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  Image restoration by combining local genetic algorithm with adaptive pre-conditioning

Jiang, T. Z., & Evans, D. J. (2001). Image restoration by combining local genetic algorithm with adaptive pre-conditioning. International Journal of Computer Mathematics, 76(3), 279-295. doi:10.1080/00207160108805025.

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 Urheber:
Jiang, Tianzi Z.1, Autor           
Evans, D. J., Autor
Affiliations:
1MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634574              

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Schlagwörter: Image restoration; Adaptive pre-conditioning genetic algorithm; Wiener and median filters
 Zusammenfassung: Image restoration is an essential preprocessing step for many image analysis applications. For this issue, the most common problem is that some interesting structures in the image will be removed from the concerned image during noise suppression. Such interesting structures in an image often correspond to the discontinuities in the image. In this paper, we propose a novel efficient method for image restoration. The central idea in this method is to combine the hybrid genetic algorithm with adaptive pre-conditioning. The remarkable advantage of our approach over the existing works in this field is that restoring corrupted images and preserving the shape transitions in the restored results have been orchestrated very well. Experiments illustrate that our method is much more effective and powerful in the noise reduction than the Wiener and median filtering techniques, two typical and widely used techniques

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Sprache(n): eng - English
 Datum: 2001
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 239141
ISI: 000166178500001
Anderer: P6692
DOI: 10.1080/00207160108805025
 Art des Abschluß: -

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Titel: International Journal of Computer Mathematics
  Andere : Int. J. Comput. Math.
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: London : Gordon and Breach Science Publishers
Seiten: - Band / Heft: 76 (3) Artikelnummer: - Start- / Endseite: 279 - 295 Identifikator: ISSN: 0020-7160
CoNE: https://pure.mpg.de/cone/journals/resource/954925407731