CN114719980A - End-to-end spectrum reconstruction method and system - Google Patents

End-to-end spectrum reconstruction method and system Download PDF

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CN114719980A
CN114719980A CN202210343128.2A CN202210343128A CN114719980A CN 114719980 A CN114719980 A CN 114719980A CN 202210343128 A CN202210343128 A CN 202210343128A CN 114719980 A CN114719980 A CN 114719980A
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spectral
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田海军
赵巨峰
崔光茫
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Hangzhou Dianzi University
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Abstract

The invention discloses an end-to-end spectrum reconstruction method, which comprises the following steps: s1, reconstructing a hyperspectral image; s2, taking the obtained hyperspectral image as the input of an optimized neural network, training neural network parameters according to an end-to-end spectral reconstruction model, and calculating a loss function; and S3, reversely transmitting the error to the coding template to be designed, changing the design parameters of the coding template, and continuously training the model until a high-quality hyperspectral image is reconstructed. By adopting the technical scheme, the design of the optical system is combined with the reconstruction method, and the self-adaptive coding template is additionally arranged on the basis of the coding aperture snapshot system according to the end-to-end spectrum reconstruction method to replace the traditional random coding template, so that the quality of spectrum reconstruction is improved.

Description

End-to-end spectrum reconstruction method and system
Technical Field
The invention relates to the technical field of hyperspectral image processing, in particular to an end-to-end spectrum reconstruction method and an end-to-end spectrum reconstruction system.
Background
Spectral imaging captures the spectral power distribution of a scene or object as a three-dimensional data cube, comprising multiple two-dimensional images of the same scene measured at different wavelengths. The hyperspectral images generally contain dozens or even hundreds of spectral channels, and the images on different spectral bands contain different spatial information and spectral information. Therefore, the application fields of the method are very wide and comprise medical imaging, remote sensing, national defense and monitoring, food quality assessment and the like.
Due to the explosive development of computational reconstruction, snapshot spectral imagers have also been developed further. Such techniques, including computed tomography spectroscopy (CTIS), prism mask spectroscopy video imaging systems (PMVIS), can capture a complete hyperspectral image with a single shot. Based on compressive sensing theory, Wagadarikar et al propose Coded Aperture Snapshot Spectral Imaging (CASSI) (e.g., Ashwin Wagadarikar, Renu John, RebeccaWillett, and David Brady, "Single discrete design for coded aperture snapshot spectral imaging," applied. Opt., vol.47, No.10, pp.B44-B51, Apr 2008).
In addition, coded aperture snapshot spectral imaging has grown rapidly in recent years. In the halftone field, Ulichney proposes a uniformly distributed random unbiased noise model, the blue noise model. This model can be applied to coded aperture templates. The only component that changes in the coded aperture snapshot spectral imaging system is the coded aperture template. And the coded aperture template determines the number of measurements acquired by the sensor and the quality of spectral reconstruction. Therefore, designing a reasonable coded aperture template will further improve the spectral reconstruction quality. In the paper "spatial blue noise coded aperture design for multi-shot compressive spectral imaging" (Claudia V Correa, HenryArguello, and Gonzalo RArce, "spatial blue noise coded aperture design for multi-shot compressive spectral imaging," JOSAA, vol.33, No.12, pp.2312-2322,2016), Correa et al designed spatial blue noise coding apertures based on the finite equidistant nature of the spectral imaging sensing matrix of the coding snapshot apertures, improving the quality of the spectral imaging. Meanwhile, the convolutional neural network is widely applied to spectrum reconstruction, and compared with traditional sparse recovery and dictionary learning, the convolutional neural network has excellent computing capability and feature learning mapping. At present, a new research direction is to replace iterative optimization in compressed sensing by a deep neural network, and the method uses data driving to replace empirical design, so that the quality of spectral reconstruction is further improved. However, the data-driven optimization of the spectral reconstruction process is only from an algorithmic level, and does not take into account the joint optimization of the optical system design.
