A method and system for three-dimensional computed tomography of smoke

Abstract

The application discloses a three-dimensional smoke computer tomography method and system, and the method comprises the following steps: S1, acquiring a to-be-reconstructed smoke image, constructing a corresponding reconstruction region, and calculating a light list corresponding to pixels of the to-be-reconstructed smoke image; S2, creating a reconstruction sub-region list according to the light list, performing collision detection on elements of the reconstruction sub-region list, and calculating a weight matrix of the to-be-reconstructed smoke; and S3, solving the weight matrix of the to-be-reconstructed smoke, and outputting a reconstruction result of the to-be-reconstructed smoke.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of computer tomography (CT), and particularly relates to a three-dimensional smoke computer tomography method and system. BACKGROUND

[0002] Generally, three-dimensional imaging methods can be realized by computational imaging or scanning plane imaging, and the computational imaging specifically includes light field imaging and volume tomography. The light field imaging can realize single-camera instantaneous volume imaging, but at the cost of reduction of spatial resolution or reduction of field of view. The volume tomography can overcome the above-mentioned shortcomings of other methods by simultaneously capturing emitted or self-emitted projections using multiple cameras, but this method is iteratively inverted on the basis of an initial model, so the closeness of the initial model to the real structure directly relates to whether the imaging result can accurately reflect the objective material properties. How to reasonably and accurately describe the initial model is crucial.

[0003] The scanning plane imaging is realized by emitting a sheet of laser light through a laser emitter and a polarizing mirror, and a displacement mechanism is used to move the light-emitting mechanism, so that the physical field is scanned layer by layer, and the three-dimensional reconstruction of the scanning plane imaging can be completed by combining the data of each layer. However, the data of each layer obtained by this method is acquired at different times, so the reconstructed three-dimensional image cannot represent the real situation of the physical field at a certain time.

[0004] At present, there is no three-dimensional smoke reconstruction algorithm with high efficiency, high precision, saving of computing time and computing resources on the market. SUMMARY

[0005] In view of the technical problems in the prior art, the application provides a three-dimensional smoke computer tomography method and system.

[0006] According to an aspect of the application, a three-dimensional smoke computer tomography method is provided, comprising:

[0007] S1, acquiring a to-be-reconstructed smoke image, constructing a corresponding reconstruction region, and calculating a light ray list corresponding to pixels of the to-be-reconstructed smoke image;

[0008] S2, creating a reconstruction sub-region list according to the light ray list, performing collision detection on elements of the reconstruction sub-region list, and calculating a weight matrix of the to-be-reconstructed smoke;

[0009] S3, solving the weight matrix of the to-be-reconstructed smoke, and outputting a reconstruction result of the to-be-reconstructed smoke.

[0010] Preferably, S2 specifically comprises:

[0011] S201, dividing the reconstruction region into a plurality of voxels according to the number of rays in the ray list;

[0012] S202, taking a to-be-tested light from the ray list and creating a corresponding reconstruction sub-region list, taking the reconstruction region as the first element of the reconstruction sub-region list;

[0013] S203, performing collision detection on the to-be-tested light and the first element in the reconstruction sub-region list, if collision occurs, judging whether the first element is composed of only one voxel: if yes, calculating the weight of the voxel; if no, decomposing the first element and adding it to the end of the first element;

[0014] S204, removing the first element in the reconstruction sub-region list, and then judging whether there is a remaining element in the reconstruction sub-region list: if yes, repeating S203; if no, sorting all weights of the to-be-tested light according to the positions of the voxels to obtain a weight matrix of the to-be-tested light;

[0015] S205, repeating S202-S204 for all to-be-tested lights in the ray list to obtain a weight matrix of the to-be-reconstructed smoke.

[0016] By sequentially detecting whether all rays in the ray list and the voxels of the reconstruction sub-region collide through the collision algorithm, the accuracy of reconstructing the three-dimensional smoke can be improved.

[0017] Further preferably, obtaining the weight matrix of the to-be-tested light specifically includes: using the Monte Carlo algorithm for the colliding voxels to calculate the weights of the voxels, and sorting all weights of the to-be-tested light according to the positions of the voxels to obtain the weight matrix of the to-be-tested light. Using the Monte Carlo algorithm to calculate the weights of the colliding voxels, the obtained weight matrix represents the shape of the to-be-reconstructed three-dimensional smoke in a digital manner.

