阅读说明：本技术 一种基于压缩感知的数据链系统终端图像压缩方法 (Data link system terminal image compression method based on compressed sensing ) 是由 黄东 于 2020-12-02 设计创作，主要内容包括：本发明涉及一种基于压缩感知的数据链系统终端图像压缩方法,属于数据链图像处理领域。本发明的主要过程是：首先,数据链系统终端A通过数字摄像头获取图像数据信息；然后,通过小波变换将图像数据信息稀疏化后得到稀疏向量组S,将测量矩阵和稀疏基矩阵与稀疏向量组相乘进行降维,得到维数远小于稀疏向量组的测量值Y,将测量值通过天线传输至数据链系统另一终端B；最后在终端B通过压缩感知还原算法将测量值还原为原始图像数据信息。(The invention relates to a data link system terminal image compression method based on compressed sensing, and belongs to the field of data link image processing. The main process of the invention is as follows: firstly, the data chain system terminal A acquires image data information through a digital camera (ii) a Then, thinning image data information through wavelet transformation to obtain a sparse vector group S, multiplying a measurement matrix and a sparse basis matrix with the sparse vector group for dimension reduction to obtain a measurement value Y with dimension far smaller than that of the sparse vector group, and transmitting the measurement value to another terminal B of the data chain system through an antenna; and finally, reducing the measured value into original image data information at the terminal B through a compressed sensing reduction algorithm.)

1. A data chain system terminal image compression method based on compressed sensing comprises the following steps:

1) acquiring image information: acquiring image information by using a digital camera in a data link system terminal A;

2) after acquiring image information, the data chain system terminal A transmits the image data information to a compression module, and the image information is thinned in the compression module through wavelet transformation to obtain a sparse vector group S;

3) multiplying the measurement matrix by the sparse vector group in a compression module to reduce the dimension to obtain a measurement value Y with the dimension far smaller than that of the sparse vector group, and outputting the measurement value Y by the compression module;

4) transmitting the measured value to a baseband module and a power amplifier module for processing, transmitting the processed measured value to a data link system terminal B through an antenna, transmitting the received signal to the power amplifier module and the baseband module of the terminal B for processing after the terminal B receives the signal, and outputting a measured value Y to a decompression module;

5) in a decompression module of the terminal B, the original signal is obtained by an orthogonal matching tracking algorithm after the measured value, the sparse basis and the measurement matrix are known.

2. The method for compressing the image of the data chain system terminal based on the compressed sensing as claimed in claim 1, wherein the step 2) of thinning the image information by wavelet transform to obtain the sparse vector set S comprises:

1) generating a wavelet transformation basis matrix psi, wherein signals are sparse on the orthogonal basis;

2) wavelet transformation makes the image sparse: s ═ Ψ ·, α, where α is the original signal, Ψ ∈ R^nxl，α∈R^lxl；

3) And outputting the sparse signal S.

3. The method as claimed in claim 1, wherein the step 3) of multiplying the measurement matrix by the sparse vector set in the compression module for dimension reduction to obtain the measurement value Y with dimension much smaller than the sparse vector set comprises:

1) generating a random Gaussian measurement matrix P, wherein P belongs to R^mxn；

2) Optimizing a random Gaussian matrix to obtain a measurement matrix phi, wherein phi belongs to R^mxn；

3) And multiplying the measurement matrix by the sparse vector group for dimensionality reduction: y ═ Φ ═ S ═ Φ ═ Ψ ·, α, where α is the original signal, Φ ∈ R^mxn，Ψ∈R^nxl，α∈R^lxlWherein m < l;

4) the measured value Y is output.

4. The method as claimed in claim 1, wherein the step 5) of obtaining the original signal through an orthogonal matching pursuit algorithm based on the known measured values, sparse bases and measurement matrices:

1) the known measurement matrix phi (phi epsilon. R)^mxn) And the measured value Y (Y. epsilon. R)^mxl) And performing sparse signal S through an orthogonal matching pursuit algorithm of a sparse decomposition algorithm (S is belonged to R)^nxl) Reduction:

2) will sparsely signal S (S is belonged to R^nxl) Obtaining an original signal alpha (alpha belongs to R) by performing wavelet inverse transformation^lxl)；

3) Outputting the original image information.

