阅读说明：本技术 远距离非视域成像方法、装置、设备及介质 (Remote non-visual field imaging method, apparatus, device and medium ) 是由 徐飞虎 刘健江 黄鑫 潘建伟 于 2020-11-11 设计创作，主要内容包括：一种远距离非视域成像方法,包括：在传统非视域成像模型的基础上,考虑探测信号在传播过程中的时间展宽和空间展宽,建立非视域成像泊松分布模型,利用探测信号探测非视域空间内的探测目标,并接收经过探测目标反射回来的探测信号,探测信号包括了探测目标的反射率的信息,将经过探测目标反射回来的探测信号输入非视域成像泊松分布模型,利用三维SPIRALTAP的凸优化算法求解非视域成像泊松分布模型,获得探测目标的反射率,基于探测目标的反射率,实现对目标的三维重构。本公开还提供了的远距离非视域成像装置、电子设备和存储介质,可解决由于远距离场景下光斑发散及近红外器件精度较低导致成像分辨率降低等问题,实现全天时远距离非视域成像。(A method of distance non-field-of-view imaging, comprising: on the basis of a traditional non-visual field imaging model, considering time broadening and space broadening of detection signals in a propagation process, establishing a non-visual field imaging Poisson distribution model, detecting a detection target in a non-visual field space by using the detection signals, receiving the detection signals reflected by the detection target, inputting the detection signals reflected by the detection target into the non-visual field imaging Poisson distribution model, solving the non-visual field imaging Poisson distribution model by using a convex optimization algorithm of three-dimensional SPIRALTAP, obtaining the reflectivity of the detection target, and realizing three-dimensional reconstruction of the target based on the reflectivity of the detection target. The remote non-visual field imaging device, the electronic equipment and the storage medium can solve the problems that imaging resolution is reduced and the like due to light spot divergence in a remote scene and low precision of a near infrared device, and realize full-day remote non-visual field imaging.)

1. A method of remote non-view imaging, comprising:

establishing a non-visual field imaging Poisson distribution model based on the time broadening and the space broadening of the detection signal in the propagation process;

detecting a detection target in a non-visual field space by using the detection signal, and receiving the detection signal reflected by the detection target, wherein the detection signal comprises information of the reflectivity of the detection target;

inputting the detection signal reflected by the detection target into the non-visual field imaging Poisson distribution model, and solving the non-visual field imaging Poisson distribution model by using a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target;

and realizing three-dimensional reconstruction of the detection target based on the reflectivity of the detection target.

2. The method of claim 1, wherein the non-view imaging poisson distribution model comprises:

let s (ξ, κ, t) denote the number of photons of the detection signal reflected back through the detection target, (ξ, κ) denote the coordinates of a scanning point formed on an intervening wall when the detection signal detects the detection target, t denotes time, g_t(t) represents the temporal spread of the probe signal, g_xy(xi, κ) represents the spatial broadening of the detection signal, (x, y, z) represents the coordinates of the detection target, α represents the reflectivity of the detection target, b (t) represents the background noise, r (x, y, z; xi, κ) represents the distance of the detection target from the scanning point, and x represents the convolution sign, then the non-field-of-view imaging poisson distribution model is:

where δ (ct-2r (x, y, z; ξ, κ)) represents the response function of the detection signal.

3. The method of claim 2, further comprising:

discretizing the non-vision field imaging Poisson distribution model to obtain:

S～Poisson(G*H(α)+B)；

wherein S represents a matrix of the distribution of the number of detected photons and G represents the temporal spread G_t(t) and said spatial broadening g_xyDiscretization matrix of (xi, kappa), H (alpha) stands forA represents the reflectivity of the detection target, and B represents the discretization matrix of the background noise B (t).

4. The method of claim 2, wherein the functional representation of the temporal and spatial broadening of the probe signal comprises:

wherein the function of the temporal broadening, the spatial broadening is expressed as a Gaussian function, σ_t、σ_rRespectively its broadening.

5. The method of claim 1, wherein the inputting the detection signal reflected back through the detection object into the non-visual field imaging poisson distribution model, and the solving the non-visual field imaging poisson distribution model by using a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection object comprises:

obtaining a probability calculation model of the detection signal comprising a preset photon number based on the non-vision field imaging Poisson distribution model;

and solving the probability calculation model by using the detection signal as input and utilizing a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target, wherein when the probability value of the probability calculation model is maximum, the solution of the probability calculation model is the reflectivity of the detection target.

