditional iterative correlation imaging and the iterative pseudo-inverse correlation imaging are respectively expressed as
Wherein n'GI，n'PGIThe threshold values of the traditional iterative correlation imaging reconstruction algorithm and the iterative pseudo-inverse correlation imaging reconstruction algorithm are respectively set.
(6) Clustering the product result of the observation matrix and the pseudo-inverse matrix thereof or the product result of the observation matrix and the transpose matrix thereof, wherein the process can be completed by writing a threshold value selection function at a computer end, and MATLAB software is used for writing the threshold value selection function, and the method specifically comprises the following steps:
the input parameters are the light field distribution of the speckle field, the picture size and the sampling times, the output parameters are threshold values, and the function part mainly completes the K-mean value clustering method to the observation matrix and the transposed product result phi thereofTPhi is the product of the observation matrix and its pseudo-inverse matrix
Off-diagonal element clustering of (2); to reduce reconstruction time andTphi and
the diagonal elements of (a) have little influence on the reconstruction quality, so only the off-diagonal elements are clustered. According to phiTPhi or
Determining the cluster number according to the distribution of each data point on the off-diagonal line, giving the initial cluster center of each class, and calculating the cluster number according to the off-diagonal data points to each initial cluster centerClustering the distance of the central point, and classifying all data points into respective categories; and obtaining a new clustering center point in the classified category according to the average value of the distances between each data point in the category and the initial clustering center point of the category. After a new clustering center point is obtained, clustering is divided again according to the distance from the non-diagonal data point to the new clustering center point until the newly determined clustering center point is not changed any more, and clustering is finished; selecting the maximum value of the minimum data subset of the clustering center as a threshold value n';
(7) and using the threshold n' in a reconstruction algorithm of the traditional iterative correlation imaging and the iterative pseudo-inverse correlation imaging. After each iterative reconstruction algorithm, the threshold value n' obtained by the method can remove a part of image background noise, and the image reconstruction quality can be greatly improved after repeated iterative reconstruction.
Further, the microcontroller controls the stepping motor to enable the laser spot to irradiate the outer edge of the ground glass, and the stepping motor moves one step length every time the rotating motor rotates one circle until the laser spot moves to the inner edge of the ground glass.
Further, the microcontroller sends a trigger pulse signal to the photoelectric coupler and the bucket detector at the same time at regular intervals to synchronously acquire data.
Further, the clustering in the step (6) adopts a K-means clustering method, and the threshold value is the maximum value of the minimum data subset in the clustering center.
Compared with the prior art, the invention has the following advantages:
in the prior art, the threshold value selection only has a rough range, a scientific selection method is not available, and only an ideal threshold value can be selected after multiple tests. The method provides a new idea for threshold selection, namely, the threshold is selected by a clustering method, the threshold reconstruction quality obtained by clustering is more ideal, the threshold selection can be completed basically by computer software, the hardware cost is low, the threshold selection is convenient and quick, the realization is simple and reasonable, and the method has a wider application prospect.
Drawings
FIG. 1 is a system diagram of iterative linked imaging threshold selection;
in the figure: the device comprises a laser 1, a light beam expander 2, ground glass 3, a light beam splitter 4, an object to be detected 5, a convex lens 6, a barrel detector 7, a photoelectric coupler 8, a microcontroller 9, a servo motor driver 10, a stepping motor driver 11, a servo motor 12, a stepping motor 13, a touch screen 14 and a computer 15;
FIG. 2 is a pseudo-inverse iterative correlation imaging
The off-diagonal element clustering effect graph;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments of an iterative pseudo-inverse correlation imaging algorithm. It is to be understood that the described embodiments are merely a few embodiments of the invention and are not to be taken as the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making innovative work, are within the scope of the protection of the present invention.
Examples
The invention provides a threshold selecting method and device for iterative correlation imaging. The device has the main functions of preparing a pseudo-thermal light source and finishing a certain specific reconstruction algorithm, such as a traditional associated imaging algorithm, a pseudo-inverse associated imaging algorithm, a differential associated imaging reconstruction algorithm and the like.
Referring to fig. 1, fig. 1 is a system diagram of iterative correlation imaging threshold selection, and includes 1, laser 2, a beam expander 3, ground glass 4, a beam splitter 5, an object to be measured 6, a convex lens 7, a barrel detector 8, a photoelectric coupler 9, a microcontroller 10, a servo motor driver 11, a step motor driver 12, a servo motor 13, a step motor 14, a touch screen 15, and a computer.
