CN113012017B - Transient scene image reconstruction method and related device based on sparse basis - Google Patents

Transient scene image reconstruction method and related device based on sparse basis Download PDF

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CN113012017B
CN113012017B CN202110368969.4A CN202110368969A CN113012017B CN 113012017 B CN113012017 B CN 113012017B CN 202110368969 A CN202110368969 A CN 202110368969A CN 113012017 B CN113012017 B CN 113012017B
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甘华权
黄运保
李海艳
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Guangdong University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0007—Image acquisition
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
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    • G03B39/00—High-speed photography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
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Abstract

The application discloses a transient scene image reconstruction method and a related device based on sparse basis, wherein the method comprises the following steps: performing expansion conversion on the target source image to obtain an initial source image column vector; converting the initial source diagram column vector in the time domain into a preset change domain to obtain a source diagram sparse column vector; expressing the sparse column vector of the source map as a linear observation image by a preset processing method, wherein the preset processing method comprises coding, shearing and accumulating; carrying out minimization problem solving according to the linear observation image by adopting a preset optimization algorithm to obtain an optimal image solution in a sparse domain; and (3) converting the optimal image solution to a time domain to obtain a target reconstruction image. The method and the device can solve the technical problems that an existing CUP technology is a time domain solving process, the calculated amount is large, the image does not have sparsity, and the reconstruction efficiency is low.

Description

Transient scene image reconstruction method and related device based on sparse basis
Technical Field
The application relates to the technical field of image reconstruction, in particular to a transient scene image reconstruction method based on sparse basis and a related device.
Background
Capturing ultra-fast dynamic scenes with high-speed imaging technology is a long-term dream for scientists because it enables the discovery of new physical phenomena and the development of new optical imaging technologies. Charge-coupled devices (CCDs) and complementary metal oxide semiconductors (Complementary Metal Oxide Semiconductor, CMOS) are relatively sophisticated tools for capturing dynamic scenes, but the maximum frame rates of CCDs and CMOS are currently only 107 frames/s, a rate which does not enable scientists to discover new physical phenomena and develop new optical imaging technologies. Recently, compressed ultra-fast photography (Compressed Ultrafast Photography, CUP) technology has been applied to capture reflection and refraction of laser pulses, movement of photons in both media, spatially modulated pulsed laser spots, etc., to increase the maximum frame rate of imaging to 1014 frames/s.
In the existing CUP technology, the initial value of the adopted reconstruction algorithm and the selection of parameters seriously affect the quality of image reconstruction, and even the iterative process cannot be converged. Moreover, since the intensity accumulation matrix is a complex matrix, the calculation amount is large in the iterative process. Finally, the prior art solves in the time domain, and the picture does not generally show sparsity in the time domain.
Disclosure of Invention
The application provides a transient scene image reconstruction method and a related device based on a sparse basis, which are used for solving the technical problems that an existing CUP technology is a time domain solving process, the calculated amount is large, the image does not have sparsity, and the reconstruction efficiency is low.
In view of this, a first aspect of the present application provides a method for reconstructing a transient scene image based on a sparse basis, including:
performing expansion conversion on the target source image to obtain an initial source image column vector;
converting the initial source diagram column vector in the time domain into a preset change domain to obtain a source diagram sparse column vector;
expressing the source map sparse column vector as a linear observation image by a preset processing method, wherein the preset processing method comprises coding, shearing and accumulating;
carrying out minimization problem solving according to the linear observation image by adopting a preset optimization algorithm to obtain an optimal image solution in a sparse domain;
and converting the optimal image solution to a time domain to obtain a target reconstruction image.
Optionally, the expanding and converting the target source image to obtain an initial source image column vector includes:
and expanding the target source image into a preset charge coupling equipment imaging size in a zero filling mode, and then expanding a column vector to obtain an initial source image column vector.
Optionally, the linear expression of the linear observation image is:
Figure GDA0004216340960000021
wherein M is p×r For intensity accumulation matrix, S r×r For shearingOperator matrix, C r×r In order to randomly encode the matrix,
Figure GDA0004216340960000022
as a sparse basis matrix, θ r×1 Is a source map sparse column vector.
Optionally, the performing the minimization problem solving by using a preset optimization algorithm according to the linear observation image to obtain an optimal image solution in the sparse domain includes:
constructing an optimization formula for minimizing problems according to the linear observation image, wherein the optimization formula is as follows:
Figure GDA0004216340960000023
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure GDA0004216340960000024
and optimizing and solving an optimization formula of the minimization problem by adopting a preset optimization algorithm to obtain an optimal image solution in a sparse domain.
