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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2017

30.04.2016 | Original Article

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

verfasst von: Guoming Chen, Qiang Chen, Shun Long, Weiheng Zhu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2017

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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.
Vorheriger Artikel Scheduling for multi-stage applications with scalable virtual resources in cloud computing
Nächster Artikel Relevance vector machines using weighted expected squared distance for ore grade estimation with incomplete data

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Metadaten
Titel
The mean shift method of chaotic sequences in the study of compressive sensing
verfasst von
Guoming Chen
Qiang Chen
Shun Long
Weiheng Zhu
Publikationsdatum
30.04.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0534-y

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