CN102621546B - Three-dimensional information obtaining method based on correlated imaging - Google Patents

Abstract

The invention discloses a three-dimensional information obtaining method based on correlated imaging, which includes the steps as follows: the detecting light emitted by a light source is projected to the target object after being processed through a spatial light modulator, so as to detect the reflected light from the target object; the reflected light is modulated for twice by a preprocessing unit and stored respectively; and the three-dimensional information of the target object is obtained by calculating the random image stored in a register and the modulated light intensity information. The three-dimensional information obtaining method based on the correlated imaging modulates the three-dimensional object after obtaining the light intensity signal array, only requires twice two-dimensional information calculation to obtain the three-dimensional information, and is suitable for obtaining the remote and quick three-dimensional information.

Description

3 D information obtaining method based on relevance imaging

Technical field

The invention belongs to three-dimensional information and obtain the field, specifically is a kind of three-dimensional information collection and disposal route based on the relevance imaging principle, can be used for remote three-dimensional imaging.

Background technology

Three-dimensional information obtain the fields such as three-dimensional modeling, target detection that nowadays have been widely used in, and develop out various methods, its relative merits and usable range are respectively arranged.Nowadays the popular method that has based on computer vision can be obtained three-dimensional information from the figure of two dimension, can realize obtaining wide view-field three-D information; Utilize time-of-flight method, within the specific limits target object is scanned, obtain the flight time of direct impulse when obtaining two-dimensional signal, thereby obtain the depth information of target object, its image acquisition speed and resolution are subject to sweep velocity and number of scan points; The measurement of use contact probe can measure accurately for the object realization of smaller size smaller, but equipment is comparatively expensive, and acquisition speed is slow, and easy surface generation destruction to target object; Use structured light to carry out active measurement for target object, sweep velocity is fast, and the measuring accuracy height relatively is applicable to the reasonable occasion of indoor body surface reflection case.

Method, probe contact measuring method, structured light method based on computer vision all are not suitable for obtaining of remote three-dimensional information, and time-of-flight method is subject to sweep velocity and number of scan points, and be subjected to the influence of environment in the communication process easily, remote three-dimensional information to obtain the result also undesirable, and the relevance imaging theory that development is in recent years come out, for obtaining of the three-dimensional information of remote object, has its special advantages.

The relevance imaging algorithm, it is far away to have an image-forming range, for the strong robustness of neighbourhood noise, uses the wide advantage of spectral range.

And the compressed sensing algorithm combines data acquisition and compression, can reconstruct the image of target object under the situation far below Nyquist sampling frequency.Be the real number signal x (n) of N for length, can carry out sparse conversion

Perhaps x=Ψ θ.Ψ is corresponding sparse basis array.Compressed sensing is not directly measured signal x (n), but measures y=Φ x by an accidental projection matrix Φ.Φ is the matrix of a M * N dimension, and each row is a base vector Expression is carried out the measurement of once linear to signal x (n).M represents to measure number of times, and satisfies M＜N.Because x can carry out rarefaction representation in the Ψ territory, so following formula also can be expressed as y=Φ x=Φ Ψ ^Tθ.The problem of finding the solution this equation can be expressed as the optimization problem of asking minimum 1 norm: Subject to y=Φ x=Φ Ψ ^Tθ=Θ θ.Available algorithm has basic tracing algorithm, greedy tracing algorithm, protruding relaxation method, combinational algorithm etc.

Summary of the invention

The present invention proposes the acquisition methods based on a kind of novel three-dimensional information of relevance imaging, for three-dimensional body, after obtaining the light intensity signal row, it is modulated, and only needs the calculating of twice two-dimensional signal just can obtain three-dimensional information.

A kind of 3 D information obtaining method based on relevance imaging, comprise and utilize light source to send detection light, survey and be projected to target object after light is handled through spatial light modulator, detect the reflected light from target object, determine specifically to comprise the steps: the three-dimensional information of target object according to this reflected light

(1) the total pixel number N of setting random image sets the modulating function f (x) of two light intensity signals and f ' (x);

N is the total pixel number of random image; Variable in the x general reference modulating function.

When setting the total pixel number N of random image, mainly set according to resolution requirement and the spatial light modulator specification of target object.