Through the above analysis, the problems and defects of the prior art are as follows: at present, most of coded aperture snapshot spectral imaging adopts a fixed random coded aperture template, and the coded aperture template with a more reasonable structure is not designed. In addition, the reconstruction quality of the spectral image is improved only from the algorithm level, but the joint optimization of the whole optical system is not considered, so that the reconstruction quality of the spectral image is influenced.
The difficulty in solving the above problems and defects is: how to design a coding aperture template with a more reasonable structure; how to design joint optimization of the whole optical system.
Disclosure of Invention
The invention aims to provide an end-to-end spectrum reconstruction method and system aiming at the defects of the prior art, a coded aperture template and a neural network are optimally designed, learnable parameters are the coded aperture template and the neural network parameters, the deviation between a real image and a reconstructed image can be reduced to the maximum extent, and the quality of spectrum image reconstruction is further improved.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method of end-to-end spectral reconstruction comprising the steps of:
s1 reconstruction of hyperspectral image
S1-1, constructing a sensing model of the adaptive coding aperture snapshot system through the adaptive coding template:
y＝Hf+n
y is an original image acquired by a sensor, H represents a system observation matrix, f represents an original hyperspectral image to be restored, and n is sensor noise;
s1-2, according to the model, preliminarily reconstructing a hyperspectral image;
s2, taking the obtained hyperspectral image as the input of an optimized neural network, training neural network parameters according to an end-to-end spectral reconstruction model, and calculating a loss function;
and S3, reversely transmitting the error to the coding template to be designed, changing the design parameters of the coding template, and continuously training the model until a high-quality hyperspectral image is reconstructed.
Preferably, in step S1, the system observation matrix H is obtained by the following formula: h ═ TPD
Wherein T is equal to {0,1} to represent the state of the encoding template, 0 represents closing, and 1 represents opening; p represents a discretization model of the dispersive element; d represents the spatial extraction related to the size of the sensor pixel, and the discretization model of the dispersive element is specifically expressed as P epsilon RM(N+L-1)×MNL。
Preferably, the sensor captures raw images
Figure BDA0003580085460000031
The system observation matrix
Figure BDA0003580085460000032
The original hyperspectral image f to be restored belongs to RMNLThe sensing noise
Figure BDA0003580085460000033
Where R represents the dimension, M represents the length of the original image in space, N represents the width of the original image in space, and L represents the number of spectral bands.
Preferably, in step S1-2, the method for preliminarily reconstructing the hyperspectral image includes constraining a solution space by using image priors as regularization, and solving a minimization problem to obtain the hyperspectral image:
Figure BDA0003580085460000034
where τ is a balance parameter, μ is a penalty parameter, e ∈ RMNLAs an auxiliary variable, the number of variables,
Figure BDA0003580085460000035
the middle subscript 2 represents the L2 norm, the upper right 2 represents the square, and the solution of the equation can be divided into two sub-questionsThus, find the minimum f and e.
Preferably, the optimized neural network in the step S2 is a CA-uet network.
Preferably, the end-to-end spectral reconstruction model in step S2 is:
Figure BDA0003580085460000041
the method comprises the steps of obtaining a spectrum reconstruction network, determining a system observation matrix, a code template, a system observation matrix, a whole spectrum reconstruction network, and alpha R (t), wherein theta represents optimization parameters of the spectrum reconstruction network and comprises design parameters of the code template, H is the system observation matrix and is used for optimizing the spectrum reconstruction network, M (-) represents the whole spectrum reconstruction network, and alpha R (t) represents regularization to realize binarization of the code template.
Preferably, said r (t) is represented by:
