CN112785662A - Self-adaptive coding method based on low-resolution priori information - Google Patents
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Abstract
The invention discloses a self-adaptive coding method based on low-resolution priori information, which comprises the following steps: estimating the mean value and the variance of each image block corresponding to the original spectral image by using the integral gray value and the variance of the low-resolution image, wherein the image blocks are scene information sub-regions of the original spectral image corresponding to each point of the low-resolution image; and calculating the threshold distribution of the image blocks of the original spectrum image scene information according to the mean value and the variance of each image block. And constructing an approximate image of the original spectral image by using the low-resolution image. Based on the approximate image of the original spectral image and the corresponding threshold distribution, the adaptive coding matrix is generated by utilizing a color dithering method with the aim of maximizing the correlation between the observation matrix and the sparse matrix as a design goal. According to the invention, high-resolution priori information is not required to be provided in a reconstruction process, the self-adaptive coding matrix can be generated by utilizing the low-resolution spectral information which can be acquired in the compressed spectral imaging system, and an additional detection process and a detection device are not required to be added.
Description
Technical Field
The invention relates to the technical field of compressed spectrum imaging, in particular to a self-adaptive coding method based on low-resolution priori information.
Background
The spectral imaging technology can acquire not only two-dimensional spatial information of a scene, but also spectral characteristics of the scene at the same time, and the data containing the spatial information and the spectral information is called a spectral data cube. In recent years, spectral imaging techniques have been widely used in the fields of security monitoring, food safety inspection, geological research, medical research, and the like. The conventional spectral imager has the following defects: 1) limited by the traditional imaging theory, only single information (gray scale response) of a two-dimensional image can be recorded within a single exposure time, and light field information (such as polarization, depth, phase information and the like) cannot be obtained; 2) due to the limitation of device performance, image data with wide field of view, high spatial-spectral resolution and high temporal resolution and an imaging system with small volume and low power consumption cannot be obtained.
With the development of the optical calculation imaging technology, the independent design concept of the imaging process, the calculation process and the device in the design of the traditional spectral imager is broken through, and the design integrating illumination, a medium, a target, an imaging device, a platform and data processing can be realized. Optical computational imaging techniques can be mainly classified into the following: 1) wavefront shaping and scattering medium imaging techniques based on optical memory effects; 2) novel polarization imaging technologies such as underwater polarization imaging and polarization haze-penetrating imaging; 3) a bionic optical imaging technology; 4) calculating a detector technology; 5) three-dimensional computational imaging technology represented by three-dimensional binocular reconstruction, structured light three-dimensional imaging and holographic three-dimensional imaging; 6) compressive sensing based computational imaging techniques. The computational imaging technology based on compressed sensing utilizes the sparsity of a scene on certain information dimensions, and recovers the information through reconstruction by sampling times far lower than those of the traditional method, so that the spatial and spectral resolution is improved.
The self-adaptive coding method based on compressed sensing is a method for designing a coding matrix by using prior information of space, spectrum, time and the like of a scene so as to improve the quality of a reconstructed image. Researchers have the ability to obtain more accurate a priori information by adding additional detection devices; approximate reconstructions of the scene are also obtained using compressed observations of the system as a priori information. These approaches come at the cost of increased complexity in the structure and reconstruction time of the compressed imaging system.
Disclosure of Invention
The invention aims to: in view of the existing problems, an adaptive coding method based on low-resolution a priori information is provided to solve the problem that the conventional adaptive coding method usually requires high-resolution a priori information in a typical adaptive coding framework.
The technical scheme adopted by the invention is as follows:
an adaptive encoding method based on low resolution prior information, comprising:
A. estimating the mean value and the variance of each image block corresponding to the original spectral image by using the integral gray value and the variance of the low-resolution image, wherein the image blocks are scene information sub-regions of the original spectral image corresponding to each point of the low-resolution image respectively, and the size of each image block corresponds to the compression ratio of the original spectral image; calculating the threshold distribution of the image blocks of the original spectrum image scene information according to the mean value and the variance of each image block;
B. constructing an approximate image of the original spectral image by using the low-resolution image;
C. based on the approximate image of the original spectral image and the corresponding threshold distribution, the adaptive coding matrix is generated by utilizing a color dithering method with the aim of maximizing the correlation between the observation matrix and the sparse matrix as a design goal.