(2) the detection number of times M of target setting object, and generate the observing matrix that a M * N ties up, utilize this observing matrix to generate M width of cloth random image, for i width of cloth random image, can be expressed as R _i, i≤M wherein, the total pixel number of each width of cloth random image is N, R _{I, n}Represent n gray values of pixel points of i width of cloth random image, wherein n≤N;

Detect number of times M and determine that according to needed measuring accuracy precision prescribed is more high, then the M value is more big, observing matrix can use-1 of the treated Bernoulli Jacob's of satisfying distribution, 1 stochastic matrix, or satisfy-1～1 stochastic matrix of Gaussian distribution or the Hadamard matrix of stochastic sampling.

The common practices that generates M width of cloth random image is each row that extracts observing matrix, the corresponding random image that is converted to a two dimension, and M is capable altogether amounts to corresponding M width of cloth random image.

(3) load the width of cloth in the M width of cloth random image in the detection light that spatial light modulator sends to light source successively, obtain having the detection light of random image information, then be projected to target object;

(4) detection is from the catoptrical light intensity of target object, and be T each detection time, obtains signal train s in the T in detection time ₁, wherein, j data can be designated as s _{1, j}, wherein the secondary random image of 1 expression is namely measured for the first time, and j is the sequence number of each data point in the signal train;

Can or come the signal of self-pulsing laser to switch or trigger by predetermined trigger pip between the different detection time T.

(5) at signal train data s _{1, j}In get data point in the effective range, described effective range is: j1≤j≤j2, the data point in the resulting effective range is modulated the signal train after obtaining modulating;

" Y ' (1)=Y ' (1)+f (d in the time of j=j1, is carried out in (1)=0 to establish Y ' (1)=0 during modulation, Y _j) * s _{1, j}, Y " (1)=Y " (1)+f ' (d _j) * s _{1, j}Computing is till j=j2;

d _j＝c(Δt·j+Δt′)，j1≤j≤j2；

Δ t is the interval time that detector is gathered signal at every turn;

Δ t ' is the time delay of detector;

C is the light velocity;

J is the sequence number of each data point in the signal train;

J1 and j2 are the data point sequence number threshold value in the effective range;

F and f ' are the described modulating function of step (1);

In the T time since detector can be repeatedly at interval carry out signals collecting so this signal train s _{1, j}In comprised a plurality of signals (data point), the present invention only takes the data point in the effective range, the j of eligible j1≤j≤j2 thinks useful signal.

For example the maximal value of j is 1000, then mean T in the time detector gathered 1000 data points, if j1 and j2 are set at 100 and 500 respectively, then the data point in the effective range is since the 100th data point, until the 500th data point.

Because the different parts of three-dimensional target object is different with the distance of detector, therefore the data point correspondence of different sequence numbers comes from the light signal of different distance d.

(6) circulation step (3)～step (5) all sends until M width of cloth random image and finishes, and for the secondary random image of i, correspondence obtains signal train s _i, wherein, j data can be designated as s _{I, j}, i is the sequence number of random image; And carry out Y ' (i)=Y ' (i)+f (d _j) * s _{I, j}, Y " (i)=Y " (i)+f ' (d _j) * s _{I, j}Modulation, finish M detection, corresponding obtain M after modulating signal train and be expressed as Y ' and Y ":

$Y^{\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right);$

$Y^{\&prime;\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right);$

It is capable to be M among Y ' and the Y " in, the signal train after the modulation described in the step (5) that each row is corresponding is owing to be M detection, so Y ' and Y ";

(7) the data rows Y ' after the modulation is carried out the calculating of relevance imaging or compressed sensing, obtain including the data rows X ' of target object texture information and range information;

Data rows Y after the modulation " is carried out the calculating of relevance imaging or compressed sensing, included the data rows X of target object texture information and range information simultaneously ";

Because modulating function f (x) and f ' are (x) known, with X ' and X " in corresponding element carry out computing, can obtain texture information and range information, be described three-dimensional information.

For signal train data s _{I, j}Modulate and to carry out the projection relation of relevance imaging or compressed sensing as follows:

$Y^{\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)$

$=\mathrm{\&Phi;}\left(\begin{array}{c}{x}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{N,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)=\mathrm{\&Phi;}{X}^{\&prime;}$

$Y^{\&prime;\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right)$

$=\mathrm{\&Phi;}\left(\begin{array}{c}{x}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{N,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right)=\mathrm{\&Phi;}{X}^{\&prime;\&prime;}$

Can be designated as:

