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

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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

References

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