[0018] Preferably, solving the weight matrix of the to-be-reconstructed smoke specifically includes: calculating and correcting the projection error of the to-be-reconstructed smoke image based on the algebraic reconstruction method, obtaining the corrected smoke concentration value, and outputting the reconstruction result of the to-be-reconstructed smoke. By correcting the projection error of the weight matrix and the to-be-reconstructed smoke image through the algebraic reconstruction method, and then redistributing the error to each voxel according to the weight value to correct in turn, the reconstruction accuracy of the three-dimensional smoke can be improved.

[0019] Preferably, the weight matrix of the to-be-reconstructed smoke is calculated based on the synchronous algebra reconstruction method to calculate and correct the projection error of the to-be-reconstructed smoke image, and a TV regularization algorithm is used for processing to obtain a corrected smoke concentration value and output the reconstruction result of the to-be-reconstructed smoke. By correcting the projection error of the to-be-reconstructed smoke image by the synchronous algebra reconstruction method, and then redistributing the error to each voxel in turn according to the weight value for correction, and then iterating by the TV regularization, the influence of image noise can be reduced while reducing the projection error, thereby improving the reconstruction accuracy of the three-dimensional smoke.

[0020] Preferably, the weight matrix of the to-be-reconstructed smoke is solved, specifically including: singular value decomposition is performed on the weight matrix of the to-be-reconstructed smoke based on the truncated singular value decomposition method, and the weight matrix of the to-be-reconstructed smoke is solved based on the ridge regression method by selecting the optimal singular value number and singular vector number, and the reconstruction result of the to-be-reconstructed smoke is output. The truncated singular value decomposition method has the effect of fast calculation, and the combination of the ridge regression method and the selection of the optimal singular value number can achieve high accuracy.

[0021] Preferably, S1 specifically includes constructing a reconstruction region corresponding to the to-be-reconstructed smoke and the ray list according to the parameters of the to-be-reconstructed smoke image.

[0022] According to an aspect of the present application, a three-dimensional smoke computed tomography system is provided, comprising:

[0023] A preprocessing module: acquires a to-be-reconstructed smoke image, constructs a corresponding reconstruction region, and calculates a ray list corresponding to the pixels of the to-be-reconstructed smoke image;

[0024] A collision detection module: creates a reconstruction sub-region list according to the ray list, performs collision detection on the elements of the reconstruction sub-region list, and calculates the weight matrix of the to-be-reconstructed smoke;

[0025] A matrix solving module: solves the weight matrix of the to-be-reconstructed smoke and outputs the reconstruction result of the to-be-reconstructed smoke.

[0026] According to an aspect of the present application, a computer program product is provided, which stores a computer program, and the computer program is executed by a processor to implement the method of any one of the first aspect.

[0027] According to an aspect of the present application, a computing system is provided, comprising a processor and a memory, wherein the processor is configured to execute the method of any one of the first aspect.

[0028] Compared with the prior art, the present application has the following advantages:

[0029] The shape of the three-dimensional smoke to be reconstructed is characterized in a digital manner by detecting whether the voxels in each reconstructed sub-region collide, the real shape of the three-dimensional smoke at a certain moment is reconstructed by solving the weight matrix, and the reconstruction accuracy is improved.

[0030] The weight matrix is solved by integrating multiple algorithms, which can save calculation time and resources while improving calculation accuracy, and users can select according to actual application scenarios to meet the use requirements in various scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0031] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate the embodiments and, together with the description, serve to explain the principles of the application. Other embodiments and many of the intended advantages of the present application will be readily appreciated as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.

[0032] Figure 1 A flow chart of a method of three-dimensional smoke computed tomography according to the present application is shown;

[0033] Figure 2 A smoke image to be reconstructed according to an embodiment of the present application is shown;

[0034] Figure 3 A flow chart of collision detection according to the method proposed by the present application is shown;

[0035] Figure 4 A flow chart of solving the weight matrix of the smoke to be reconstructed according to the method proposed by the present application is shown;

[0036] Figure 5 A reconstructed smoke image obtained according to the method proposed by the present application is shown;

[0037] Figure 6 A system structure diagram of a three-dimensional smoke computed tomography according to the present application is shown;

[0038] Figure 7 A computer system structure schematic diagram of an electronic device suitable for implementing the embodiments of the present application is shown. DETAILED DESCRIPTION

[0039] The present application will be further described in detail below in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for ease of description.

