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A Joint Image Reconstruction Method Oriented to Compressive Sensing

A technology of joint reconstruction and compressed sensing, which is applied in the field of image processing, can solve the problem of low accuracy of compressed sensing image reconstruction, and achieve the effect of improving accuracy

Active Publication Date: 2016-08-17
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0017] The present invention is to solve the problem of low accuracy of compressed sensing image reconstruction, and proposes an image joint reconstruction method oriented to compressed sensing

Method used

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  • A Joint Image Reconstruction Method Oriented to Compressive Sensing
  • A Joint Image Reconstruction Method Oriented to Compressive Sensing
  • A Joint Image Reconstruction Method Oriented to Compressive Sensing

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specific Embodiment approach 1

[0061] Specific implementation mode 1: a method for image joint reconstruction oriented to compressed sensing is implemented in the following steps:

[0062] Step 1. Define the algorithm

[0063] Original image X ∈ R N×P , the random measurement matrix Φ in the vertical direction H ∈R M×N , basis matrix Ψ H ∈R N×N , and ML ∈R M×P , basis matrix Ψ L ∈R P×P , and M

[0064] Step 2. Orthogonal transformation

[0065] Using basis matrix Ψ H ∈R N×N and Ψ L ∈R P×P Respectively for the original image X ∈ R N×P Perform orthogonal transformation to make it sparse;

[0066] Wherein the orthogonal transformation method is as follows:

[0067] X = Ψ H S (1)

[0068] x T =Ψ L D (2)

[0069] Among them, the formula (1) is the orthogonal transformation of the original image X in the vertical direction, and S is the image signal X in the basis matrix Ψ H The sparse matrix below; formula (2) is the orthogonal transformation of the original image X in the horizontal dire...

specific Embodiment approach 2

[0088] Specific embodiment two: the difference between this embodiment and specific embodiment one is: the measurement matrix Φ described in step one H and Φ L follow a Gaussian distribution. Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0089] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: the basic matrix Ψ described in step two H and Ψ L Both are wavelet basis matrices. Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention relates to the technical field of image processing, in particular to a compressed-sensing-oriented image combination reconstruction method. The problem that compressed sensing image reconstruction accuracy is low is solved. The method comprises the steps that first, a set algorithm is input; second, orthogonal transformation is carried out; third, vertical-direction linear random measuring is carried out; fourth, horizontal-direction linear random measuring is carried out; fifth, image independent reconstruction is carried out; and sixth, image combination reconstruction is carried out. The method is used in the field of image reconstruction.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image joint reconstruction method oriented to compressed sensing. Background technique [0002] In recent years, based on the sparsity of signals, Donoho et al. proposed a new sampling theory---Compressed Sensing (CS). Different from the traditional sampling theory, compressed sensing points out that for a signal that is sparse or sparse in a transform domain, it can be sampled at a frequency much lower than that required by the Nyquist sampling theorem, and the original signal can be accurately reconstructed. The theory of compressed sensing has immediately attracted the attention of scholars in related fields at home and abroad. In just a few years, it has been successfully applied in many fields such as image processing, synthetic aperture radar imaging, and wireless sensor networks. [0003] The basic idea of ​​compressed sensing is: if the signal is sparse or co...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T11/00G06T5/00G06T9/00
Inventor 黄国兴付宁乔立岩
Owner HARBIN INST OF TECH