As shown in fig. 1, laser 1 passes through a beam expander 2 and then irradiates on a rotating ground glass 3 to generate a pseudo thermal light source, and the pseudo thermal light source is divided into two beams by a beam splitter 4. One beam irradiates an object to be measured 5, the object is imaged by a convex lens 6, the total light intensity is recorded by a barrel detector 7, and the other beam irradiates a photoelectric coupler 8 at a position of the target object which is spatially symmetrical with respect to the pseudo-thermal light source and then the light field distribution is recorded. The microcontroller 9 controls a servo motor 12 and a stepping motor 13 through drivers 10 and 11 respectively, the servo motor enables the ground glass to start rotating, the stepping motor enables the ground glass to move longitudinally, the rotation angular velocity of the servo motor and the stepping velocity of the stepping motor can be set on a touch screen 14, and the running speeds of the two motors are properly adjusted, so that laser can irradiate any position on the ground glass. After the laser irradiation frosted glass process is finished, the microcontroller transmits a pulse to the barrel detector and the photoelectric coupling device simultaneously, so that the barrel detector and the photoelectric coupling device are instructed to start to execute a recording process. The threshold selection process is performed at the computer terminal 15.
The invention provides a threshold selection method for iterative correlation imaging, which specifically comprises the following steps:
1. the laser light source obtains a pseudo-thermal light source by rotating the ground glass sheet, the servo motor controls the rotation of the ground glass, and the stepping motor controls the longitudinal movement of the ground glass, so as to generate different speckle fields.
2. The pseudo-thermal light source is divided into an object arm light beam and a reference arm light beam by the beam splitter; after the object arm beam irradiates the object to be detected with the transmission coefficient of T (x, y), the total light intensity of the object to be detected is recorded by the barrel detector, and the total light intensity obtained by the nth detection is marked as BnMeanwhile, the light field distribution of the reference arm light beam is received by the CCD, and the n-th measurement of the light field distribution obtains a speckle field marked as In(x,y)。
3. The reconstruction formula of the iterative pseudo-inverse correlation imaging can be expressed as
Wherein phi is an observation matrix generated by arranging speckle fields obtained by N times of measurement line by line,
is a pseudo-inverse of the observation matrix phi.
4. To approximate actual noise interference, pseudo-inverse correlation imaging reconstruction junctions are usedIf the initial value is the result, constructing a hypothetical noise interference term
The reconstruction formula for iterative pseudo-inverse correlation imaging can then be expressed as
5. And clustering the non-diagonal elements of the product result of the observation matrix and the pseudo-inverse matrix thereof. The process can be completed by compiling a threshold selection function at a computer end; wherein the input parameters are speckle field light field distribution, picture size and sampling times, the output parameters are threshold values, and the function part mainly completes the product result of K-means clustering method on observation matrix and pseudo-inverse matrix thereof
Off-diagonal elements of (2). In order to reduce reconstruction time and
the diagonal elements of (a) have little influence on the reconstruction quality, so only the off-diagonal elements are clustered. According to
Determining the clustering number according to the distribution condition of each data point on the off-diagonal line, then giving the initial clustering center of each class, and classifying the data point into the class when the distance between the off-diagonal data point and the initial clustering center point is close; and obtaining a new central point in the classified category according to the average value of the distances between each data point in the category and the initial clustering central point of the category. After a new central point is obtained, the previous step is executed again, and clustering is divided again; and (4) finishing clustering until the newly determined central point is not changed any more, and selecting the maximum value of the minimum data subset in the clustering center as a threshold value n'. FIG. 2 is a pseudo-inverse iterative correlation imaging
Off diagonal ofAnd (3) a line element clustering effect graph, wherein the speckle field size is 50 multiplied by 50 pixels, and the sampling times are 600 times. According to
The number of clusters is determined to be 3 by the distribution of each data point, and the initial cluster center of each class is set. Experiments show that the lowest category after clustering is finished is a noise interference item, and the middle and upper layers are reconstruction beneficial items, so that the maximum value of the minimum data subset of the clustering center is selected as a threshold value n'.
In summary, the method and the device for selecting the iterative correlation imaging threshold according to the present invention have the advantages that the threshold selection can be completed by computer software, the hardware cost is low, the threshold selection is convenient and fast, the implementation is simple and reasonable, and the method and the device have wide application prospects.
Claims (2)
1. A threshold selecting device for iterative correlation imaging is characterized by comprising a laser (1), a light beam expander (2), ground glass (3), a light beam splitter (4), an object to be detected (5), a convex lens (6), a barrel detector (7), a photoelectric coupler (8), a microcontroller (9), a servo motor driver (10), a stepping motor driver (11), a servo motor (12), a stepping motor (13), a touch screen (14) and a computer (15);
the laser (1) irradiates on the rotating ground glass (3) through a beam expander (2) to generate a pseudo-thermal light source, the pseudo-thermal light source is divided into two beams of light through a beam splitter (4), the two beams of light are an object arm beam and a reference arm beam respectively, the object arm beam irradiates on an object to be detected (5), total light intensity is recorded by a barrel detector (7) after imaging through a convex lens (6), and light field distribution is recorded after the reference beam irradiates on a photoelectric coupler (8) at a position of the object to be detected (5) which is spatially symmetrical with respect to the pseudo-thermal light source; the micro-controller (9) controls a servo motor (12) and a stepping motor (13) through a servo motor driver (10) and a stepping motor driver (11) respectively, the servo motor (12) controls the ground glass (3) to rotate, the stepping motor (13) controls the ground glass (3) to move longitudinally, the rotation angular velocity of the servo motor (12) and the stepping velocity of the stepping motor (13) are arranged on a touch screen (14), and the micro-controller (9) drives the stepping motor (13) to move by one step length every time the servo motor (12) rotates for one circle, so that laser irradiates any position on the ground glass; after the laser irradiation frosted glass process is finished, the microcontroller transmits a pulse to the barrel detector and the photoelectric coupling device simultaneously so as to indicate the barrel detector and the photoelectric coupling device to start to execute a recording process, and the threshold value selection process is finished in the computer (15).