[0040] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other in the case of no conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0041] Figure 1 A flow chart of a method for three-dimensional smoke computed tomography according to an embodiment of the present application is shown in the figure, which comprises:

[0042] S1, obtaining a to-be-reconstructed smoke image, constructing a corresponding reconstruction region, and calculating a ray list corresponding to the pixels of the to-be-reconstructed smoke image;

[0043] S2, creating a reconstruction sub-region list according to the ray list, performing collision detection on the elements of the reconstruction sub-region list, and calculating a weight matrix of the to-be-reconstructed smoke;

[0044] S3, solving the weight matrix of the to-be-reconstructed smoke, and outputting the reconstruction result of the to-be-reconstructed smoke.

[0045] In specific embodiments, the angles, number, and size of the input photos are not subject to any limitation, and the size of the reconstruction region and the division method of the voxels are not subject to any limitation. S1 specifically comprises constructing a reconstruction region corresponding to the to-be-reconstructed smoke according to the to-be-reconstructed smoke image, and obtaining the ray list according to the parameters (size, number, projection angle) of the to-be-reconstructed smoke image.

[0046] According to the projection angle of any one to-be-reconstructed smoke image, the local coordinate system of the image can be converted into a global coordinate system, that is, the directions of the x-axis (pixel row unchanged, column changed), y-axis (pixel column unchanged, row changed), and z-axis (perpendicular to the image plane) of the image plane in the global coordinate system are obtained. The center point of the image is placed at the origin of the global coordinate system, and according to the relative position of any pixel in the image and the center pixel, the spatial coordinates of the pixel center point can be obtained. Taking the above-mentioned x and y axis directions as the length and width respectively, and taking the above-mentioned z axis direction as the height (penetrating through the entire calculation domain), a cuboid corresponding to the light ray of the pixel in the calculation domain can be obtained. The above-mentioned operation is performed on all images and pixels to obtain the ray list.

[0047] In an optional embodiment, as shown in Figure 2 the number of to-be-reconstructed smoke images is 5, the size is 81*81 pixels, and the reconstruction region is constructed as a cube with an edge length of 82.5 pixels. At the same time, the region is discretized into 35937 cube voxels with an edge length of 2.5 pixels. The normal of the projection plane of each photo is known, so 32805 cuboid light rays can be obtained.

[0048] As shown in Figure 3As shown, these rectangular light rays are placed into a light ray list L. A beam of light to be tested is taken from the light ray list L, and a corresponding reconstructed sub-region list A is created. The reconstructed region is added to the reconstructed sub-region list as its first reconstructed sub-region, i.e., the first element. Collision detection is performed on the light to be tested and the first element of the reconstructed sub-region list A using a collision detection algorithm.

[0049] In one embodiment, the collision detection algorithm uses an obb directional bounding box and its 15 separation axes for calculation.

[0050] If a collision occurs, it is determined whether the first element consists of only one voxel. If the first element consists of only one voxel, the weight of that voxel is calculated using a Monte Carlo algorithm to obtain the weight matrix of the light to be measured. If the first element does not consist of only one voxel, the first element is decomposed and added to the end of the reconstructed sub-region list A. Figure 3 As shown, the first element is decomposed into two sub-regions. It can be understood that the decomposition method is not limited to the binary search method and can be adjusted according to actual needs.

[0051] The Monte Carlo algorithm calculates the weight value of each position by counting random points. The number of random points in the Monte Carlo algorithm ranges from 50,000 to 2 million. The larger the number is set, the more accurate the weight matrix is, but the calculation time increases.

[0052] Regardless of whether a collision occurs, after the collision detection is completed, the first element in the reconstructed sub-region list A needs to be removed, and then it needs to be determined whether there are any remaining elements in the reconstructed sub-region list A.

[0053] If so, retrieve the first new element from the reconstructed sub-region list A in sequence, and perform the collision detection steps described above with the light to be tested again. Otherwise, sort all weights of the light according to the position of the voxels, and the weight matrix calculation for the light is complete. Performing the above operations on all lights in the light list will yield the complete weight matrix.