According to the method, an additional detection process and a detection device are not needed, the self-adaptive coding matrix can be generated by utilizing the low-resolution spectral information which can be acquired in the spectral imaging system based on the compressive sensing principle, and the high-quality hyperspectral image can be reconstructed without high-resolution priori information.
Further, the low-resolution image is a spatial down-sampling that performs equal-proportion compression on the original spectrum image in the x dimension and the y dimension. For example, compression with a compression ratio R is performed in the x dimension and the y dimension, and accordingly, the size of the image block is R × R.
Further, the spatial down-sampling is uniform sampling.
Further, in the step B, an interpolation method based on kernel is adopted, and an approximate image of the original scene is constructed from the low-resolution image.
Further, the interpolation method based on the kernel is an interpolation method based on a Lanczos kernel.
Further, the step C includes:
with the aim of maximizing the correlation between the observation matrix and the sparse matrix as a design goal, a color dithering method is combined with threshold distribution to generate a coding matrix framework in a self-adaptive mode;
and evaluating an observation matrix of the scene information of the low-resolution image by utilizing an encoding matrix frame, and replacing the observation value of the scene information in the encoding matrix frame with the value of the approximate image in the evaluation process.
Further, the adaptively generating the coding matrix framework by using a dithering method in combination with a threshold distribution with the purpose of maximizing the correlation between the observation matrix and the sparse matrix comprises:
let the observation matrix adopted for the image block in the Kth observation be
Wherein,
for the column vector of the observation matrix, a sparse matrix is used
A column vector of sparse bases;
let S be common in sparse matrixspaA non-zero column vector of which the set is
Where l (i) represents the position of a non-zero column vector; the correlation between the observation matrix and the sparse matrix is represented as:
wherein,
is the complement of Y;
using maximised ξYAnd
the difference between the two is used as a design target of the coding matrix framework, and the coding matrix framework is designed by combining a dithering method with threshold distribution.
Further, the evaluating the observation matrix of the scene information of the low-resolution image by using the coding matrix frame, and replacing the observation value of the scene information in the coding matrix frame with the value of the approximate image in the evaluating process, includes:
using the value f of said approximation imagelow_inp(R·mx+i,R·my+j,λi) Replacing observations of scene information in the coding matrix framework by threshold distributions of scene information regions
Estimating a threshold value of any point, wherein elements of a jth column in an ith row in an adaptive coding matrix corresponding to the jth column in the ith row in the scene information are as follows:
wherein R is the compression ratio from the original spectrum image to the low resolution image in the x dimension or the y dimension, (m)x,my) Representing a point on the low resolution image, λ representing the spectral dimension coordinate, sgn (·) representing the symbolic operand.
Further, the element of the jth column in the ith row in the adaptive coding matrix corresponding to the ith row and the jth column in the scene information is:
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the invention, an adaptive coding matrix can be generated by utilizing the low-resolution spectral information which can be acquired in the spectral imaging system based on the compressive sensing principle without adding an additional detection process and a detection device, so that the reconstruction quality of the spectral image is finally improved.
2. According to the method, high-resolution priori information is not required to be provided in the reconstruction process, the self-adaptive coding matrix can be generated by utilizing the low-resolution spectral information which can be acquired in the compressed spectral imaging system, the efficiency of acquiring scene compressed scene information is improved, and the method has the potential of realizing real-time acquisition.
3. Compared with the traditional encoding method which is not suitable for self-adaptive encoding, the method improves the quality of the reconstructed spectral image.
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The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
fig. 1 is a flow chart of an adaptive encoding method based on low resolution a priori information.