Figure BDA0003580085460000042
wherein, ti,jE {0,1} represents the binarization of the encoding template.
Preferably, the loss function is expressed as:
Figure BDA0003580085460000043
wherein the content of the first and second substances,
Figure BDA0003580085460000044
for the high spectral value of each pixel point reconstructed, the spectral loss minimizes the angle between the reconstructed image and the true spectral feature.
Preferably, in step S3, the encoding template design parameter is a finite equidistant property constant δ of the system sensing matrix, δ being expressed as a function of the random encoding aperture pattern structure: t ═ T (δ), where T (·) represents the mapping function of the finite equidistant property constant δ to the system observation matrix.
The invention also discloses an end-to-end spectrum reconstruction system, which comprises an objective lens, an adaptive coding template, a relay lens, a prism and a gray camera, wherein the adaptive coding template comprises a memory, a processor and a computer program which is stored in the memory and can execute the end-to-end spectrum reconstruction method on the processor.
The invention has the following characteristics and beneficial effects:
by adopting the technical scheme, the design of the optical system is combined with the reconstruction method, and the self-adaptive coding template is additionally arranged on the basis of the coding aperture snapshot system according to the end-to-end spectrum reconstruction method to replace the traditional random coding template, so that the quality of spectrum reconstruction is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of an end-to-end spectral reconstruction method according to the present invention.
FIG. 2 is a diagram of an end-to-end spectral reconstruction system according to the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The invention provides an end-to-end spectrum reconstruction method, as shown in fig. 1, comprising the following steps:
s1 reconstruction of hyperspectral image
S1-1, constructing a sensing model of the adaptive coding aperture snapshot system through the adaptive coding template:
y＝Hf+n
wherein y is an original image acquired by the sensor, H represents a system observation matrix, f represents an original hyperspectral image to be restored, and n is sensor noise. In particular, the sensor collects raw images
Figure BDA0003580085460000061
The system observation matrix
Figure BDA0003580085460000062
The original hyperspectral image f to be restored belongs to RMNLThe sensing noise
Figure BDA0003580085460000063
Wherein R represents dimensionDegree, M denotes the length of the original image in space, N denotes the width of the original image in space, and L denotes the number of spectral bands.
The system observation matrix H is obtained by:
H＝TPD
wherein T is equal to {0,1} to represent the state of the encoding template, 0 represents closing, and 1 represents opening; p represents a discretization model of the dispersive element; d represents the spatial extraction related to the size of the sensor pixel, and the discretization model of the dispersive element is specifically expressed as P epsilon RM(N+L-1)×MNL。
S1-2, according to the model, preliminarily reconstructing a hyperspectral image
Specifically, the method for preliminarily reconstructing the hyperspectral image comprises the steps of adopting image prior as regularization to constrain solution space, solving a minimization problem to obtain the hyperspectral image:
Figure BDA0003580085460000064
where τ is a balance parameter, μ is a penalty parameter, e ∈ RMNLAs an auxiliary variable, the number of variables,
Figure BDA0003580085460000065
the middle subscript 2 represents the L2 norm and the upper right 2 represents the square, and the solution to the equation can be split into two subproblems to find the minimized f and e.
Figure BDA0003580085460000066
Figure BDA0003580085460000067
Solving the above by gradient descent method
Figure BDA0003580085460000071
Figure BDA0003580085460000072
Where λ is the step size of the gradient descent, set to 0.01,
Figure BDA0003580085460000073
is a hyperspectral image.
S2, obtaining the hyperspectral image
Figure BDA0003580085460000074
As the input of the optimized neural network, training neural network parameters and calculating a loss function according to an end-to-end spectrum reconstruction model;
specifically, the optimized neural network is a CA-uet network, which may be denoted as p (·), and the minimized e may be denoted as:
Figure BDA0003580085460000075
