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Bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT)

A two-dimensional compression and image acquisition technology, applied in image communication, television, electrical components, etc., can solve the problem of low reconstruction ability of the reconstruction matrix signal, improve the signal reconstruction effect, improve the signal reconstruction ability, widely The effect of the application foreground

Inactive Publication Date: 2013-10-09
GUANGXI UNIVERSITY OF TECHNOLOGY +1
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  • Application Information

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Problems solved by technology

[0004] In order to solve the problem of low signal reconstruction capability and measurement matrix design of the reconstruction matrix, the present invention provides a two-dimensional compressed sensing image acquisition based on DCT and DFT and the refactoring method

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  • Bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT)
  • Bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT)
  • Bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT)

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specific Embodiment approach 1

[0023] Specific implementation mode one: according to the instructions attached figure 1 This embodiment will be specifically described. A two-dimensional compressed sensing image acquisition and reconstruction method based on DCT and DFT, the process of the method is:

[0024] Step 1: Generate 0-1 sparse matrix , , . Each row vector of contains no less than 2 elements with a value of 1, Each column vector of contains at least one element with value 1. with are all natural numbers. Generate reconstruction matrix , while making the optimization matrix , is the sparse transformation basis. It can be a DCT matrix (discrete cosine matrix) or a DFT matrix (discrete Fourier matrix);

[0025] Step 2: Set the number of iterations i The initial value of 0, set the iteration error ;

[0026] Step 3: Calculation by separate test of Jarque-Bera The number of rows for which the real and imaginary parts of each column and row follow a Gaussian distribution (the ...

specific Embodiment approach 2

[0039] Embodiment 2: This embodiment is a further description of a DCT and DFT-based two-dimensional compressed sensing image acquisition and reconstruction method described in Embodiment 1. In step 2, the iterative error is set err 1 for , err 2 for , err 3 for .

specific Embodiment approach 3

[0040] Specific embodiment three: This specific embodiment is a further description of a two-dimensional compressed sensing image acquisition and reconstruction method based on DCT and DFT described in specific embodiment one, and the orthogonal normalization described in step four Each row vector, and then the specific process of unitizing each column vector is: first Orthogonalize the row vectors, then normalize the row vectors, and finally normalize the column vectors.

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Abstract

The invention discloses a bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT), belongs to the technical field of designs of measurement matrixes and optimization of reconstruction matrixes in the compressed sensing process and provides a method for firstly determining the measurement matrix and a sparse matrix and then optimizing the reconstruction matrix. In a measurement stage, the 0-1 sparse matrix is adopted; in a reconstruction stage, a Gaussian matrix is adopted; and therefore, an after-optimization method capable of easily implementing hardware and guaranteeing a signal reconstruction effect can be realized. The method comprises the following steps of: performing row vector orthogonal normalization and column vector unitization on the reconstruction matrix obtained by the (i-1)th iteration calculation through ith iteration, optimizing the reconstruction matrix on the basis of maximum values of absolute values of relevant coefficients among row and column vectors, the convergence stability of row vector modules and the number of rows and the number of columns which obey the Gaussian distribution, and finishing the after-optimization on measurement data subjected to one-dimensional and two-dimensional sparse transformation and the measurement matrixes by calculating a transitional matrix and a proximity matrix. The method lays a foundation for the compressed sensing from theoretical research to industrialization.

Description

technical field [0001] The invention belongs to the technical field of compressed sensing, and specifically provides a two-dimensional compressed sensing image acquisition and reconstruction method based on DCT and DFT. Background technique [0002] The design of the measurement matrix and the optimization of the reconstruction matrix in compressed sensing are the key factors in the reconstruction of relational signals. Although random matrices (Gaussian, Bernoulli and other matrices) have good signal reconstruction ability and universality, but due to the difficulty of hardware implementation, people turn to deterministic matrices with poor properties and easy hardware implementation (Toplitz, loops, polynomials, 0-1 sparse matrices, etc). The 0-1 sparse matrix is ​​not only easy to implement in hardware, but also requires a small storage space and a fast operation speed. However, the row-column correlation of the 0-1 sparse matrix is ​​poor, and the use of sparse matrix ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N7/26H04N7/30
Inventor 程涛
Owner GUANGXI UNIVERSITY OF TECHNOLOGY
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