FIG. 2 is a graph comparing the implementation effects (PSNR, SSIM) of the method of the present invention and the conventional random encoding method.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
The present invention may relate to the following noun explanations:
dithering (coloring) is a method of simulating high resolution chromatography using low resolution chromatography. Common dithering methods are divided into spatial dithering and temporal dithering. Spatial dithering represents more chromatography with a certain regular recurrence through a limited chromatography. Time dithering utilizes the visual residual effect of human eyes to realize the superposition of different color spectrums on the same pixel by increasing one time dimension, thereby increasing the fineness degree of color spectrum change.
Basic knowledge to which the present invention relates:
in a compressed spectral imaging system, a low-resolution spatial detector is mostly used to receive compressed original scene information. Therefore, a conventional compressed spectrum imaging system naturally has the capability of acquiring original scene space information in a low-spatial resolution form (i.e., scene information corresponding to a low-resolution image).
Example one
As shown in fig. 1, the present embodiment discloses an adaptive encoding method based on low-resolution a priori information, including:
A. and estimating the mean value and the variance of each image block corresponding to the original spectral image by using the integral gray value and the variance of the low-resolution image, wherein the image blocks are scene information sub-regions of the original spectral image corresponding to each point of the low-resolution image. And calculating the threshold distribution of the image blocks of the original spectrum image scene information according to the mean value and the variance of each image block.
Suppose that the spatial spectrum information of the original scene is F, the scene information at the spatial dimension coordinate of (x, y) and the spectral dimension coordinate of λ is F (x, y, λ)i). The process that the coding device performs spatial compression coding on the scene information and then the scene information is received by the detector can be regarded as a spatial down-sampling process. The ideal low resolution sample of scene information is Flow_ideal(mx,my,λi) The low-resolution spatial image actually obtained by the detector is Flow(mx,my,λi) Noise of the detector is omega-N (0, sigma)s 2) Assuming that spatial down-sampling is uniform sampling, there is the following relationship:
Flow_ideal(mx,my,λi)＝Sleft_s·F(x,y,λi)·Sright_s (1)
Flow(mx,my,λi)＝Flow_ideal(mx,my,λi)+ω (2)
wherein,
ones represents a matrix with all 1 elements, where Mx,MyRepresenting the size of the low-resolution scene information in the x and y dimensions, respectively, where Nx,NyRepresenting the size of the original scene information in the x and y dimensions, respectively.
Suppose that the spatial image is compressed in x dimension and y dimension respectively in equal proportion with the proportion of R, and the low-resolution image FlowCan be regarded as that the scene image information F passes through the space compression ratio gammaspa＝R2Down-sampling and taking into account the results of detector noise effects. The size of the scene information image is equal to the size of the low resolution image, corresponding to the low resolution scene information flow(mx,my,λi) Is Fsub(mx,my,λi). Suppose Fsub(mx,my,λi) Has a mean value of
Standard deviation of
Analyzing the image mean relation of the two in the space dimension:
as can be seen, the low resolution image point (m)x,my) Spectral response intensity flow(mx,my,λi) I.e. the scene information image block Fsub(mx,my,λi) Is measured. Since the corresponding low resolution image point does not contain the changes inside the image block of the scene information, i.e. it does not provide effective information for calculating the image block
Thus, the overall standard deviation of the image at low resolution
Estimate approximately
That is, it is assumed that the change situation inside each scene information image block coincides with the change situation of the low-resolution image as a whole. Thus, the sub-region F of the scene space informationsub(mx,my,λi) Has a threshold distribution of
B. And constructing an approximate image of the original spectral image by using the low-resolution image. In the embodiment of the invention, an interpolation method based on a kernel is adopted, and an approximate image of an original scene is constructed by a low-resolution image.
This embodiment takes a typical Lanczos kernel interpolation method as an example, and explains how to construct an approximate image of an original spectral image by interpolation.
First, the interpolated image is initialized:
wherein, Flow_inp(x,y,λi) Representing a low resolution interpolated image. Then selecting a suitable window template and calculating the position of each windowThe weighting factor in the x-axis direction is as follows:
where a is a coefficient. Similarly, the weight coefficient lan (y) in the y-axis direction can be calculated, and the weight coefficient according to the two-dimensional interpolation template is the product of the one-dimensional weight coefficients, so that:
Lan(x,y)＝Lan(x)·Lan(y) (6)
and according to the initialized interpolation image, carrying out weighted average on points in the template:
thus, the interpolated image F can be obtained from the low resolution prior informationlow_inp. In some embodiments, the coefficient a takes 3, i.e., a Lanczos-3 kernel is used.
