Method for reconstructing image based on blind compressed sensing module

A technology of compressed sensing and image reconstruction, which is applied in the field of image processing, can solve the problems of signal sparseness and strict limitation of initial basis, and achieve strong robustness and avoid high requirements for sparseness

Inactive Publication Date: 2013-11-20
XIDIAN UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to propose an image reconstruction method based on blind compressive sensing to solve the problem that the signal must be sparse during compressed obser

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  • Method for reconstructing image based on blind compressed sensing module
  • Method for reconstructing image based on blind compressed sensing module
  • Method for reconstructing image based on blind compressed sensing module

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Embodiment Construction

[0026] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0027] Step 1, perform redundant transformation on the input image I to obtain the redundant matrix X:

[0028] (1a) Starting from the element of row i and column j of the input image I, take out p*q image blocks sequentially from top to bottom and from left to right. Each time a block is taken, i is incremented by 1 and j remains unchanged. Wait until When i is added to c-p+1, j is added with 1, at this time i becomes 1, and this is repeated until i and j are respectively added to c-p+1 and d-q+1, where c is the input The number of rows of image I, d is the number of columns of input image I;

[0029] (1b) Connect each column of the extracted image block end-to-end in order to obtain a column vector, and then sequentially combine all the column vectors to obtain a redundancy matrix X.

[0030] Step 2, perform compressed observation on the redundant matrix X:

[0031] Le...

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Abstract

The invention discloses a method for reconstructing an image based on a blind compressed sensing module and mainly aims at solving problems that only sparse signals can be monitored by traditional compressed sensing, and the quality of the reconstructed image is poor. The method is realized by the following steps: (1) carrying out redundance transformation on an input image to obtain a redundance matrix; (2) carrying out compressed observation on the redundance matrix under an observation matrix; (3) updating a sparse matrix by an OMP (Orthogonal Matching Pursuit) algorithm in an adaptive way according to a compressed observation result; (4) updating a sparse base by a singular value decomposition method according to the updated sparse matrix; (5) multiplying the updated sparse matrix and the updated sparse base to obtain a reconstructed image redundance matrix; and (6) carrying out reverse redundance transformation on the reconstructed image redundance matrix to obtain a reconstructed image; and evaluating the reconstructed image by the peak signal-to-noise ratio of the image. The method has the advantages of high reconstructed image quality and great noise inhibiting effect and can be applied in image denoising and image compression.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a compressed sensing image reconstruction method, which can be used for image denoising and image compression. Background technique [0002] Compressed sensing theory can restore signals from a small amount of observation data, and has been applied to the field of image reconstruction. In practical applications, because the processed image signals often do not have sparsity, the traditional compressed sensing theory often needs Choose a fixed sparse base, the existing methods such as wavelet transform-based compressive sensing technology, discrete cosine transform-based compressive sensing technology, that is, first sparse under a fixed sparse base, and then observe the sparse signal, and observe the signal The more sparse the signal is, the better the effect of restoring and reconstructing the signal is, but this fixed sparse basis has a limited scope of app...

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Application Information

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IPC IPC(8): G06T5/00G06T9/00
Inventor 王勇吴超田洪伟张凤郑娜楚天许录平
Owner XIDIAN UNIV
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