Multivariate compressive sensing reconstruction method based on wavelet HMT (Hidden Markov Tree) model

A compressed sensing reconstruction and multi-variable technology, applied in the field of image processing, can solve problems such as the inability to determine the non-zero support of unknown coefficients, and achieve the effect of improving reconstruction quality, strengthening sparsity, and reducing measurement

Active Publication Date: 2013-05-01
CHINA JILIANG UNIV
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Problems solved by technology

[0005] In order to solve the problem that the multivariate tracking algorithm existing in the prior art cannot determine the non-zero support of the unknown coefficient, the present invention proposes a multivariate compressed sensing reconstruction method based on the wavelet HMT model, including the following steps:

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  • Multivariate compressive sensing reconstruction method based on wavelet HMT (Hidden Markov Tree) model
  • Multivariate compressive sensing reconstruction method based on wavelet HMT (Hidden Markov Tree) model
  • Multivariate compressive sensing reconstruction method based on wavelet HMT (Hidden Markov Tree) model

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[0040] The present invention will be further described below in conjunction with accompanying drawing.

[0041] refer to figure 1 , the multivariate compressive sensing reconstruction method based on wavelet HMT model of the present invention, comprises the following steps:

[0042] step one , the wavelet transform is performed on the image, the low-frequency transformation coefficients are retained, and the high-frequency transformation coefficients are multivariately compressed and sampled to obtain the multivariate measurement vector Y :

[0043] Y = AX ,in X yes N x Q dimensional high-frequency transform coefficient matrix, A yes K x N dimensional random sensing matrix, where K N ;

[0044] step two , using the existing MPA algorithm to reconstruct the initial image;

[0045] step three , to calculate the posterior state probability that the high-frequency transformation coefficient of the reconstructed image is in a state of large value:

[0046] Es...

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Abstract

The invention discloses a multivariate compressed sensing reconstruction method based on a wavelet HMT (Hidden Markov Tree) model. The multivariate compressive sensing reconstruction method comprises the following steps of: carrying out wavelet transformation on an image, preserving a low-frequency transform coefficient, and carrying out multivariate compressive sampling on a high-frequency transform coefficient to obtain a multivariate measurement vector Y; reconstructing an initial image by using the existing MPA (Multivariate Pursuit Algorithm); calculating the posterior state probability of the high-frequency transform coefficient of the reconstructed image in a large magnitude state; updating a weighted value of the high-frequency transform coefficient; reconstructing the image by using a WMPA algorithm; returning to the second step if the condition that an appointed repeated interation weighting reconstruction times I is equal to 2 is not obtained; or else, obtaining the reconstructed image of the original image. The multivariate compressive sensing reconstruction method based on the wavelet HMT model, disclosed by the invention, has a good reconstruction effect and is applicable to both medical images and natural images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image multivariate compressed sensing reconstruction method, which can be used to reconstruct medical images and natural images. Background technique [0002] With the development of new imaging systems with high fidelity and high resolution, people are faced with problems such as high cost, low efficiency, and waste of resources for data storage and transmission when acquiring massive image data, so new data acquisition is required and reconstructed theoretical framework to solve the above problems brought by traditional methods. [0003] Compressive Sensing (CS) is a new signal sampling theoretical framework for this purpose, which was first proposed by American scholars Donoho and Cand?s, such as: Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306; Cand?s E J, Romberg J, Tao T. Robust uncertainty principles:...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50
Inventor 武娇顾兴全
Owner CHINA JILIANG UNIV
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