6. The method of claim 3 or 5, wherein the obtaining a probability calculation model that the detection signal comprises a preset photon number based on the non-visual field imaging Poisson distribution model comprises:

wherein Pr (S; G H (alpha), B) represents the joint probability of the matrix of photon number distribution being S, Pr_ij(s_ij(ii) a G, H (alpha), B) represents the number s of detected photons at the scanning point (i, j)_ijThe probability of (d);

7. the method of claim 6, wherein the solving the probabilistic calculation model using a three-dimensional SPIRALTAP convex optimization algorithm with the probe signal as an input to obtain the reflectivity of the probe object comprises:

α＝argmin L(α；S，H，G，B)+λ||α||_TV；

wherein L (alpha; S, H, G, B) represents the negative log-likelihood function of Pr (S; G H (alpha), B) | | alpha | | purple cells_TVRepresenting a smoothing term for compensating image distortion, λ being a positive real number, and adjusting the magnitude of the effect of the smoothing term, n_x、n_y、n_tThe three-dimensional size of the reflectivity matrix α is shown, and i, j, k respectively show the three-dimensional corner marks inside the matrix of α.

8. A remote non-field-of-view imaging apparatus, comprising:

the modeling module is used for establishing a non-visual field imaging Poisson distribution model based on the time broadening and the space broadening of the detection signal in the propagation process;

a detection module, configured to detect a detection object in a non-visual field space by using the detection signal, and receive the detection signal reflected by the detection object, where the detection signal includes information of reflectivity of the detection object;

the reflectivity calculation module is used for inputting the detection signal reflected by the detection target into the non-visual field imaging poisson distribution model, and solving the non-visual field imaging poisson distribution model by using a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target;

and the image reconstruction module is used for realizing three-dimensional reconstruction of the detection target based on the reflectivity of the detection target.

9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, performs the steps of the method for distance non-field of view imaging according to any of claims 1 to 7.

10. A computer-readable storage medium, having a computer program stored thereon, which, when being executed by a processor, carries out the steps of the method for distance non-field imaging according to any one of claims 1 to 7.

Technical Field

The present disclosure relates to the field of non-visual field three-dimensional imaging technologies, and in particular, to a remote non-visual field imaging method and apparatus, an electronic device, and a medium.

Background

In recent years, with the technological development of laser radars, some techniques have now realized three-dimensional imaging at kilometer-scale distances with centimeter-scale resolution. However, in some special scenes, due to the occlusion of various obstacles, it is difficult to effectively image the target and the scene hidden behind the obstacles by using a direct imaging method. In order to search, detect and observe targets behind obstacles, a three-dimensional non-visual field imaging technology based on laser radar is proposed, which can realize the tracking or imaging of hidden object targets by bypassing the obstacles through a wall surface reflecting surface with certain reflectivity. Currently, a series of active advances have been made in indoor scenarios. However, although the existing technology can realize non-visual field three-dimensional imaging of indoor close-distance hidden objects, because of the relatively large time broadening of the near-infrared band system used for long-distance imaging and the great influence of the flare divergence caused by the divergence angle of the optical device at long distance on the imaging quality, the existing three-dimensional non-visual field imaging technology still cannot realize kilometer-level non-visual field imaging.

Disclosure of Invention

In view of the above problems, the present invention provides a remote non-visual field imaging method, so as to solve the problems of the influence of time broadening caused by an infrared laser and a detector on the imaging quality, the divergence of remote detection light spots, and the like, reduce the requirements of active pulse coaxial non-visual field imaging on devices, and realize full-day remote non-visual field imaging.

The present disclosure provides, in one aspect, a method of remote non-view imaging, comprising: establishing a non-visual field imaging Poisson distribution model based on the time broadening and the space broadening of the detection signal in the propagation process; detecting a detection target in a non-visual field space by using the detection signal, and receiving the detection signal reflected by the detection target, wherein the detection signal comprises information of the reflectivity of the detection target; inputting the detection signal reflected by the detection target into the non-visual field imaging Poisson distribution model, and solving the non-visual field imaging Poisson distribution model by using a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target; and realizing three-dimensional reconstruction of the detection target based on the reflectivity of the detection target.

