The mean shift method of chaotic sequences in the study of compressive sensing

The mean shift method of chaotic sequences in the study of compressive sensing This paper presents a novel reconstruction approach of digital image in compressive sensing by the use of mean shift of different chaotic sequence to the measurement matrix. This matrix preserves better details of the structures of the recovered images, and enables a systematic construction of the measurement matrices of it. This proposed approach provides not only visible Peak Signal to Noise Ratio improvements over state-of-the-art methods (e.g. the Gaussian random matrix method) but also better preservation of the image structures during compression, which in turn enables better visual quality in image recovery, as illustrated in our experimental results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

The mean shift method of chaotic sequences in the study of compressive sensing

Loading next page...
 
/lp/springer_journal/the-mean-shift-method-of-chaotic-sequences-in-the-study-of-compressive-BMSvEQsNDj
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
ISSN
1868-8071
eISSN
1868-808X
D.O.I.
10.1007/s13042-016-0534-y
Publisher site
See Article on Publisher Site

Abstract

This paper presents a novel reconstruction approach of digital image in compressive sensing by the use of mean shift of different chaotic sequence to the measurement matrix. This matrix preserves better details of the structures of the recovered images, and enables a systematic construction of the measurement matrices of it. This proposed approach provides not only visible Peak Signal to Noise Ratio improvements over state-of-the-art methods (e.g. the Gaussian random matrix method) but also better preservation of the image structures during compression, which in turn enables better visual quality in image recovery, as illustrated in our experimental results.

Journal

International Journal of Machine Learning and CyberneticsSpringer Journals

Published: Apr 30, 2016

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off