Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Compressive sensing reconstruction method based on pseudo-inverse adaptive matching pursuit

An adaptive algorithm and compressed sensing technology, applied in image data processing, 2D image generation, calculation, etc., can solve unreachable problems

Active Publication Date: 2013-10-02
NORTHWESTERN POLYTECHNICAL UNIV
View PDF2 Cites 23 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although the reconstruction methods based on these algorithms can accurately reconstruct the original image, they have a common shortcoming, that is, the sparsity of the signal must be known, which is often unreachable in practical applications.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Compressive sensing reconstruction method based on pseudo-inverse adaptive matching pursuit
  • Compressive sensing reconstruction method based on pseudo-inverse adaptive matching pursuit
  • Compressive sensing reconstruction method based on pseudo-inverse adaptive matching pursuit

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0038] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0039] The hardware environment used for implementation is: Pentium-43G computer, 2GB memory, 128M graphics card, and the running software environment is: Mat1ab7.0 and windowsXP. We have realized the new algorithm proposed by the present invention with Matlab programming language. The image data adopts 512×512 images of Lena, Pepper and Barbara.

[0040] The present invention is specifically implemented as follows:

[0041] Step 1 Projection measurement: For an original image A with a dimension of N×K, select a Gaussian random matrix Φ with a dimension of M×N (M≤N) to perform projection measurement on the image A to obtain a measurement signal matrix Y. Each element of the selected matrix Φ here has a mean of 0 and a variance of Gaussian distribution. The specific process of projection measurement is as follows:

[0042] Y=Φ·A

[0043] Since the dimensio...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a compressive sensing image reconstruction method based on a pseudo-inverse adaptive matching pursuit (PIAMP), which is characterized by comprising the steps that a gauss random matrix is selected for projecting an image to form a measurement signal matrix; an image sparse base is constructed; each column of the measurement signal matrix is reconstructed by adopting the PIAMP to form a sparse coefficient of each column of the image; and the sparse coefficients of the columns of the image are arrayed and subjected to cosine transformation to form a reconstruction image. The method particularly takes account of the dictionary relevance, iteration stage division and the like in a sparse coefficient reconstruction process, so that selection of the sparse coefficients is more accurate, and the precision of image reconstruction is higher.

Description

technical field [0001] The invention relates to a new method for compressed sensing image reconstruction, that is, compressed sensing image reconstruction based on pseudo-inverse adaptive matching algorithm (Pseudo-inverse Adaptive Matching Pursuit, PIAMP). It can be widely used in various image processing systems supported by compressed sensing theory. Background technique [0002] In the traditional image coding and transmission process, the image must first be sampled according to the Nyquist sampling theorem (the sampling frequency should be greater than or equal to twice the highest frequency in the analog signal), and then the image should be sampled under a set of sparse bases. The sampled data is transformed to obtain the sparse expression coefficients of the image, and then the threshold value processing method is used to discard small data that is zero or close to zero, and only the large coefficients obtained by image transformation are transmitted and processed. ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06T11/00
Inventor 李晖晖曾艳郭雷
Owner NORTHWESTERN POLYTECHNICAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com