Optionally, the non-visual field imaging poisson distribution model comprises: let s (ξ, κ, t) denote the number of photons of the detection signal reflected back through the detection target, (ξ, κ) denote the coordinates of a scanning point formed on an intervening wall when the detection signal detects the detection target, t denotes time, g_t(t) represents the temporal spread of the probe signal, g_xy(xi, κ) represents the spatial broadening of the detection signal, (x, y, z) represents the coordinates of the detection target, α represents the reflectivity of the detection target, b (t) represents the background noise, r (x, y, z; xi, κ) represents the distance of the detection target from the scanning point, and x represents the convolution sign, then the non-field-of-view imaging poisson distribution model is:

where δ (ct-2r (x, y, z; ξ, κ)) represents the response function of the detection signal.

Optionally, the method further comprises: discretizing the non-vision field imaging Poisson distribution model to obtain:

S～Poisson(G*H(α)+B)；

wherein S represents a matrix of the distribution of the number of detected photons and G represents the temporal spread G_t(t) and said spatial broadening g_xyDiscretization matrix of (xi, kappa), H (alpha) stands forA represents the reflectivity of the detection target, and B represents the discretization matrix of the background noise B (t).

Optionally, the functional representation of the temporal and spatial broadening of the probe signal comprises:

wherein the function of the temporal broadening, the spatial broadening is expressed as a Gaussian function, σ_t、σ_rRespectively its broadening.

Optionally, the inputting the detection signal reflected by the detection target into the non-visual field imaging poisson distribution model, and solving the non-visual field imaging poisson distribution model by using a convex optimization algorithm of three-dimension SPIRALTAP to obtain the reflectivity of the detection target includes: obtaining a probability calculation model of the detection signal comprising a preset photon number based on the non-vision field imaging Poisson distribution model; and solving the probability calculation model by using the detection signal as input and utilizing a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target, wherein when the probability value of the probability calculation model is maximum, the solution of the probability calculation model is the reflectivity of the detection target.

Optionally, the obtaining, based on the non-visual field imaging poisson distribution model, a probability calculation model that the detection signal includes a preset photon number includes:

wherein Pr (S; G H (alpha), B) represents the joint probability of the matrix of photon number distribution being S, Pr_ij(S_ij(ii) a G, H (alpha), B) represents the number s of detected photons at the scanning point (i, j)_ijThe probability of (d);

optionally, the obtaining the reflectivity of the detection target by solving the probability calculation model with a three-dimensional SPIRALTAP convex optimization algorithm using the detection signal as an input includes:

α＝argmin L(α；S，H，G，B)+λ||α||_TV；

wherein L (alpha; S, H, G, B) represents the negative log-likelihood function of Pr (S; G H (alpha), B) | | alpha | | purple cells_TVRepresenting a smoothing term for compensating image distortion, λ being a positive real number, and adjusting the magnitude of the effect of the smoothing term, n_x、n_y、n_tThe three-dimensional size of the reflectivity matrix α is shown, and i, j, k respectively show the three-dimensional corner marks inside the matrix of α.

Another aspect of the present disclosure provides a long-range non-visual field imaging apparatus including: the modeling module is used for establishing a non-visual field imaging Poisson distribution model based on the time broadening and the space broadening of the detection signal in the propagation process; a detection module, configured to detect a detection object in a non-visual field space by using the detection signal, and receive the detection signal reflected by the detection object, where the detection signal includes information of reflectivity of the detection object; the reflectivity calculation module is used for inputting the detection signal reflected by the detection target into the non-visual field imaging poisson distribution model, and solving the non-visual field imaging poisson distribution model by using a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target; and the image reconstruction module is used for realizing three-dimensional reconstruction of the detection target based on the reflectivity of the detection target.

Another aspect of the present disclosure provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing the steps of the method of tele-non-field imaging according to any of the first aspects.

Another aspect of the disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any one of the distance non-field of view imaging methods of the first aspect.

The at least one technical scheme adopted in the embodiment of the disclosure can achieve the following beneficial effects:

the present disclosure provides a remote non-visual field imaging method, apparatus, electronic device, and medium, which can cope with a problem of a reduction in imaging resolution due to a low precision of a near-infrared device, and a problem of a reduction in imaging resolution due to a light spot divergence in a remote scene, reduce a requirement of active pulse coaxial non-visual field imaging on the device, and implement full-time remote non-visual field imaging.