The second aspect of the present application provides a transient scene image reconstruction device based on sparse basis, including:
the expansion module is used for carrying out expansion conversion on the target source image to obtain an initial source image column vector;
the first conversion module is used for converting the initial source diagram column vector in the time domain into a preset change domain to obtain a source diagram sparse column vector;
the image processing module is used for expressing the source image sparse column vector into a linear observation image through a preset processing method, and the preset processing method comprises coding, shearing and accumulating;
the solving module is used for carrying out minimization problem solving according to the linear observation image by adopting a preset optimizing algorithm to obtain an optimal image solution in a sparse domain;
and the second conversion module is used for performing solution conversion on the optimal image to a time domain to obtain a target reconstruction image.
Optionally, the expansion module is specifically configured to:
and expanding the target source image into a preset charge coupling equipment imaging size in a zero filling mode, and then expanding a column vector to obtain an initial source image column vector.
Optionally, the linear expression of the linear observation image is:
Figure GDA0004216340960000031
wherein M is p×r For intensity accumulation matrix, S r×r To cut operator matrix, C r×r In order to randomly encode the matrix,
Figure GDA0004216340960000032
as a sparse basis matrix, θ r×1 Is a source map sparse column vector.
Optionally, the solving module is specifically configured to:
constructing an optimization formula for minimizing problems according to the linear observation image, wherein the optimization formula is as follows:
Figure GDA0004216340960000033
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure GDA0004216340960000034
and optimizing and solving an optimization formula of the minimization problem by adopting a preset optimization algorithm to obtain an optimal image solution in a sparse domain.
A third aspect of the present application provides a sparse-based transient scene image reconstruction device, the device comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the sparse-based transient scene image reconstruction method of the first aspect according to instructions in the program code.
A fourth aspect of the present application provides a computer readable storage medium for storing program code for performing the sparse-based transient scene image reconstruction method of the second aspect.
From the above technical solutions, the embodiments of the present application have the following advantages:
in the application, a transient scene image reconstruction method based on sparse basis is provided, which comprises the following steps: performing expansion conversion on the target source image to obtain an initial source image column vector; converting the initial source diagram column vector in the time domain into a preset change domain to obtain a source diagram sparse column vector; expressing the sparse column vector of the source map as a linear observation image by a preset processing method, wherein the preset processing method comprises coding, shearing and accumulating; carrying out minimization problem solving according to the linear observation image by adopting a preset optimization algorithm to obtain an optimal image solution in a sparse domain; and (3) converting the optimal image solution to a time domain to obtain a target reconstruction image.
According to the transient scene image reconstruction method based on the sparse basis, the target source image is subjected to sparse expression in a mode of converting the target source image into the preset change domain, the calculated amount in the solving process can be reduced through image sparse expression, and the number of processed images is increased. Therefore, the method and the device can solve the technical problems that the existing CUP technology is a time domain solving process, the calculated amount is large, the image does not have sparsity, and the reconstruction efficiency is low.
Drawings
Fig. 1 is a schematic flow chart of a transient scene image reconstruction method based on sparse basis according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a transient scene image reconstruction device based on sparse basis according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
For ease of understanding, referring to fig. 1, an embodiment one of a sparse-based transient scene image reconstruction method provided in the present application includes:
and step 101, performing expansion conversion on the target source image to obtain an initial source image column vector.
Further, step 101 includes:
and expanding the target source image into a preset charge coupling equipment imaging size in a zero filling mode, and then expanding a column vector to obtain an initial source image column vector.
Assume that the size of the target source image is (N x ,N y K) to simplify the matrix operation, reduce the amount of computation, extend the target source image complement 0 to a preset CCD imaging size, assuming the CCD imaging size is (m, N), where m=n x ,n＝N y +k-1; the expanded image is then converted into an initial source column vector I r×1 Where r=k×m×n.
Step 102, converting the initial source diagram column vector in the time domain into a preset variation domain to obtain a source diagram sparse column vector.
Since the value of the initial source column vector in the time domain is mostly not 0, the general image signal can show sparsity in a specific domain after being transformed by a certain domain, namely
Figure GDA0004216340960000051
Wherein (1)>
Figure GDA0004216340960000052
As a sparse basis matrix, θ r×1 Is a source map sparse column vector.
The preset change domain can be defined according to requirements, for example, discrete cosine transform is performed on the image in a specific frequency domain, and the initial source diagram column vector in the time domain is converted into the frequency domain, so that the image presents sparse characteristics.
And 103, expressing the sparse column vector of the source map into a linear observation image through a preset processing method, wherein the preset processing method comprises coding, shearing and accumulating.
The CUP system can process the source image by adopting a preset processing method to obtain a linear observation image y p The specific preset processing method refers to coding, cutting and accumulating, and the variety of the processing method can be increased or decreased according to the actual situation.