Y′(i)＝s _i，j1·f(d _j1)+s _i，j1+1·f(d _j1+1)+...+s _i，j2·f(d _j2)，

Y″(i)＝s _i，j1·f′(d _j1)+s _i，j1+1·f′(d _j1+1)+...+s _i，j2·f′(d _j2)，

Use the mode of relevance imaging that the information of target object is calculated, need to calculate the modulation image R that stores in the buffer unit _iIn the gray-scale value R of each pixel _{I, n}, utilize R _{I, n}With data Y ' (i) and the Y " related coefficient (i) can obtain X ' and X " after the modulation.Wherein:

$X^{\&prime;}\left(n\right)=\frac{\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;}\left(i\right)R_{i,n}-\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;}\left(i\right)\&CenterDot;\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{R}_{i,n}}{\sqrt{\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;2}\left(i\right)-{\left(\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;}\left(i\right)\right)}^2}\sqrt{\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{R}_{i,n}^2-{\left(\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{R}_{i,n}^2\right)}^2}}$

$X^{\&prime;\&prime;}\left(n\right)=\frac{\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;\&prime;}\left(i\right)R_{i,n}-\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;\&prime;}\left(i\right)\&CenterDot;\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{R}_{i,n}}{\sqrt{\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;\&prime;2}\left(i\right)-{\left(\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{Y}^{\&prime;\&prime;}\left(i\right)\right)}^2}\sqrt{\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{R}_{i,n}^2-{\left(\frac{1}{M}\underset{i=1}{\overset{M}{\mathrm{\&Sigma;}}}{R}_{i,n}^2\right)}^2}}$

Compressed sensing also is a kind of mode that image is reconstructed, and can reduce sampling number greatly, uses well below traditional number of samples reduction original signal, can reduce the signal that N ties up (M＜N) by M time measurement.

Owing to treating that image restored signal O launches to become N and ties up existence relation between the light intensity signal composition M dimensional vector y that goes vector x and measure each time: y=Φ x, the solution procedure of x can be converted into convex programming problem:

Subject to y=Φ x=Φ Ψ ^Tθ, wherein Ψ is the sparse transformation matrix of x.Utilize existing algorithm can solve x.The element of projection matrix Φ can satisfy the Hadamard matrix of Bernoulli Jacob's distribution-1,1 stochastic matrix or stochastic sampling, also can satisfy the stochastic matrix of Gaussian distribution-1～1.But because spatial light modulator can not realize the loading of negative value, at this moment need to use earlier certain method with the element among the Φ become on the occasion of.

Be positioned at the planar object at different distance place or continuous object, if only use above-mentioned method, calculated amount will be very big.Be example with the planar object that is positioned at the different distance place, what detector detected will be the data rows that a plurality of pulses are arranged, and at this moment will carry out repeatedly the calculating of reading and carry out repeatedly compressed sensing of peak value and position.And for a continuous object, what detector detected will be the data rows of a broadening, need cut into slices to data according to the asynchronism(-nization) that arrives detector, and the light intensity numerical value in each section is carried out addition.

Be shown below, for vertically being distributed in d _J1, d _J1+1..., d _J2Object in the scope, formula y=Φ x can be expressed as:

Wherein, s _{I, j}Be corresponding to distance d when measuring for the i time _jThe light intensity that receives of object distribution detector, in order to reduce calculated amount, need carry out pre-service to the signal that detects, we can multiply by the modulating function matrix F=(f (d with distance dependent at the following formula two ends _J1), f (d _J1+1) ..., f (d _J2)) ^T, multiplied result is to be shown below:

$Y^{\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)---\left(2\right)$

$=\mathrm{\&Phi;}\left(\begin{array}{c}{x}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{N,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)=\mathrm{\&Phi;}{X}^{\&prime;}$

(Y " in like manner)

Suppose X ' (i)=x _{I, j1}F (d _J1)+x _{I, j1+1}F (d _J1+1)+...+x _{I, j2}F (d _J2), by the distribution characteristics of space object as can be known, each row has only a nonzero value at most among the matrix X.Suppose the capable nonzero value x that exists of i _{I, j}≠ 0, i ∈ 1,2 ..., N}, j1≤j≤j2.The element value that X ' i is capable must be x so _{I, j}F (d _j).Calculating through relevance imaging or compressed sensing can obtain matrix X ', and each nonzero value has all comprised the depth information of space object correspondence position among the matrix X '.

Then, create another one modulating function matrix

F '=(f ' (d _J1), f ' (d _J1+1) ..., f ' (d _J2)) ^T, with top identical method, can be in the hope of matrix X ", corresponding to the capable nonzero value of i among the X '

X " the capable value of i be Since modulating function f (x) and f ' are (x) known, just can this solve depth information d _j

For example, can make f (x) ≡ 1, f ' (x)=x, the X ' that calculates has so comprised the two-dimensional signal of target object, and f ' (d _j)/f (d _j)=d _j, can ask for the depth information of corresponding element again, thereby obtain the three-dimensional information of target object.