[0054] The list of rays can be distributed to different threads for calculation through multi-threaded parallel computing.

[0055] After obtaining the weight matrix of the smoke to be reconstructed, the weight matrix is ​​solved.

[0056] like Figure 4 As shown, the computational method for solving the problem can be selected from one of the following, depending on the specific circumstances such as computation time and resources, computational accuracy, whether to use an iterative algorithm, and whether to use a noise reduction operation: Algebraic Reconstruction Method (ART algorithm), Truncated Singular Value Decomposition Method, Synchronous Algebraic Reconstruction Method (SART algorithm), or a combination of TV regularization algorithm.

[0057] In one embodiment, the weight matrix of the to-be-reconstructed smoke is solved, specifically including: singular value decomposition is performed on the weight matrix of the to-be-reconstructed smoke based on a truncated singular value decomposition method. In theory, the singular value decomposition method can decompose multiple singular values at a time and select a certain number of singular values for calculation, but in use, it is found that selecting only part of the singular values for calculation after singular value decomposition will result in a large error, for example, the error of selecting 1000 singular values for calculation after decomposing 2000 singular values is much larger than that of decomposing only 1000 singular values and using 1000 singular values for calculation, therefore, the singular value number is gradually increased from small to large during calculation of the singular value decomposition method, and singular value decomposition and equation solving are performed on each singular value number until the optimal singular value number is found. Due to the defects of the method in application, it is recommended to preferentially select the iterative algorithm in actual use.

[0058] Therefore, the present application is based on the truncated singular value decomposition method, and the optimal singular value number and singular vector number are selected based on the ridge regression method, so as to solve the weight matrix of the to-be-reconstructed smoke and output the reconstruction result of the to-be-reconstructed smoke.

[0059] The ridge regression method specifically operates as follows: a plurality of singular value numbers are selected, and the singular value number is used for solving, the two-norm ||x||2 of the solving result and the two-norm ||Ax-P||2 of the back-projection error are counted, and a graph is drawn with ||x||2 as the horizontal coordinate and ||Ax-P||2 as the vertical coordinate, and the singular value number corresponding to the point with the maximum curvature in the image is the optimal singular value number.

[0060] In one embodiment, the weight matrix of the to-be-reconstructed smoke is solved, specifically including: initializing the smoke concentration values in all voxels, calculating the projection error of the to-be-reconstructed smoke image based on an algebraic reconstruction method, and corresponding correction to obtain the corrected smoke concentration value, and outputting the reconstruction result of the to-be-reconstructed smoke.

[0061] The calculation principle of the algebraic reconstruction method is to calculate the projection error of the to-be-reconstructed smoke image row by row based on the weight matrix of the to-be-reconstructed smoke and feed back the calculation result for correction, and the physical meaning is to calculate the projection of the current reconstruction region at a certain pixel point, compare it with the to-be-reconstructed smoke image, obtain the projection error, then redistribute the error to each voxel according to the weight value, and finally each voxel is corrected according to the error value distributed.

[0062] The calculation formula is wherein, is the value in the jth voxel after the kth iteration, is the value after the (k+1)th iteration, w i，j is the weight of the ith pixel to the jth voxel, p iis the value in the i-th pixel, N is the column number of the matrix and also the number of voxels. λ is the relaxation factor, which is in the range of 0-2, and is used to control the convergence speed and the calculation accuracy. The typical value range is 0-1. The larger the number, the faster the calculation, but the error will be larger when the calculation is terminated. The smaller the value, the slower the convergence, but the calculation accuracy is generally higher. The convergence criterion is determined by calculating the 2-norm of the change between the two adjacent iteration solutions δ k The convergence criterion is determined by calculating the 2-norm of the change between the two adjacent iteration solutions δ δ k is the 2-norm of the change between the two adjacent iteration solutions, F k is the calculation result of the k-th iteration, N is the number of voxels, and the convergence criterion ξ is a constant in the range of 0-1, which can be selected as 1e -6 .

[0063] In one embodiment, the weight matrix of the smoke to be reconstructed is calculated based on the synchronous algebraic reconstruction method, the projection error of the smoke image to be reconstructed is calculated and corrected, and a TV regularization algorithm is used for processing to obtain the corrected smoke concentration, and the reconstruction result of the smoke to be reconstructed is output.