Drawings

For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

FIG. 1 schematically illustrates a schematic diagram of a distance non-field-of-view imaging system provided by an embodiment of the disclosure;

FIG. 2 schematically illustrates a flow chart of a method of distance non-view imaging provided by an embodiment of the present disclosure;

FIG. 3 is a schematic diagram schematically illustrating test results of a distance non-viewing area imaging method provided by an embodiment of the present disclosure;

FIG. 4 is a schematic diagram schematically illustrating simulation results of a long-range non-visual-field imaging method provided by an embodiment of the disclosure when different light spots exist in a detection signal;

FIG. 5 is a schematic diagram schematically illustrating simulation results of a long-range non-field-of-view imaging method provided by an embodiment of the disclosure when detection signals include different time spreads;

FIG. 6 is a schematic diagram schematically illustrating simulation results of a distance non-viewing field imaging method in a distance experiment according to an embodiment of the disclosure;

fig. 7 schematically shows a block diagram of an electronic device provided in an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.

Accordingly, the techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable medium having instructions stored thereon for use by or in connection with an instruction execution system. In the context of this disclosure, a computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the instructions. For example, the computer readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of the computer readable medium include: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and/or wired/wireless communication links.

Fig. 1 schematically illustrates a schematic diagram of a distance non-field-of-view imaging system provided by an embodiment of the present disclosure.

As shown in fig. 1, in the remote non-visual-field imaging system, a laser emitter emits a detection signal light to an intermediate wall to form a scanning point on the intermediate wall, the scanning point is reflected by the intermediate wall to reach a detection target in a non-visual-field space behind an obstacle, the detection target reflects the detection signal back to the intermediate wall, and a detector detects the detection signal reflected back through the scanning point. The waveform in the upper left corner of fig. 1 is a time domain diagram of the detection signal detected by the detector.

In a long-distance active pulse non-visual field imaging system, the resolution of a reconstruction result in the transverse direction and the depth is not high enough due to the fact that a device cannot achieve higher precision in time, and when the time precision is low to a certain degree, an image cannot be reconstructed by an original reconstruction algorithm; in addition, because the system has a divergence angle, a large light spot can be formed under the condition of a long distance, so that the space is widened, the reconstruction result is wrong in the transverse direction, and when the light spot is large to a certain degree, the original reconstruction algorithm cannot obtain a reconstructed image. Due to the characteristics, the original reconstruction algorithm is not accurate enough, and an effective reconstruction result cannot be obtained under certain conditions.

In the embodiment of the disclosure, on the basis of an existing non-visual field imaging model, time broadening caused by devices, atmospheric environment and the like and space broadening caused by light spots are considered emphatically, a non-visual field imaging poisson distribution model is provided, and a remote non-visual field imaging method is provided based on the non-visual field imaging model to solve the problems of influence of time broadening caused by an infrared laser and a detector on imaging quality, remote detection light spot divergence and the like.

Fig. 2 schematically illustrates a flowchart of a method for distance non-view imaging provided by an embodiment of the present disclosure.

As shown in fig. 2, a distance non-viewing area imaging method provided by the embodiment of the present disclosure includes steps S210 to S240.

S210, on the basis of a traditional non-visual field imaging model, considering the time broadening and the space broadening of a detection signal in the propagation process, and establishing a non-visual field imaging Poisson distribution model.

The non-visual field imaging poisson distribution model comprises:

let s (ξ, κ, t) denote the number of photons of the detection signal reflected back through the detection target, (ξ, κ) denote the coordinates of a scanning point formed on an intervening wall when the detection signal detects the detection target, t denotes time, g_t(t) represents the temporal spread of the probe signal, g_xy(xi, κ) represents the spatial broadening of the detection signal, (x, y, z) represents the coordinates of the detection target, α represents the reflectivity of the detection target, b (t) represents the background noise, r (x, y, z; xi, κ) represents the distance of the detection target from the scanning point, and x represents the convolution sign, then the non-field-of-view imaging poisson distribution model is:

where δ (ct-2r (x, y, z; ξ, κ)) represents the response function of the detection signal.

The functional representation of the temporal and spatial broadening of the probe signal includes:

wherein the function of the temporal broadening, the spatial broadening is expressed as a Gaussian function, σ_t、σ_rRespectively its broadening.