The inventive method is reduced to 2 with the calculation times of relevance imaging and compressed sensing, has reduced calculated amount widely, for the acquisition of three-dimensional information, very big advantage is arranged.

Description of drawings

Fig. 1 is the system schematic that realizes the inventive method.

Fig. 2 is the process flow diagram of the inventive method.

Fig. 3 is the process flow diagram of modulated process in the inventive method.

Embodiment

For realizing a kind of 3 D information obtaining method based on relevance imaging of the present invention, can utilize system shown in Figure 1, system comprises pulsed laser among Fig. 1, lens combination 1, catoptron 2, spatial light modulator 3, lens combination 4, three-dimensional body (being target object), detector, and the peripheral circuit of being formed by buffer unit, pretreatment unit, storer, computing unit.

Lens combination 1 is positioned at the pulsed laser front end, the pulse laser beam (detection light) of emission enters spatial light modulator 3 through lens combination 1, catoptron 2, load random image in the detection light that spatial light modulator 3 sends to light source successively, obtain having the detection light of random image information, then be projected to target object through lens combination 4.

Detector is collected the light intensity row that different time is returned by target object, and transfers to peripheral circuit and handle.The signal train that detector is collected is not directly to store to calculate, but through pretreatment unit it is modulated earlier, adds range information.

Spatial light modulator 3 can be the digital micro-mirror array (DMD) of reflection-type, also can be the liquid crystal over silicon device (LCOS) of reflection-type, and spatial light modulator 3 is connected with buffer unit.The detector that is positioned on one side can separate with dividing plate and said apparatus, prevents from launching the interference of light intensity, and detector need be connected with pretreatment unit, the light intensity of collecting in order to processing.Buffer unit, pretreatment unit, the peripheral circuit that storage unit and computing unit constitute can be realized corresponding function by computer or single-chip microcomputer.

Referring to Fig. 2,3, a kind of 3 D information obtaining method based on relevance imaging of the present invention comprises:

(1) the total pixel number N of setting random image sets the modulating function f (x) of two light intensity signals and f ' (x);

(2) the detection number of times M of target setting object, and generate the observing matrix that a M * N ties up, utilize this observing matrix to generate M width of cloth random image;

Observing matrix can use-1,1 stochastic matrix of the treated Bernoulli Jacob's of satisfying distribution, or satisfies-1～1 stochastic matrix of Gaussian distribution, or the Hadamard matrix of stochastic sampling.

The common practices that generates M width of cloth random image is each row that extracts observing matrix, the corresponding random image that is converted to a two dimension, and M is capable altogether amounts to corresponding M width of cloth random image.

Because the lateral resolution of target object is determined that by the random image that loads each secondary random image can launch to become the one-dimensional vector that N element arranged.

(3) the pulsed laser spatial emission pulse laser beam (detection light) as light source enters spatial light modulator 3 through lens combination 1, catoptron 2, load the width of cloth in the M width of cloth random image in the detection light that spatial light modulator 3 sends to light source successively, obtain having the detection light of random image information, be projected to target object through lens combination 4;

(4) utilize the detector detection from the catoptrical light intensity of target object, be T each detection time, obtains signal train s in the T in detection time ₁, wherein, j data can be designated as s _{1, j}, wherein 1 represents the first secondary random image, j is the sequence number of each data point in the signal train;

Can or come the signal of self-pulsing laser to switch or trigger by predetermined trigger pip between the different detection time T.

(5) at signal train data s _{1, j}In get data point in the effective range, effective range is: j1≤j≤j2, in pretreatment unit, the data point in the resulting effective range is modulated the signal train after obtaining modulating;

" Y ' (1)=Y ' (1)+f (d in the time of j=j1, is carried out in (1)=0 to establish Y ' (1)=0 during modulation, Y _j) * s _{1, j}, Y " (1)=Y " (1)+f ' (d _j) * s _{1, j}Computing is till j=j2;

d _j＝c(Δt·j+Δt′)，j1≤j≤j2；

Δ t is the interval time that detector is gathered signal at every turn;

Δ t ' is the time delay of detector;

C is the light velocity;

J is the sequence number of each data point in the signal train;

J1 and j2 are the data point sequence number threshold value in the effective range;

F and f ' are the described modulating function of step (1);

In the T time since detector can be repeatedly at interval carry out signals collecting so these signal train data s _{1, j}In comprised a plurality of signals (data point), the present invention only takes the data point in the effective range, the j of eligible j1≤j≤j2 thinks useful signal.