Technical Field

The invention relates to a data link system terminal image compression method based on compressed sensing, and belongs to the field of data link image compression research.

Background

The data link refers to a link for communicating data, and in military, the data link is a data network, just like the internet, and only one data terminal needs to obtain required information from the data link, and the terminal can be used to add things to the data link network.

The data chain is generally used for military projects, and the most perfect military data chain is that all units contribute data information. The data chain is used as a 'battlefield nerve' and plays a great role, and the data chain can be divided into three categories according to the application range: a general tactical data chain, an intelligence level data chain, and a weapons level data chain. The intelligence-level data chain is mainly used for transmitting image and video data detected by various detection platforms, and particularly in large strategic detection, the intelligent monitoring system is long in voyage, large in coverage area, high in real-time requirement and huge in image data amount, so that the required communication bandwidth is large, and the data transmission rate is high.

The intelligence data chain in the present stage is difficult to realize, the communication bandwidth in the data chain is large by using the traditional image data sampling theorem and the compression theorem, and the technical research and development difficulty is large, the structure of the terminal is complex and the cost is high due to high communication bandwidth requirement. Therefore, it is an important issue to study how to transmit more data information under lower bandwidth requirement.

Compressed sensing is a technology for finding sparse solution of an underdetermined linear system, and is a new sampling theory, which obtains discrete samples of a signal by random sampling under the condition that the sampling rate is far less than the Nyquist sampling rate by developing the sparse characteristic of the signal, and then perfectly reconstructs the signal through a nonlinear reconstruction algorithm. The compressed sensing theory research mainly comprises three aspects of signal sparse representation, measurement matrix construction and reconstruction algorithm design. The compressed sensing theory completes the data compression process in the sampling process.

The compressed sensing theory is applied to the image data compression of a data chain system terminal, the power consumption of a sampled image can be reduced, and simultaneously, the image information can be compressed to a greater degree, so that more image data information can be transmitted under the same bandwidth requirement, and the purposes of reducing the technical difficulty of communication bandwidth research and development in a data chain and reducing the cost are achieved.

Disclosure of Invention

The invention discloses a data chain system terminal image compression method based on compressed sensing, aiming at the problem that the communication bandwidth technology is difficult to research and develop due to the huge data quantity of data of a data chain image in the prior art. The method comprises the following steps: firstly, a data chain system terminal A acquires image data information alpha through a digital camera; then, thinning image data information through wavelet transformation to obtain a sparse vector group S, multiplying a measurement matrix and a sparse basis matrix with the sparse vector group for dimension reduction to obtain a measurement value Y with dimension far smaller than that of the sparse vector group, and transmitting the measurement value to another terminal B of the data chain system through an antenna; and finally, reducing the measured value into original image data information at the terminal B through a compressed sensing reduction algorithm.

The invention provides a data chain system terminal image compression method based on compressed sensing, which comprises the following steps:

step one, acquiring image information: acquiring image information by using a digital camera in a data link system terminal A;

step two, after the data chain system terminal A acquires the image information, transmitting the image data information to a compression module, and thinning the image information in the compression module through wavelet transformation to obtain a sparse vector group S;

step three, multiplying the measurement matrix by the sparse vector group in a compression module for dimensionality reduction to obtain a measurement value Y with dimensionality far smaller than that of the sparse vector group, and outputting the measurement value Y by the compression module;

transmitting the measured value to a baseband module and a power amplifier module for processing, transmitting the processed measured value to a data link system terminal B through an antenna, transmitting the received signal to the power amplifier module and the baseband module of the terminal B for processing after the terminal B receives the signal, and outputting a measured value Y to a decompression module;

and step five, in a decompression module of the terminal B, knowing a measured value, a sparse base and a measurement matrix, and solving an original signal through an orthogonal matching tracking algorithm.