it can be understood that the optimized neural network CA-uet is mainly composed of two parts, a uet module and a channel attention module. In the Unet network, the first convolutional layer generates a tensor with a characteristic size of 64 using a 3 × 3 × 31 filter to enhance sparsity of spectral gradients. The network then generates a multi-scale feature, a contraction path with a largest pool and an expansion path with an upcurl layer. For each level, two convolutional layers encode spatial spectral features. By skipping the connection, the scaling feature will be connected to the upscaling feature. In the channel attention module, extracting 1 × 1 × 64 global information from M × N feature maps of 64 channels, then performing full connection to obtain a 64/r-dimensional vector, performing Relu activation, performing full connection again, converting the 64/r-dimensional vector into a 64-dimensional vector, and performing sigmoid activation to enable the numerical value to be between 0 and 1 to obtain a weight matrix. The weight matrix is multiplied by the feature map. Finally, 31 convolution layers of 3 × 3 × 64 are used to generate a tensor of the original hyperspectral cube size, namely, the restored hyperspectral image.
Further, the end-to-end spectral reconstruction model is as follows:
Figure BDA0003580085460000076
the method comprises the steps of obtaining a spectrum reconstruction network, determining a system observation matrix, a code template, a system observation matrix, a whole spectrum reconstruction network, and alpha R (t), wherein theta represents optimization parameters of the spectrum reconstruction network and comprises design parameters of the code template, H is the system observation matrix and is used for optimizing the spectrum reconstruction network, M (-) represents the whole spectrum reconstruction network, and alpha R (t) represents regularization to realize binarization of the code template. And (4) according to the end-to-end spectrum reconstruction model, the coded aperture template can be optimally designed in a combined mode.
Said R (t) is represented by:
Figure BDA0003580085460000081
wherein, ti,jE {0,1} represents the binarization of the encoding template.
The loss function is expressed as:
Figure BDA0003580085460000082
wherein the content of the first and second substances,
Figure BDA0003580085460000083
for the high spectral value of each reconstructed pixel point, the spectral loss minimizes the angle between the reconstructed image and the true spectral feature, and the loss function value can be calculated by the above formula.
And S3, reversely transmitting the error to the coding template to be designed, changing the design parameters of the coding template, and continuously training the model until a high-quality hyperspectral image is reconstructed.
Specifically, the design parameter of the coding template is a finite equidistant property constant δ of a system sensing matrix, wherein δ is expressed as a function of a random coding aperture pattern structure: t ═ T (δ), where T (·) denotes a mapping function of finite equidistant property constants δ to the system observation matrix, which ensures maximum separation of the coding patterns to be designed in the horizontal direction and maximum incoherence in the vertical direction. The initial delta setting is 0.5.
The error back propagation adopts a gradient descent method:
Figure BDA0003580085460000084
Figure BDA0003580085460000085
where λ, ε is the gradient descent step, λ is set to 0.001, and ε is set to 0.002.
In the present embodiment, the number of iterations is set to 20. And obtaining the final delta and a designed self-adaptive coding template pattern, and reconstructing a high-quality hyperspectral image.
The invention also discloses an end-to-end spectrum reconstruction system, which comprises an objective lens, an adaptive coding template, a relay lens, a prism and a gray-scale camera, wherein the adaptive coding template comprises a memory, a processor and a computer program which is stored in the memory and can execute the end-to-end spectrum reconstruction method on the processor, as shown in fig. 2.
The working principle of the technical scheme is as follows:
the target scene is transmitted to the self-adaptive coding template through the objective lens, the hyperspectral image is reconstructed through the end-to-end spectrum reconstruction method, the reconstructed hyperspectral image is obtained through the relay lens and the prism and the grayscale camera, the reconstructed hyperspectral image is optimized through the neural network and is sent to the self-adaptive coding template for iteration, and finally the high-quality hyperspectral image is output.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments, including the components, without departing from the principles and spirit of the invention, and still fall within the scope of the invention.