[0064] The calculation principle of the synchronous algebraic reconstruction method is that the weight matrix of the smoke to be reconstructed is divided into blocks according to the smoke image to be reconstructed, the projection error of all rows (all pixels in a smoke image to be reconstructed) in a block is calculated, and then the weight in the weight matrix is redistributed to each voxel. Finally, each voxel is iterated according to the error value to complete the synchronous algebraic reconstruction method. The calculation formula is wherein, is the value in the j-th voxel after the k-th iteration, is the value after the (k+1)-th iteration, w i，j is the weight of the i-th pixel to the j-th voxel, p i is the value in the i-th pixel, P φ refers to any one smoke image to be reconstructed, N is the column number of the matrix and also the number of voxels, and λ is the relaxation factor. The relaxation factor λ of the synchronous algebraic reconstruction method is in the range of 0-2, and the value range of the convergence criterion ξ is 0-1.

[0065] The calculation principle of the TV regularization algorithm is to calculate the total variation of all voxels after obtaining the result of the synchronous algebraic reconstruction method, and then derive the total variation of each voxel and iterate according to the steepest descent method to reduce noise. In addition, the negative value in the voxel needs to be set to zero after each iteration in the equation solving process to ensure its non-negativity.

[0066] The calculation steps are as follows: (1) calculating the total variation: Where i, j, k are the position of the voxel, x, y, z can take any number in -1, 0, 1, which is determined by the selected adjacent voxel, and ε is a small constant to ensure μ i，j，k is not 0, which can be 1e -8 , μ i，j，k of all voxels is 0, the total variation expression ||F||TV can be obtained by summing up TV i，j，k μ i，j，k ; (2) Calculate the gradient of the total variation (3) Calculate the minimum value of the total variation using the steepest descent method Where

[0067] The value of η in step (3) is calculated in real time during the iteration process, and the formula is Where the proportional coefficient α is obtained by performing a geometric similarity example under the condition of not using TV regularization or given according to experience. For example, α can be the minimum point in the error curve when only SART algorithm is used without TV regularization. The proportional coefficient of Nλ is determined by the characteristics of the example itself, and in actual use, it can be given according to experience. Since the error curve has a faster descending speed and a slower ascending speed, α can be slightly smaller to delay the effective time of TV regularization.

[0068] Both the algebraic reconstruction method and the synchronous algebraic reconstruction method combined with the TV regularization algorithm can distribute the weight matrix to different threads for iterative calculation through multi-thread distribution until the calculation result meets the convergence condition and then output.

[0069] During the calculation process, when it is necessary to quickly obtain the smoke distribution profile, ART algorithm is selected, and when high-precision results are required, SART and TV regularization algorithms are used. ART algorithm is simple to operate and requires fewer parameters to be set during calculation. Only the image and weight matrix need to be input, and the relaxation factor and convergence condition can be set to start calculation. These parameters can be fixed, and the values can be larger to achieve fast calculation, which is mainly used to obtain the approximate shape of the distribution in a short time. The operation of SART and TV regularization algorithms is more complex. In order to achieve high precision, the values of the relaxation factor and convergence condition are often small, so the calculation time of SART and TV regularization algorithms will be longer, which is suitable for conditions that allow long-time calculation to obtain high-precision results.

[0070] ​​When a basic shape of smoke distribution needs to be obtained quickly, the algebraic reconstruction method can be used with relatively large relaxation factors and convergence conditions. When higher accuracy is required and long computation times are acceptable, the synchronous algebraic reconstruction method combined with the TV regularization algorithm can be chosen with relatively small parameters. The truncated singular value decomposition method can achieve the same fast computation as ART by setting a small number of singular values ​​and performing the calculation only once. Higher accuracy can be achieved by selecting multiple singular values ​​from small to large and using ridge regression to select the optimal number of singular values.

[0071] Figure 5 The following is based on Figure 2 The calculation results of the smoke image to be reconstructed were obtained by using the synchronous algebraic reconstruction method combined with the TV regularization algorithm. Figure 5 The locations and numerical values ​​of voxels with a concentration greater than 50 are given; voxels with a concentration less than 50 within the computational domain are hidden. Figure 5 and Figure 2 In comparison, it can be seen that the method provided by this invention achieves the computational objective of three-dimensional reconstruction of smoke.