Further, the linear expression of the linear observation image is:
Figure GDA0004216340960000053
wherein M is p×r For intensity accumulation matrix, S r×r To cut operator matrix, C r×r In order to randomly encode the matrix,
Figure GDA0004216340960000054
as a sparse basis matrix, θ r×1 Is a source map sparse column vector.
The above formula can be expressed as:
Figure GDA0004216340960000055
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure GDA0004216340960000056
the encoding of the source image is realized through the encoding matrix, the source image is sheared through the shearing operator matrix, the accumulating operation is carried out on the source image through the intensity accumulating matrix, the matrix calculating process is realized, and the source image is processed through a calculating mode.
And 104, carrying out minimization problem solving according to the linear observation image by adopting a preset optimization algorithm to obtain an optimal image solution in the sparse domain.
Further, step 104 includes:
constructing an optimization formula for minimizing problems according to the linear observation image, wherein the optimization formula is as follows:
Figure GDA0004216340960000057
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure GDA0004216340960000058
and optimizing and solving an optimization formula of the minimization problem by adopting a preset optimization algorithm to obtain an optimal image solution in the sparse domain.
Assuming y as described above p And A p×r For a known quantity, the reconstruction target is the source image at the preset variation domain, which is treated as a solution to the minimization problem.
The solving problem of the optimization formula is an under constraint L1 problem, a preset optimization algorithm can select a two-step iterative contraction/threshold algorithm (Twist), and the specific formula is expressed as follows:
X 1 ＝Γ γ (X 0 )
X t+1 ＝(1-α)X t-1 +(α-β)X t +βΓ γ (X t )
Γ γ X t )＝Ψ γ (X t +K T (y p -KX t ))
wherein Γ is y (. Cndot.) is a denoising function, ψ γ For nonlinear denoising operator, T is total iteration number, alpha, beta is algorithm parameter, K=A p×r ,X＝θ r×1 . In addition, the preset optimization algorithm can also select an alternate direction multiplier algorithm, and the specific optimization process is not repeated.
To ensure iterative optimization convergence, it is necessary to converge on A p×r Carrying out standardization processing, and solving an equation set K by adopting an iterative algorithm T y p ＝K T KX, the convergence is satisfied by ρ (K T K) < 1, where ρ (K) T K) For K T Spectrum of KRadius, i.e.
Figure GDA0004216340960000061
The maximum characteristic value of (2) is less than 1, but in actual operation, i.e. +.>
Figure GDA0004216340960000062
The characteristic value of (2) is less than 1, so that normalization processing is required, assuming +.>
Figure GDA0004216340960000063
The maximum eigenvalue of (a) is k, and the change processing is carried out on A:
Figure GDA0004216340960000064
after the change, the iterative optimization convergence can be ensured.
And 105, converting the optimal image solution to a time domain to obtain a target reconstruction image.
The theta can be obtained after iterative optimization q×1 By means of
Figure GDA0004216340960000065
The column vectors can be converted into the time domain to obtain the target reconstructed image.
According to the transient scene image reconstruction method based on the sparse basis, the target source image is subjected to sparse expression in the mode of converting the target source image into the preset change domain, the calculated amount in the solving process can be reduced through image sparse expression, and the number of processed images is increased. Therefore, the embodiment of the application can solve the technical problems that the existing CUP technology is a time domain solving process, the calculated amount is large, the image does not have sparsity, and the reconstruction efficiency is low.
The above is an embodiment of a sparse-based transient scene image reconstruction method provided by the present application, and the following is an embodiment of a sparse-based transient scene image reconstruction device provided by the present application.
For ease of understanding, referring to fig. 2, the present application provides an embodiment of a sparse-based transient scene image reconstruction apparatus, including:
the expansion module 201 is configured to perform expansion conversion on the target source image to obtain an initial source image column vector;
a first conversion module 202, configured to convert an initial source map column vector in a time domain into a preset variation domain, so as to obtain a source map sparse column vector;
an image processing module 203, configured to express the source map sparse column vector as a linear observation image through a preset processing method, where the preset processing method includes encoding, clipping, and accumulating;
the solving module 204 is configured to perform minimization problem solving according to the linear observation image by using a preset optimization algorithm, so as to obtain an optimal image solution in the sparse domain;
the second conversion module 205 is configured to convert the optimal image solution to a time domain, so as to obtain a target reconstructed image.
Further, the expansion module 201 is specifically configured to:
and expanding the target source image into a preset charge coupling equipment imaging size in a zero filling mode, and then expanding a column vector to obtain an initial source image column vector.
Further, the linear expression of the linear observation image is:
Figure GDA0004216340960000071
wherein M is p×r For intensity accumulation matrix, S r×r To cut operator matrix, C r×r In order to randomly encode the matrix,
Figure GDA0004216340960000074
as a sparse basis matrix, θ r×1 Is a source map sparse column vector.