Claims (10)

1. A method of end-to-end spectral reconstruction, comprising the steps of:
s1 reconstruction of hyperspectral image
S1-1, constructing a sensing model of the adaptive coding aperture snapshot system through an adaptive coding template:
y＝Hf+n
y is an original image acquired by a sensor, H represents a system observation matrix, f represents an original hyperspectral image to be restored, and n is sensor noise;
s1-2, according to the model, preliminarily reconstructing a hyperspectral image;
s2, taking the obtained hyperspectral image as the input of an optimized neural network, training neural network parameters according to an end-to-end spectral reconstruction model, and calculating a loss function;
and S3, reversely transmitting the error to the coding template to be designed, changing the design parameters of the coding template, and continuously training the model until a high-quality hyperspectral image is reconstructed.
2. The end-to-end spectral reconstruction method of claim 1, wherein in step S1, the system observation matrix H is obtained by the following formula: h ═ TPD
Wherein T is equal to {0,1} to represent the state of the encoding template, 0 represents closing, and 1 represents opening; p represents a discretization model of the dispersive element; d represents the spatial extraction related to the size of the sensor pixel, and the discretization model of the dispersive element is specifically expressed as P epsilon RM(N +L-1)×MNL。
3. The end-to-end spectral reconstruction method of claim 2, wherein the raw image acquired by the sensor is
Figure FDA0003580085450000011
The system observation matrix
Figure FDA0003580085450000012
The original hyperspectral image f to be restored belongs to RMNLThe sensing noise
Figure FDA0003580085450000013
Where R represents the dimension, M represents the length of the original image in space, N represents the width of the original image in space, and L represents the number of spectral bands.
4. An end-to-end spectrum reconstruction method according to claim 3, wherein in step S1-2, the primary reconstruction method of the hyperspectral image is to use image priors as regularization to constrain solution space and solve a minimization problem to obtain the hyperspectral image:
Figure FDA0003580085450000014
where τ is the balance parameter, μ is the penalty parameter, e ∈ RMNLAs an auxiliary variable, the number of variables,
Figure FDA0003580085450000021
the middle subscript 2 represents the L2 norm and the upper right 2 represents the square, and the solution to the equation can be split into two subproblems to find the minimized f and e.
5. The end-to-end spectral reconstruction method of claim 1, wherein said optimized neural network in step S2 is a CA-uet network.
6. The end-to-end spectral reconstruction method of claim 5, wherein the end-to-end spectral reconstruction model in step S2 is:
Figure FDA0003580085450000022
the method comprises the steps of obtaining a spectrum reconstruction network, determining a system observation matrix, a code template, a system observation matrix, a whole spectrum reconstruction network, and alpha R (t), wherein theta represents optimization parameters of the spectrum reconstruction network and comprises design parameters of the code template, H is the system observation matrix and is used for optimizing the spectrum reconstruction network, M (-) represents the whole spectrum reconstruction network, and alpha R (t) represents regularization to realize binarization of the code template.
7. The end-to-end spectral reconstruction method of claim 6, wherein said R (t) is represented by:
Figure FDA0003580085450000023
wherein, ti,jE {0,1} represents binarization of the encoding template.
8. The end-to-end spectral reconstruction method of claim 7, wherein said loss function is expressed as:
Figure FDA0003580085450000024
wherein the content of the first and second substances,
Figure FDA0003580085450000025
for the high spectral value of each pixel point reconstructed, the spectral loss minimizes the angle between the reconstructed image and the true spectral feature.
9. The end-to-end spectral reconstruction method of claim 7, wherein in step S3, the encoding template design parameter is a finite equidistant property constant δ of the system sensing matrix, δ being expressed as a function of the random encoding aperture pattern structure: t ═ T (δ), where T (·) represents the mapping function of the finite equidistant property constant δ to the system observation matrix.
10. An end-to-end spectral reconstruction system comprising an objective lens, an adaptive encoding template, a relay lens, a prism, a grayscale camera, the adaptive encoding template comprising a memory, a processor, and a computer program stored in the memory and operable on the processor to perform the end-to-end spectral reconstruction method of any of claims 1-9.
CN202210343128.2A 2022-04-02 2022-04-02 End-to-end spectrum reconstruction method and system Pending CN114719980A (en)

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Publication number Priority date Publication date Assignee Title
CN115187870A (en) * 2022-09-13 2022-10-14 浙江蓝景科技有限公司杭州分公司 Marine plastic waste material identification method and system, electronic equipment and storage medium

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