[0072] like Figure 6 As shown, according to one aspect of the present invention, a system for three-dimensional smoke computed tomography is provided, comprising:

[0073] Preprocessing module 601: acquires the smoke image to be reconstructed, constructs the corresponding reconstruction region, and calculates the list of rays corresponding to the pixels of the smoke image to be reconstructed;

[0074] Collision detection module 602: Creates a list of reconstructed sub-regions based on the light beam list, performs collision detection on the elements of the list of reconstructed sub-regions, and calculates the weight matrix of the smoke to be reconstructed;

[0075] Matrix solving module 603: solves the weight matrix of the smoke to be reconstructed and outputs the reconstruction result of the smoke to be reconstructed.

[0076] The following is for reference. Figure 7 It shows a schematic diagram of the structure of a computer system 700 suitable for implementing electronic devices according to embodiments of the present application. Figure 7 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0077] like Figure 7As shown, the computer system 700 includes a central processing unit (CPU) 701 that performs various suitable actions and processes according to programs stored in a read-only memory (ROM) 702 or loaded from a storage section 709 into a random access memory (RAM) 704. Various programs and data required for the operation of the system 700 are also stored in the RAM 704. The CPU 701, the ROM 702, the ROM 703, and the RAM 704 are connected to each other through a bus 705. An input / output (I / O) interface 706 is also connected to the bus 705.

[0078] The following components are connected to the I / O interface 706: an input section 707 including a keyboard, a mouse, etc.; an output section 708 including a display such as a liquid crystal display (LCD), and a speaker, etc.; a storage section 709 including a hard disk, etc.; and a communication section 710 including a network interface card such as a LAN card, a modem, etc. The communication section 710 performs communication processing via a network such as the Internet. A drive 711 is also connected to the I / O interface 706 as necessary. A removable recording medium 712 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the drive 711 as necessary, so that a computer program read therefrom is installed into the storage section 709 as necessary.

[0079] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts are implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program is downloaded and installed from a network by the communication section 710, and / or installed from the removable recording medium 712. When the computer program is executed by the central processing unit (CPU) 701, the above-described functions defined in the methods of the present application are performed.

[0080] It should be noted that the computer-readable storage medium of this application is a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium is, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium is any tangible medium containing or storing a program that is used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium includes a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals take various forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium or any computer-readable storage medium other than a computer-readable storage medium may transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0081] Computer program code for performing the operations of this application is written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code executes entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer is connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The computer program product of the present application can be a computer program embodied on a computer readable medium. The computer program product can be stored in the memory 130 and executed by the processor 120 as a computer program running on the operating system, a stand-alone application, or as a plug-in for another application, integrated into the other application. The computer readable medium can be a storage device such as ROM, floppy disks, compact disks, tape, etc. Also, computer readable medium can be available on the network through an appropriate device such as hard disk or flash memory.

[0083] The modules involved in the embodiments of the present application are described in the form of software, and can also be implemented in the form of hardware.

[0084] As another aspect, the present application also provides a computer readable storage medium, which is included in the electronic device described in the above embodiments, and also exists independently without being assembled into the electronic device. The computer readable storage medium carries one or more programs, when the one or more programs are executed by the electronic device, the electronic device is caused to: S1, acquire a to-be-reconstructed smoke image, construct a corresponding reconstruction region, and calculate a light list corresponding to pixels of the to-be-reconstructed smoke image; S2, create a reconstruction sub-region list according to the light list, perform collision detection on elements of the reconstruction sub-region list, and calculate a weight matrix of the to-be-reconstructed smoke; S3, solve the weight matrix of the to-be-reconstructed smoke, and output a reconstruction result of the to-be-reconstructed smoke.