2. The method for selecting the threshold of the device for iterative correlation imaging as claimed in claim 1, wherein the specific steps are as follows:
(1) the laser light source irradiates rotating ground glass to obtain a pseudo-thermal light source, the servo motor controls the rotation of the ground glass, the stepping motor controls the longitudinal movement of the ground glass, and the stepping motor moves by one step length every time the servo motor rotates by one circle, so that different speckle fields are generated after the ground glass is irradiated by the laser at different moments;
(2) the pseudo-thermal light source is divided into an object arm light beam and a reference arm light beam by the beam splitter; setting the transmission function and the spatial distribution of an object to be measured to be represented in a two-dimensional coordinate system, wherein x and y respectively represent a horizontal coordinate and a vertical coordinate; after the object arm beam irradiates the object to be detected with the transmission coefficient of T (x, y), the total light intensity of the object to be detected is recorded by the barrel detector, and the total light intensity obtained by the nth detection is marked as BnMeanwhile, the light field distribution of the reference arm light beam is received by the CCD, and the n-th measurement of the light field distribution obtains a speckle field marked as In(x,y)；
(3) Arranging speckle fields obtained by N times of measurement line by line to generate an observation matrix phi, and solving a pseudo-inverse matrix of the observation matrix phi;
the reconstruction formula of the pseudo-inverse correlation imaging is as follows:
wherein phi is an observation matrix generated by arranging speckle fields obtained by N times of measurement line by line,
is a pseudo-inverse matrix of an observation matrix phi, and p is an object to be measuredThe pixel information of (2) assuming that the original image of the object to be measured and the light field distribution of the speckle field are both p × p pixels;
the matrix form of the reconstruction formula of the traditional correlation imaging is expressed as
Wherein,<Bn>is the average of the total light intensity obtained by N measurements,
the second term of the formula tends to be constant when the measurement times are large enough, so that the quality of a reconstructed image cannot be influenced after the second term is removed;
(4) will phiTPhi and
written separately as follows:
ΦTΦ＝s+n
wherein s is phiTPhi or
Is a diagonal matrix of diagonal elements, n is phiTPhi or
A noise interference term matrix composed of the non-diagonal elements of (a);
let T ═ T (1,1), T (1,2)…,T(p,p)]TThen, the reconstruction formula of the conventional correlation imaging can be expressed as:
the reconstruction formula of pseudo-inverse correlation imaging can be expressed as:
(5) respectively using the traditional correlation reconstruction result and the pseudo-inverse correlation imaging reconstruction result as initial values to construct a hypothesis noise interference item
And
then the reconstruction formulas of the traditional iterative correlation imaging and the iterative pseudo-inverse correlation imaging are respectively expressed as
Wherein n'GI,n′PGIRespectively threshold values of a traditional iteration correlation imaging reconstruction algorithm and an iteration pseudo-inverse correlation imaging reconstruction algorithm;
(6) clustering the product result of the observation matrix and the pseudo-inverse matrix thereof or the product result of the observation matrix and the transpose matrix thereof, wherein the process can be completed by writing a threshold selection function at a computer end, and MATLAB software is used for writing the threshold selection function, and the method specifically comprises the following steps:
the input parameters are the light field distribution of the speckle field, the size of the picture and the sampling times, the output parameters are threshold values, and the threshold value selecting function completes the K-mean value clustering method to observe the sum of the matrix and the sumIts transposed product result phiTPhi or the product of the observation matrix and its pseudo-inverse matrix
Off-diagonal element clustering of (2); according to phiTPhi or
Determining the clustering number according to the distribution condition of each data point on the off-diagonal line, then giving the initial clustering center of each class, and classifying all data points into respective classes according to the distance from the off-diagonal data points to the initial clustering center; obtaining a new clustering center point in the classified category according to the average value of the distances between each data point in the category and the initial clustering center point of the category; after a new clustering center point is obtained, clustering is divided again according to the distance from the non-diagonal data point to the new clustering center point until the newly determined clustering center point is not changed any more, and clustering is finished; selecting the maximum value of the minimum data subset of the clustering center as a threshold value n';
(7) and using the threshold n' in a reconstruction algorithm of the traditional iterative correlation imaging and the iterative pseudo-inverse correlation imaging.
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