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Hyper-spectral compressive imaging method based on three-dimensional tensor compressed sensing

A compressed sensing and compressed imaging technology, applied in the field of signal processing, can solve problems such as difficulty in compressed sensing work, damage to multi-dimensional signal structure information, information loss, etc., and achieve the effects of shortening running time, reducing complexity, and improving recovery effects.

Inactive Publication Date: 2015-09-23
XIDIAN UNIV
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Problems solved by technology

This method improves the reconstruction effect to a certain extent, but still uses the method of vectorizing the image, which destroys the structural information between multi-dimensional signals, causes information loss, and brings difficulties to the subsequent compressed sensing work.

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  • Hyper-spectral compressive imaging method based on three-dimensional tensor compressed sensing
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  • Hyper-spectral compressive imaging method based on three-dimensional tensor compressed sensing

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Embodiment Construction

[0032] refer to figure 1 , the implementation steps of the present invention are as follows:

[0033] Step 1. Input a hyperspectral image, represent it as a 3D tensor Z ,in, I 1 , I 2 , I 3 are the sizes of the three dimensions of the hyperspectral image, respectively.

[0034] Step 2. Construct the observation matrix.

[0035] Let the sampling rates of the three dimensions be S 1 , S 2 , S 3 , to construct an observation matrix Φ with a Kronecker structure:

[0036] Φ = Φ 3 ⊗ Φ 2 ⊗ Φ 1 ,

[0037] Among them, Φ 1 、Φ 2 、Φ 3 are sizes J 1 ×I 1 、J 2 ×I 2 and J 3 ×I 3 The Gaussian random matrix of is used as the observation matrix of three dimensions respectively, and the observation matrix Φ of the i-th dimension i The number of rows J i By the sampling rate S of the i-th dimension i and the size...

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Abstract

The invention discloses a hyper-spectral compressive imaging method based on three-dimensional tensor compressed sensing, and the method is mainly used for solving the problem that the structural information of multidimensional data in a multi-dimensional compressed sensing process is broken in prior art. The implementation steps of the method are as follows: simultaneously performing compressed sampling on three dimensionalities of a hyper-spectral image on the basis of not vectoring the hyper-spectral image via introducing a tensor method to obtain a measuring value; then calculating sensing matrixes on the three dimensionalities; then using a tensor orthogonal matching pursuit algorithm to calculate a sparse coefficient; finally finishing reconstitution of the hyper-spectral image according to a multi-dimensional sparse representation of the hyper-spectral image. The experimental result shows that the hyper-spectral compressive imaging method based on three-dimensional tensor compressed sensing of the invention is fast in reconstitution speed and good in effect in comparison with other traditional compressed sensing methods in same sampling rate, and could be used for obtaining a remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and relates to a hyperspectral compression imaging method, which can be used for remote sensing image acquisition. Background technique [0002] Compressed sensing is a new sampling theory developed in the field of image processing technology in recent years. By using the sparse characteristics of the signal, it can realize the accurate recovery of information under the condition of much smaller than the traditional Nyquist sampling rate. Compressive sensing algorithms usually operate on one-dimensional signals. When the signal dimension exceeds one dimension, it is usually vectorized, the signal is converted into a one-dimensional vector, and then the compressed sensing operation is performed. However, there is a structural correlation between the dimensions of the multidimensional signal. Simple vectorization will destroy the structure between the dimensions of the multidimensional si...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 杨淑媛焦李成金莉刘芳马晶晶马文萍熊涛刘红英李斌张继仁
Owner XIDIAN UNIV
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