C. Based on the approximate image of the original spectral image and the corresponding threshold distribution, the adaptive coding matrix is generated by utilizing a color dithering method with the aim of maximizing the correlation between the observation matrix and the sparse matrix as a design goal.
The method comprises the following steps of firstly, taking maximization of correlation between an observation matrix and a sparse matrix as a design purpose, and adaptively generating an encoding matrix framework by using a dithering method in combination with threshold distribution.
Suppose that the K-th observation is aimed at the sub-image block Fsub(mx,my,λi) The observation matrix adopted is
Wherein,
is the column vector of the observation matrix. The thinning base adopted is
Is a column of sparse radicalsAnd (4) vectors. The correlation between the observation matrix and the sparse basis can be evaluated by the following formula:
ξ＝max{|<φi,ψj>|2},i＝1,2,...K and j＝1,2,...R2 (8)
when a low resolution image is known, it is assumed that the position and number of non-zero column vectors in the sparse basis corresponding to the image block can be calculated. S is shared in sparse base corresponding to image blockspaA non-zero column vector of which the set is
Where l (i) represents the position of the non-zero column vector. At this time, according to the orthogonal principle, the problem of recovering scene information can be converted into the problem of making the column vector of the existing projection matrix equal to the ideal sparse basis, so that only S is neededspaThe secondary observations are sufficient to accurately recover the scene information. Under this assumption, the calculation formula of the cross-correlation in formula (8) is divided into two parts:
wherein,
is the complement of Y, phiiAnd psijThe energy normalization process has been performed. Using maximised ξYAnd
the difference between them is a design goal. Suppose that the noise omega distribution of the detector used to acquire scene information obeys omega-N (0, sigma)s 2) Distribution of (2). The observed value of the acquired scene information is:
wherein
And
respectively corresponding to a low resolution image point (m)x,my) And then, acquiring information and original scene information by the detector.
An observation matrix is designed by using a dithering method and combining threshold operation, and the elements of the adaptive observation matrix of the jth column in the ith row are as follows:
wherein sgn (·) represents a symbolic operand,
element representing the jth column in the ith row in the scene information, threshold ΛijIs a compliance
A random threshold of distribution.
And evaluating an observation matrix of the scene information of the low-resolution image by utilizing an encoding matrix frame, and replacing the observation value of the scene information in the encoding matrix frame with the value of the approximate image in the evaluation process.
Combining the threshold distribution generated in step A based on the above designed coding matrix frame
And an approximation image of the original spectral image generated in step B
Under the low-resolution self-adaptive coding framework designed by the process, a self-adaptive coding matrix of the low-resolution image scene information is obtained. In the process of evaluating the observation matrix of the low-resolution image scene information by using the low-resolution self-adaptive coding frame designed by the process, when the spectral band is lambdaiUsing the value f of the approximation imagelow_inp(R·mx+i,R·my+j,λi) Instead of a new observation for a scene in the coding framework,
according to
Threshold distribution of regions
A threshold value is estimated for any point. In this way it is possible to obtain,
the corresponding element of the jth column in the ith row in the adaptive coding matrix is:
in an imaging system based on the compressed sensing principle, a common coding device cannot directly realize a coding matrix containing a negative value, so that the following equivalent transformation is performed on elements of an adaptive coding matrix in the formula (11) to adapt to the realization on an actual device:
example two
Practical tests prove the effectiveness and superiority of the scheme of the invention, and a random coding method which is wider in application range and has public confidence in the field of compressed spectrum imaging is selected for further comparison through simulation experiments. This example uses the spectral data published at the university of telawa, usa as experimental data, which has 24 spectral bands and a spatial resolution of 256 × 256. Matlab 2020a is selected as a simulation experiment platform. The algorithm was evaluated for superiority and inferiority by Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM).