To simplify the formulation of the model, let:

the model formula can be abbreviated as:

s(ξ，κ，t)～Poisson(h(α；ξ，κ，t)*g_t(t)*g_xy(ξ，κ)+b(t))；

in order to perform numerical calculation in a computer, when the model needs to be discretized:

S～Poisson(G*H(α)+B)；

wherein S represents a matrix of the distribution of the number of detected photons and G represents the temporal spread G_t(t) and said spatial broadening g_xyAnd (xi, kappa), H (alpha) represents the discretization matrix of H (alpha; xi, kappa, t), alpha represents the reflectivity of the detection target, and B represents the discretization matrix of the background noise B (t).

S220, detecting a detection object in a non-visual field space by using the detection signal, and receiving the detection signal reflected by the detection object, where the detection signal includes information of reflectivity of the detection object.

The specific detection method refers to the method shown in fig. 1, and is not described herein again.

And S230, inputting the detection signal reflected by the detection target into the non-visual area imaging Poisson distribution model, and solving the non-visual area imaging Poisson distribution model by using a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target.

Step S230 includes steps S231 to S232.

Step S231, obtaining a probability calculation model of the detection signal including a preset photon number based on the non-visual field imaging poisson distribution model.

And step S232, taking the detection signal as input, solving the probability calculation model by utilizing a convex optimization algorithm of three-dimensional SPIRALTAP to obtain the reflectivity of the detection target, wherein when the probability value of the probability calculation model is maximum, the solution of the probability calculation model is the reflectivity of the detection target.

Specifically, according to the discretized non-visual field imaging Poisson distribution model, the number of detected photons at a scanning point (i, j) is s_ijThe probability of (c) is:

based on the non-vision field imaging poisson distribution model, the probability that the photon number distribution matrix of the detection signal detected at each scanning point is S can be obtained, namely the probability calculation model is as follows:

wherein Pr (S; G H (alpha), B) represents the joint probability of the matrix of photon number distribution being S, Pr_ij(s_ij(ii) a G, H (alpha), B) represents the number s of detected photons at the scanning point (i, j)_ijThe probability of (c).

And when the probability value of the probability calculation model is the maximum, the reflectivity matrix of the detection target corresponding to the probability calculation model is the reflectivity of the detection target, and the reconstruction of the detection target can be realized based on the reflectivity.

In the embodiment of the present disclosure, the probability calculation model is solved by using a convex optimization algorithm of three-dimensional SPIRALTAP, and in order to maximize the probability value of the probability calculation model, there are:

α＝argmax Pr(S；G*H(α)，B)。

in the embodiment of the present disclosure, defining L (α; S, H, G, B) as a negative log-likelihood function of Pr (S; G × H (α, B), then:

α＝argmin L(α；S，H，G，B)+λ||α||_TV；

wherein L (alpha; S, H, G, B) represents the negative log-likelihood function of Pr (S; G H (alpha), B) | | alpha | | purple cells_TVRepresenting a smoothing term for compensating image distortion, λ being a positive real number, and adjusting the magnitude of the effect of the smoothing term, n_x、n_y、n_tThe three-dimensional size of the reflectivity matrix α is shown, and i, j, k respectively show the three-dimensional corner marks inside the matrix of α.

S240, based on the reflectivity of the detection target, three-dimensional reconstruction of the detection target is achieved.

There are many methods for reconstructing the detection target based on the reflectivity, and the details are not described herein.

Fig. 3 schematically illustrates a diagram of test results of a distance non-viewing area imaging method provided by an embodiment of the present disclosure.

As shown in fig. 3, in the test experiment, we imaged the hidden object in the room in the laboratory actively, (a) shows the detected real data, and it can be found that we can not get any information of the hidden object from the original data if not processed; (b) is the result of the reconstruction using the algorithm proposed by the present disclosure; (c) the reality of the hidden object is shown, and comparing it with (b) may find that the algorithm used by the present disclosure is able to reconstruct the contours and details of the hidden object.