Because the different parts of three-dimensional target object is different with the distance of detector, therefore the data point correspondence of different sequence numbers comes from the light signal of different distance d.

(6) circulation step (3)～step (5) all sends until M width of cloth random image and finishes, and for the secondary random image of i, correspondence obtains signal train s _i, wherein, j data can be designated as s _{I, j}, i is the sequence number of random image; And carry out Y ' (i)=Y ' (i)+f (d _j) * s _{I, j}, Y " (i)=Y " (i)+f ' (d _j) * s _{I, j}Modulation, finish M detection, corresponding obtain M after modulating signal train and be expressed as Y ' and Y ":

$Y^{\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right);$

$Y^{\&prime;\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right);$

It is capable to be M among Y ' and the Y " in, the signal train after the modulation described in the step (5) that each row is corresponding is owing to be M detection, so Y ' and Y "; Signal train after the modulation all is sent in the storer and preserves.

(7) the data rows Y ' after the modulation is carried out compressed sensing and calculate, obtain including the data rows X ' of target object texture information and range information;

$Y^{\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)$

$=\mathrm{\&Phi;}\left(\begin{array}{c}{x}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f\left(d_{j1}\right)+x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{N,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)=\mathrm{\&Phi;}{X}^{\&prime;}$

Data rows Y after the modulation " is carried out the calculating of compressed sensing, included the data rows X of target object texture information and range information simultaneously ";

$Y^{\&prime;\&prime;}=\left(\begin{array}{c}{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">s</figure-callout>}}_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ {\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">s</figure-callout>}}_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{M,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+s_{M,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right)$

$=\mathrm{\&Phi;}\left(\begin{array}{c}{x}_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{1,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{1,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{2,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{2,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{N,\mathrm{<figure-callout\; id="1"\; label="j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">j</figure-callout>}1+1}\&CenterDot;f^{\&prime;}\left({\mathrm{<figure-callout\; id="1"\; label="d\; j"\; filenames="HDA0000147667740000011.png,HDA0000147667740000021.png"\; state="\{\{state\}\}">d</figure-callout>}}_j\right)+...+x_{N,\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000147667740000011.png"\; state="\{\{state\}\}">j</figure-callout>}2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right)=\mathrm{\&Phi;}{X}^{\&prime;\&prime;}$

Because modulating function f (x) and f ' are (x) known, with X ' and X " in corresponding element carry out computing, can obtain texture information and range information, be described three-dimensional information.

Claims (5)

1. 3 D information obtaining method based on relevance imaging, comprise and utilize light source to send detection light, survey and be projected to target object after light is handled through spatial light modulator, detection is from the reflected light of target object, determine the three-dimensional information of target object according to this reflected light, it is characterized in that, specifically comprise the steps:

(1) the total pixel number N of setting random image sets the modulating function f (x) of two light intensity signals and f ' (x);

(2) the detection number of times M of target setting object, and generate the observing matrix that a M * N ties up, utilize this observing matrix to generate M width of cloth random image;

(3) load the width of cloth in the M width of cloth random image in the detection light that spatial light modulator sends to light source successively, obtain having the detection light of random image information, then be projected to target object;

(4) detection is from the catoptrical light intensity of target object, and be T each detection time, obtains signal train s in the T in detection time ₁, wherein, j data can be designated as s _{1, j}, wherein 1 represents the first secondary random image, j is the sequence number of each data point in the signal train;

(5) at signal train data s _{1, j}In get data point in the effective range, described effective range is: j1≤j≤j2, the data point in the resulting effective range is modulated the signal train after obtaining modulating;

" Y ' (1)=Y ' (1)+f (d in the time of j=j1, is carried out in (1)=0 to establish Y ' (1)=0 during modulation, Y _j) * s _{1, j}, Y " (1)=Y " (1)+f ' (d _j) * s _{1, j}Computing is till j=j2;

d _j＝c(Δt·j+Δt′)，j1≤j≤j2；

Δ t is the interval time that detector is gathered signal at every turn;

Δ t ' is the time delay of detector;

C is the light velocity;

J is the sequence number of each data point in the signal train;

J1 and j2 are the data point sequence number threshold value in the effective range;

F and f ' are the described modulating function of step (1);