[0085] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. A method of three-dimensional computed tomography of smoke, characterized by, The method comprises the following steps: S1, obtaining a to-be-reconstructed smoke image, constructing a corresponding reconstruction region, and calculating a light list corresponding to pixels of the to-be-reconstructed smoke image; S2, creating a reconstruction sub-region list according to the light list, performing collision detection on elements of the reconstruction sub-region list, and calculating a weight matrix of the to-be-reconstructed smoke, specifically comprising: S201, dividing the reconstruction region into a plurality of voxels according to the number of lights in the light list; S202, taking a to-be-tested light from the light list and creating a corresponding reconstruction sub-region list, and taking the reconstruction region as the first element of the reconstruction sub-region list; S203, performing collision detection on the to-be-tested light and the first element in the reconstruction sub-region list, and if collision occurs, judging whether the first element is composed of only one voxel: if yes, using a Monte Carlo algorithm to calculate the weight of the voxel; if no, decomposing the first element and adding it to the end of the first element; S204, removing the first element in the reconstruction sub-region list, and then judging whether there are remaining elements in the reconstruction sub-region list: if yes, repeating S203; if no, sorting all weights of the to-be-tested light according to the positions of the voxels to obtain a weight matrix of the to-be-tested light; S205, repeating S202-S204 for all to-be-tested lights in the light list to obtain a weight matrix of the to-be-reconstructed smoke; S3, solving the weight matrix of the to-be-reconstructed smoke, and outputting a reconstruction result of the to-be-reconstructed smoke.

2. A method of three-dimensional computed tomography of smoke according to claim 1, characterized in that Solving the weight matrix of the to-be-reconstructed smoke specifically comprises: calculating a projection error of the weight matrix of the to-be-reconstructed smoke and the to-be-reconstructed smoke image based on an algebraic reconstruction method, and correspondingly correcting the projection error to obtain a corrected smoke concentration value, and outputting the reconstruction result of the to-be-reconstructed smoke.

3. A method of three-dimensional computed tomography of smoke according to claim 1, wherein, Solving the weight matrix of the to-be-reconstructed smoke specifically comprises: calculating a projection error of the weight matrix of the to-be-reconstructed smoke and the to-be-reconstructed smoke image based on a synchronous algebraic reconstruction method, and correspondingly correcting the projection error, and using a TV regularization algorithm for processing to obtain a corrected smoke concentration value, and outputting the reconstruction result of the to-be-reconstructed smoke.

4. A method of three-dimensional computed tomography of smoke according to claim 1, wherein, Solving the weight matrix of the to-be-reconstructed smoke specifically comprises: performing singular value decomposition on the weight matrix of the to-be-reconstructed smoke based on a truncated singular value decomposition method, selecting an optimal singular value number and a singular vector number based on a ridge regression method to solve the weight matrix of the to-be-reconstructed smoke, and outputting the reconstruction result of the to-be-reconstructed smoke.

5. A method of three-dimensional smoke computed tomography according to claim 1, wherein, S1 specifically comprises constructing a reconstruction region corresponding to the to-be-reconstructed smoke and the light list according to parameters of the to-be-reconstructed smoke image.

6. A system for three-dimensional computed tomography of aerosols, characterized in that It comprises: a preprocessing module: obtaining a to-be-reconstructed smoke image, constructing a corresponding reconstruction region, and calculating a light list corresponding to pixels of the to-be-reconstructed smoke image; a collision detection module: creating a reconstruction sub-region list according to the light list, performing collision detection on elements of the reconstruction sub-region list, and calculating a weight matrix of the to-be-reconstructed smoke, specifically comprising: S201, dividing the reconstruction region into a plurality of voxels according to the number of rays in the ray list; S202, taking a to-be-tested light from the ray list and creating a corresponding reconstruction sub-region list, taking the reconstruction region as the first element of the reconstruction sub-region list; S203, performing collision detection on the to-be-tested light and the first element in the reconstruction sub-region list, and if collision occurs, judging whether the first element is composed of only one voxel: if yes, using a Monte Carlo algorithm on the voxel where collision occurs to calculate the weight of the voxel; if no, decomposing the first element and adding it to the end of the first element; S204, removing the first element in the reconstruction sub-region list, and then judging whether there is a remaining element in the reconstruction sub-region list: if yes, repeating S203; if no, sorting all weights of the to-be-tested light according to the positions of the voxels to obtain a weight matrix of the to-be-tested light; S205, repeating S202-S204 for all to-be-tested lights in the ray list to obtain a weight matrix of the to-be-reconstructed smoke; a matrix solving module: solving the weight matrix of the to-be-reconstructed smoke and outputting a reconstruction result of the to-be-reconstructed smoke.

7. A computer program product having stored thereon a computer program, The computer program, when executed by a processor, implements the method of any one of claims 1-5.

8. A computing system comprising a processor and a memory, the processor being configured to perform the method of any one of claims 1-5.

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