In the disclosed embodiment, in order to verify the superiority of the present algorithm, simulations are performed using different spot sizes and time spreads, as shown in fig. 4 and 5, where fig. 4 schematically shows a schematic diagram of a simulation result of a distance non-visual field imaging method provided by an embodiment of the present disclosure when different spots exist in a detection signal, and fig. 5 schematically shows a schematic diagram of a simulation result of a distance non-visual field imaging method provided by an embodiment of the present disclosure when the detection signal includes different time spreads. The first row of images in fig. 4 and 5 both show the results obtained by the filtered-background algorithm, the second row of images in fig. 4 and 5 both show the results obtained by the algorithm without considering the temporal spatial broadening, and the third row of images in fig. 4 and 5 both show the results obtained by reconstructing the detection data by the method provided by the present disclosure.

Fig. 6 schematically illustrates a simulation result of a distance non-viewing field imaging method in a distance experiment according to an embodiment of the present disclosure.

As shown in fig. 6, in a long-distance (1.43km) experiment, the fourth column of images from left to right are images reconstructed by the method provided by the present disclosure, and the first three columns of images are reconstruction results of a comparison algorithm, and the results show that the method provided by the present disclosure can restore a clearer three-dimensional image of people and letters by reconstructing a detection signal outside the long distance (1.43km), which has certain advantages compared with other more advanced algorithms.

The present disclosure provides a remote non-visual field imaging apparatus including: the device comprises a modeling module, a detection module, a reflectivity calculation module and an image reconstruction module.

And the modeling module is used for establishing a non-visual field imaging Poisson distribution model based on the time broadening and the space broadening of the detection signal in the propagation process.

A detection module, configured to detect a detection object in a non-visual field space by using the detection signal, and receive the detection signal reflected by the detection object, where the detection signal includes information of a reflectivity of the detection object.

And the reflectivity calculation module is used for inputting the detection signal reflected by the detection target into the non-visual field imaging Poisson distribution model, and solving the non-visual field imaging Poisson distribution model by using a three-dimensional SPIRALTAP convex optimization algorithm to obtain the reflectivity of the detection target.

And the image reconstruction module is used for realizing three-dimensional reconstruction of the detection target based on the reflectivity of the detection target.

It can be understood that the remote non-visual field imaging device provided by the present disclosure includes the above-mentioned remote non-visual field imaging method, and the technical features and technical effects are the same as those of the remote non-visual field imaging method, and are not described herein again.

It is understood that the modeling module, the detection module, the reflectivity calculation module, and the image reconstruction module may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present invention, at least one of the modeling module, the detection module, the reflectivity calculation module, the image reconstruction module may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-a-package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in a suitable combination of three implementations of software, hardware, and firmware. Alternatively, at least one of the modeling module, the detection module, the reflectivity calculation module, the image reconstruction module may be at least partially implemented as a computer program module, which when executed by a computer, may perform the functions of the respective module.

Fig. 7 schematically shows a block diagram of an electronic device provided in an embodiment of the present disclosure.

As shown in fig. 7, the electronic device described in this embodiment includes: the electronic device 700 includes a processor 710, a computer-readable storage medium 720. The electronic device 700 may perform the method described above with reference to fig. 2 to enable detection of a particular operation.

In particular, processor 710 may comprise, for example, a general purpose microprocessor, an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 710 may also include on-board memory for caching purposes. Processor 710 may be a single processing unit or a plurality of processing units for performing the different actions of the method flows described with reference to fig. 2 in accordance with embodiments of the present disclosure.

Computer-readable storage medium 720 may be, for example, any medium that can contain, store, communicate, propagate, or transport the instructions. For example, a readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of the readable storage medium include: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and/or wired/wireless communication links.

The computer-readable storage medium 720 may include a computer program 721, which computer program 721 may include code/computer-executable instructions that, when executed by the processor 710, cause the processor 710 to perform a method flow such as described above in connection with fig. 2 and any variations thereof.

The computer program 721 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 721 may include one or more program modules, including 721A, modules 721B, … …, for example. It should be noted that the division and number of modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, which when executed by the processor 710, enable the processor 710 to perform the method flow described above in connection with fig. 2 and any variations thereof, for example.

According to an embodiment of the present invention, at least one of the first signal processing module, the second signal processing module, the non-viewing area spatial reflectivity calculation module, the image reconstruction module may be implemented as a computer program module as described with reference to fig. 7, which when executed by the processor 710 may implement the respective operations described above.

The present disclosure also provides a computer-readable medium, which may be embodied in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer readable medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.