The implementation effect comparison of the method provided by the invention and the traditional random coding method is shown in fig. 2, and a subgraph (1) in fig. 2 shows the advantages and disadvantages of the PSNR evaluation indexes of the two methods in different spectral bands, so that the method provided by the invention has obvious advantages compared with the random coding method; the figure (2) in figure 2 shows the advantages and disadvantages of the two methods under the SSIM evaluation index when the two methods are in different spectral bands, and it can be seen that the method provided by the invention has obvious advantages compared with a random coding method.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed.
Claims (9)
1. An adaptive encoding method based on low resolution priori information, comprising:
A. estimating the mean value and the variance of each image block corresponding to the original spectral image by using the integral gray value and the variance of the low-resolution image, wherein the image blocks are scene information sub-regions of the original spectral image corresponding to each point of the low-resolution image; calculating the threshold distribution of the image blocks of the original spectrum image scene information according to the mean value and the variance of each image block;
B. constructing an approximate image of the original spectral image by using the low-resolution image;
C. based on the approximate image of the original spectral image and the corresponding threshold distribution, the adaptive coding matrix is generated by utilizing a color dithering method with the aim of maximizing the correlation between the observation matrix and the sparse matrix as a design goal.
2. The low resolution a priori information based adaptive encoding method according to claim 1, wherein the low resolution image is a spatial down-sampling of a raw spectral image compressed in x and y dimensions in equal proportion.
3. The low resolution a priori information based adaptive encoding method of claim 2, wherein the spatial down-sampling is uniform sampling.
4. The adaptive coding method based on low-resolution priori information of any one of claims 1 to 3, wherein in the step B, a kernel-based interpolation method is adopted to construct an approximate image of an original scene from a low-resolution image.
5. The low resolution a priori information based adaptive encoding method according to claim 4, wherein the kernel based interpolation method is a Lanczos kernel based interpolation method.
6. The adaptive encoding method based on low resolution a priori information of claim 1, wherein the step C comprises:
with the aim of maximizing the correlation between the observation matrix and the sparse matrix as a design goal, a color dithering method is combined with threshold distribution to generate a coding matrix framework in a self-adaptive mode;
and evaluating an observation matrix of the scene information of the low-resolution image by utilizing an encoding matrix frame, and replacing the observation value of the scene information in the encoding matrix frame with the value of the approximate image in the evaluation process.
7. The adaptive coding method based on low resolution a priori information as claimed in claim 6, wherein the adaptively generating the coding matrix framework using a dithering method in combination with a threshold distribution with the purpose of maximizing the correlation between the observation matrix and the sparse matrix comprises:
let the observation matrix adopted for the image block in the Kth observation be
Wherein,
for the column vector of the observation matrix, a sparse matrix is used
A column vector of sparse bases;
let S be common in sparse matrixspaA non-zero column vector of which the set is
Where l (i) represents the position of a non-zero column vector; the correlation between the observation matrix and the sparse matrix is represented as:
wherein,
is the complement of Y;
using maximised ξYAnd
the difference between the two is used as a design target of the coding matrix framework, and the coding matrix framework is designed by combining a dithering method with threshold distribution.
8. The adaptive encoding method based on low-resolution priori information as claimed in claim 7, wherein the estimating of the observation matrix of the scene information of the low-resolution image by using the encoding matrix framework, and replacing the observation value of the scene information in the encoding matrix framework with the value of the approximation image in the estimating process comprises:
using the value f of said approximation imagelow_inp(R·mx+i,R·my+j,λi) Replacing observations of scene information in the coding matrix framework by threshold distributions of scene information regions
Estimating a threshold value of any point, wherein elements of a jth column in an ith row in an adaptive coding matrix corresponding to the jth column in the ith row in the scene information are as follows:
wherein R is the compression ratio from the original spectrum image to the low resolution image in the x dimension or the y dimension, (m)x,my) Representing a point on the low resolution image, λ representing the spectral dimension coordinate, sgn (·) representing the symbolic operand.
9. The adaptive encoding method based on low resolution a priori information of claim 8, wherein the element of the jth column in the ith row of the adaptive encoding matrix corresponding to the jth column in the ith row of the scene information is:
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