The invention has the advantages that:

1. according to the invention, the compressed sensing is applied to the data link system, and the compressed sensing theory completes the data compression process in the sampling process, so that the power consumption of the data link system terminal is greatly reduced compared with the traditional data acquisition and compression method;

2. according to the invention, the compressed sensing is applied to a data chain system, and under the condition that the sampling rate is far less than the Nyquist sampling rate, discrete samples of signals are obtained by random sampling, so that the data volume of sampling is greatly reduced;

3. according to the invention, the compressed sensing is applied to the data chain system, and the dimension of the output measurement value after the dimension reduction is compressed is far smaller than that of the original signal, so that more image data information can be transmitted under the same data bandwidth, and the requirement on the data bandwidth of the data chain is reduced.

Drawings

FIG. 1 is a schematic flow chart of the present invention for image compression to restoration;

FIG. 2 is a schematic flow chart of the present invention for deriving sparse signals;

FIG. 3 is a schematic flow chart of the measured values obtained by the present invention;

FIG. 4 is a schematic flow chart of the present invention for restoring the measured value to the original image;

FIG. 5 is a schematic diagram of an input original image of the present invention;

FIG. 6 is a schematic diagram of an output restored image according to the present invention.

Detailed Description

The invention provides a data link system terminal image compression method based on compressed sensing, and the experiment of the invention is realized by MATLAB platform simulation, and the specific operation comprises the following steps:

with reference to fig. 1, a method for compressing an image at a terminal of a data link system based on compressed sensing includes the following steps:

step one, combining with fig. 5, acquiring image information: acquiring image information by using a digital camera in a data link system terminal A;

1) acquiring image data information by using a digital camera with 512x512 pixels in the test;

2) the data chain system terminal A generates a matrix alpha for the image data information, and the alpha belongs to R^512x512。

Step two, with reference to fig. 2, after the data link system terminal a acquires image information, transmitting the image data information to a compression module, and thinning the image information in the compression module through wavelet transformation to obtain a sparse vector group S;

1) knowing the dimensionality of the original image data information as R^lxl＝R^512x512I.e. l is 512, the dimension of the wavelet transform base is R^nxl＝R^512x512I.e., n is 512;

2) generating a random Gaussian wavelet transform basis matrix Ψ (Ψ ∈ R)^512x512) The image data signal is sparse on the orthogonal basis;

3) wavelet transform lets graphImage thinning: s ═ Ψ θ α, where α is the original signal, Ψ ∈ R^nxl，α∈R^lxl；

4) Outputting a sparse signal S (S is belonged to R)^nxl)。

Step three, combining with the figure 3, multiplying the measurement matrix by the sparse vector group in a compression module to reduce the dimension to obtain a measurement value Y with the dimension far smaller than that of the sparse vector group, and outputting the measurement value Y by the compression module;

1) taking the measured value row dimension as m-205

2) Generating a random Gaussian measurement matrix P, wherein P belongs to R^mxn；

3) Optimizing a random Gaussian matrix, reducing the column correlation of the random Gaussian measurement matrix to obtain a measurement matrix phi, wherein phi belongs to R^mxn；

3) And multiplying the measurement matrix by the sparse vector group for dimensionality reduction: y ═ Φ ═ S ═ Φ ═ Ψ ·, α, where α is the original signal, Φ ∈ R^mxn，Ψ∈R^nxl，α∈R^lxlWherein m < l;

transmitting the measured value to a baseband module and a power amplifier module for processing, transmitting the processed measured value to a data link system terminal B through an antenna, transmitting the received signal to the power amplifier module and the baseband module of the terminal B for processing after the terminal B receives the signal, and outputting a measured value Y to a decompression module;

and step five, combining the graph 4, in a decompression module of the terminal B, knowing a measured value, a sparse base and a measurement matrix, and solving an original signal through an orthogonal matching tracking algorithm.

1) The known measurement matrix phi (phi epsilon. R)^mxn) And the measured value Y (Y. epsilon. R)^mxl) And performing sparse signal S through an orthogonal matching pursuit algorithm of a sparse decomposition algorithm (S is belonged to R)^nxl) Reduction:

2) will sparsely signal S (S is belonged to R^nxl) And performing wavelet inverse transformation to obtain an original signal alpha (alpha epsilon R)^lxl)；

3) Referring to fig. 6, restored original image information is output.