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Sparse representation-based positron emission tomography image super-resolution reconstruction method

A technology of super-resolution reconstruction and positron emission, which is applied in the field of image processing, can solve problems such as inaccurate assumptions of generated models and low image quality, and achieve the effect of reducing computing costs and improving quality

Inactive Publication Date: 2018-03-20
HUAIHAI INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the technical problems of inaccurate generation model assumptions and low image quality in the prior art, and propose a method for super-resolution reconstruction of positron emission tomography images based on sparse representation

Method used

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  • Sparse representation-based positron emission tomography image super-resolution reconstruction method

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Experimental program
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Effect test

Embodiment 1

[0017] Example 1: Super-resolution reconstruction method for positron emission tomography images based on sparse representation

[0018] Design a method for super-resolution reconstruction of positron emission tomography images based on sparse representation, including the following steps:

[0019] Step 1: Establish a global model of positron emission tomography image super-resolution based on sparse representation: Y=SHX, where H represents a blur filter, S represents a sampling operator, X represents a high-resolution image, and Y represents a low-resolution image image;

[0020] Step 2: Establish a local model for super-resolution of positron emission tomography images based on sparse representation: min||α||0δ.t.||FD ια-Fy|| 2 2 ≤ε, where F is a linear feature extraction operator; min||α||1δ.t.||FD ι α-Fy|| 2 2 ≤ε; In the formula, λ is to balance a certain dilution and the accuracy of y estimation; In the formula

[0021] Step 3: Constraint enhancement for g...

Embodiment 2

[0028] Embodiment 2: Test verification and analysis

[0029] Through simple experimental testing and analysis of the results, the obtained results are shown in Table 1 and Table 2:

[0030] Table 1 Robustness to Noise

[0031]

[0032]

[0033] Table 2 Impact of global constraints

[0034] method

[0035] It can be seen from Table 1 and Table 2 that the method of the present invention has better robustness to noise and better performance on global constraints.

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Abstract

The invention relates to the technical field of image processing, and particularly provides a sparse representation-based positron emission tomography image super-resolution reconstruction method. Themethod comprises the following four steps of establishing a sparse representation-based positron emission tomography image super-resolution global model, and establishing a local model of the super-resolution of a positron emission tomography image based on sparse representation, carrying out the global reconstruction constraint enhancement and carrying out the global optimization. According to the method, the calculation cost can be reduced, and the image quality can be improved. The method has the self-adaptive robustness to the noise.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for super-resolution reconstruction of positron emission tomography images based on sparse representation. Background technique [0002] Standard Definition (SD) refers to VCD, DVD, TV programs and other videos with a resolution of about 400 lines. High Definition (HD) refers to a video with at least 720 lines of progressive scan or 1080 lines of interlaced scan and a screen aspect ratio of 16:9. Comparing the specifications of standard-definition video and high-definition video, it can be seen that the difference between the two is mainly in resolution and aspect ratio, and the key to conversion is the improvement of video resolution. [0003] Image super-resolution reconstruction technology is a technical solution to improve image resolution. Currently, there are three commonly used image super-resolution methods: interpolation-based, reconstruction-based and...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 康家银
Owner HUAIHAI INST OF TECH
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