Hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity

A compressed sensing and hyperspectral technology, applied in the field of image processing, which can solve the problems of low image quality and insufficient image processing to provide reliable data sources.

Active Publication Date: 2016-04-20
XIDIAN UNIV
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Although this method can transfer the PCA dimension reduction step from the encoding end to the decoding end, and the reconstruction time is

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  • Hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity
  • Hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity
  • Hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity

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[0027] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0028] Refer to attached figure 1 , the implementation steps of the present invention are as follows:

[0029] Step 1, input hyperspectral data X ori , get vectorized hyperspectral data X.

[0030] Enter hyperspectral data in Indicates the real number space, H and P respectively indicate the number of pixels in the horizontal and vertical directions of the hyperspectral data in the spatial domain, N indicates the total number of bands, X n ={X i,j,n ,i=1,...,H,j=1,...,P} means hyperspectral data X ori The nth band of X i,j ={X i,j,1 ,...,X i,j,N} represents a pixel of hyperspectral data;

[0031] The hyperspectral data X ori Each pixel of each column is stacked one by one to obtain vectorized hyperspectral data

[0032] Step 2, sampling the vectorized hyperspectral data X.

[0033] Use the block diagonal sampling matrix for the vectorized hyp...

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Abstract

The invention discloses a hyper-spectral compression perception reconstruction method based on nonlocal total variation and low-rank sparsity, and mainly solves the problems in the prior art that reconstruction accuracy is low and the effect is poor after compressed sampling of hyper-spectral data. The hyper-spectral compression perception reconstruction method comprises the steps that 1. the hyper-spectral data are inputted and vectorized; 2. the vectorized hyper-spectral data are sampled so that sampling data are obtained; 3. initial reconstruction of the sampling data is performed; 4. the initially reconstructed data are clustered; 5. the sampling data are classified according to the type of image elements so that various types of sampling data are obtained; 6. a secondary reconstruction model is constructed; and 7. The secondary reconstruction model is solved according to various types of sampling data so that the optimal data of secondary reconstruction are obtained, and the data act as the final reconstruction data. The idea of nonlocal total variation and clustering is introduced on the basis of low-rank sparse reconstruction so that the hyper-spectral compression perception reconstruction method has advantages of high reconstruction accuracy and great effect and can be used for hyper-spectral data imaging.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to a hyperspectral data compression sensing reconstruction method, which can be used for hyperspectral imaging. Background technique [0002] Hyperspectral data is composed of hundreds of very narrow bands. The high-resolution characteristics of its space and band direction make hyperspectral data have a large data dimension. The huge amount of information has a huge impact on the storage, transmission and subsequent processing of hyperspectral data. Here comes the difficulty. The traditional compression sampling method uses the Nyquist sampling rate to uniformly or non-uniformly sample the signal, and then compresses it through algorithms such as prediction, transformation, and vector quantization. This high-redundancy sampling and recompression process causes a great waste of resources, which puts great pressure on airborne or spaceborne applications with low power ...

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Application Information

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IPC IPC(8): G06T9/00
CPCG06T9/001
Inventor 孟红云张小华杨星田小林陈佳伟钟桦
Owner XIDIAN UNIV
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