(6) circulation step (3)～step (5) all sends until M width of cloth random image and finishes, and for the secondary random image of i, correspondence obtains signal train s _i, wherein, j data can be designated as s _{I, j}, i is the sequence number of random image; And carry out Y ' (i)=Y ' (i)+f (d _j) * s _{I, j}, Y " (i)=Y " (i)+f ' (d _j) * s _{I, j}Modulation, finish M detection, corresponding obtain M after modulating signal train and be expressed as Y ' and Y ":

$Y^{\&prime;}=\left(\begin{array}{c}{s}_{1,j1}\&CenterDot;f\left(d_{j1}\right)+s_{1,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+s_{1,j2}\&CenterDot;f\left(d_{j2}\right)\\ s_{2,j1}\&CenterDot;f\left(d_{j1}\right)+s_{2,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+s_{2,j2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,j1}\&CenterDot;f\left(d_{j1}\right)+s_{M,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+s_{M,j2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right);$

$Y^{\&prime;\&prime;}=\left(\begin{array}{c}{s}_{1,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{1,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+s_{1,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ s_{2,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{2,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+s_{2,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{M,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+s_{M,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right);$

(7) the data rows Y ' after the modulation is carried out the calculating of relevance imaging or compressed sensing, obtain including the data rows X ' of target object texture information and range information;

Data rows Y after the modulation " is carried out the calculating of relevance imaging or compressed sensing, included the data rows X of target object texture information and range information simultaneously ";

Because modulating function f (x) and f ' are (x) known, with X ' and X " in corresponding element carry out computing, can obtain texture information and range information, be described three-dimensional information.

2. the 3 D information obtaining method based on relevance imaging as claimed in claim 1 is characterized in that, it is as follows that the data rows Y ' after the modulation is carried out satisfying relation when compressed sensing is calculated:

$Y^{\&prime;}=\left(\begin{array}{c}{s}_{1,j1}\&CenterDot;f\left(d_{j1}\right)+s_{1,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+s_{1,j2}\&CenterDot;f\left(d_{j2}\right)\\ s_{2,j1}\&CenterDot;f\left(d_{j1}\right)+s_{2,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+s_{2,j2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,j1}\&CenterDot;f\left(d_{j1}\right)+s_{M,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+s_{M,j2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)$

$=\mathrm{\&Phi;}\left(\begin{array}{c}{x}_{1,j1}\&CenterDot;f\left(d_{j1}\right)+x_{1,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+x_{1,j2}\&CenterDot;f\left(d_{j2}\right)\\ x_{2,j1}\&CenterDot;f\left(d_{j1}\right)+x_{2,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+x_{2,j2}\&CenterDot;f\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ x_{N,j1}\&CenterDot;f\left(d_{j1}\right)+x_{N,j1+1}\&CenterDot;f\left(d_{j1+1}\right)+...+x_{N,j2}\&CenterDot;f\left(d_{j2}\right)\end{array}\right)=\mathrm{\&Phi;}{X}^{\&prime;}.$

3. the 3 D information obtaining method based on relevance imaging as claimed in claim 1 is characterized in that, to the data rows Y after the modulation " it is as follows to satisfy relation when carrying out the calculating of compressed sensing:

$Y^{\&prime;\&prime;}=\left(\begin{array}{c}{s}_{1,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{1,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+s_{1,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ s_{2,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{2,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+s_{2,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ s_{M,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+s_{M,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+s_{M,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right)$

$=\mathrm{\&Phi;}\left(\begin{array}{c}{x}_{1,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{1,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+x_{1,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ x_{2,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{2,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+x_{2,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ x_{N,j1}\&CenterDot;f^{\&prime;}\left(d_{j1}\right)+x_{N,j1+1}\&CenterDot;f^{\&prime;}\left(d_{j1+1}\right)+...+x_{N,j2}\&CenterDot;f^{\&prime;}\left(d_{j2}\right)\end{array}\right)=\mathrm{\&Phi;}{X}^{\&prime;\&prime;}.$

4. the 3 D information obtaining method based on relevance imaging as claimed in claim 1, it is characterized in that described observing matrix is to satisfy-1,1 stochastic matrix that Bernoulli Jacob distributes, or satisfy-1～1 stochastic matrix of Gaussian distribution or the Hadamard matrix of stochastic sampling.

5. the 3 D information obtaining method based on relevance imaging as claimed in claim 1 is characterized in that, extracts each row of observing matrix in the step (2), the corresponding width of cloth random image that is converted to.

Images