Further, the solving module 204 is specifically configured to:
constructing an optimization formula for minimizing problems according to the linear observation image, wherein the optimization formula is as follows:
Figure GDA0004216340960000072
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure GDA0004216340960000073
and optimizing and solving an optimization formula of the minimization problem by adopting a preset optimization algorithm to obtain an optimal image solution in the sparse domain.
According to the transient scene image reconstruction method based on the sparse basis, the target source image is subjected to sparse expression in the mode of converting the target source image into the preset change domain, the calculated amount in the solving process can be reduced through image sparse expression, and the number of processed images is increased. Therefore, the embodiment of the application can solve the technical problems that the existing CUP technology is a time domain solving process, the calculated amount is large, the image does not have sparsity, and the reconstruction efficiency is low.
The above is an embodiment of a sparse-based transient scene image reconstruction device provided by the present application, and the following is an embodiment of a sparse-based transient scene image reconstruction device provided by the present application.
The application also provides a transient scene image reconstruction device based on the sparse basis, which comprises a processor and a memory;
the memory is used for storing the program codes and transmitting the program codes to the processor;
the processor is configured to execute the sparse-based transient scene image reconstruction method in the method embodiment according to the instructions in the program code.
The application also provides a computer readable storage medium for storing program code for executing the sparse-based transient scene image reconstruction method in the above method embodiment.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to execute all or part of the steps of the methods described in the embodiments of the present application by a computer device (which may be a personal computer, a server, or a network device, etc.). And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (6)

1. A transient scene image reconstruction method based on a sparse basis is characterized by comprising the following steps:
performing expansion conversion on the target source image to obtain an initial source image column vector;
converting the initial source diagram column vector in the time domain into a preset change domain to obtain a source diagram sparse column vector;
expressing the source map sparse column vector as a linear observation image by a preset processing method, wherein the preset processing method comprises coding, shearing and accumulating; the linear expression of the linear observation image is:
Figure FDA0004216340930000011
wherein M is p×r For intensity accumulation matrix, S r×r To cut operator matrix, C r×r In order to randomly encode the matrix,
Figure FDA0004216340930000012
as a sparse basis matrix, θ r×1 Sparse column vectors for the source map;
carrying out minimization problem solving according to the linear observation image by adopting a preset optimization algorithm to obtain an optimal image solution under a sparse domain, wherein the method comprises the following steps:
when (when)
Figure FDA0004216340930000013
When the method is used, an optimization formula for minimizing the problem is constructed according to the linear observation image, wherein the optimization formula is as follows:
Figure FDA0004216340930000014
and converting the optimal image solution to a time domain to obtain a target reconstruction image.
2. The method for reconstructing a transient scene image based on a sparse basis according to claim 1, wherein the performing expansion transformation on the target source image to obtain an initial source image column vector comprises:
and expanding the target source image into a preset charge coupling equipment imaging size in a zero filling mode, and then expanding a column vector to obtain an initial source image column vector.
3. A sparse-basis-based transient scene image reconstruction device, comprising:
the expansion module is used for carrying out expansion conversion on the target source image to obtain an initial source image column vector;
the first conversion module is used for converting the initial source diagram column vector in the time domain into a preset change domain to obtain a source diagram sparse column vector;
the image processing module is used for expressing the source image sparse column vector into a linear observation image through a preset processing method, and the preset processing method comprises coding, shearing and accumulating; the linear expression of the linear observation image is:
Figure FDA0004216340930000015
wherein M is p×r For intensity accumulation matrix, S r×r To cut operator matrix, C r×r In order to randomly encode the matrix,
Figure FDA0004216340930000021
as a sparse basis matrix, θ r×1 Sparse column vectors for the source map;
the solving module is used for carrying out minimization problem solving according to the linear observation image by adopting a preset optimizing algorithm to obtain an optimal image solution under a sparse domain, and is specifically used for:
when (when)
Figure FDA0004216340930000022
When the method is used, an optimization formula for minimizing the problem is constructed according to the linear observation image, wherein the optimization formula is as follows:
Figure FDA0004216340930000023
and the second conversion module is used for performing solution conversion on the optimal image to a time domain to obtain a target reconstruction image.
4. The sparse-based transient scene image reconstruction device of claim 3, wherein the expansion module is specifically configured to:
and expanding the target source image into a preset charge coupling equipment imaging size in a zero filling mode, and then expanding a column vector to obtain an initial source image column vector.
5. A sparse-based transient scene image reconstruction device, the device comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the sparse-based transient scene image reconstruction method of any of claims 1-2 according to instructions in the program code.
6. A computer readable storage medium for storing program code for performing the sparse-based transient scene image reconstruction method of any one of claims 1-2.
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