An automated approach to quasi-periodic noise removal in natural images
Identifieur interne : 000672 ( Main/Exploration ); précédent : 000671; suivant : 000673An automated approach to quasi-periodic noise removal in natural images
Auteurs : Frédéric Sur [France] ; Michel Grediac [France]Source :
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Abstract
Quasi-periodic noise may affect digital images. This phenomenon is reflected by spurious repetitive patterns covering the whole image. Quasi-periodic noise is by nature well localized in the Fourier spectrum. A possibility is thus to attenuate it through a well-designed notch filter. In contrast to existing algorithms which require hand-tuned filter design, this research report presents an automated approach based on the expected power spectrum of a natural image. The resulting algorithm enables to eliminate a large range of repetitive structures, from simple periodic noises, whose influence on the image spectrum is limited to a few Fourier coefficients, to quasi-periodic noises with much more complex spectrum structures. The proposed algorithm is assessed on the basis of various experiments. A comparison with morphological component analysis (MCA), a blind source separation algorithm, is also provided. A Matlab implementation is available. This report is anextension of a published article.
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This phenomenon is reflected by spurious repetitive patterns covering the whole image. Quasi-periodic noise is by nature well localized in the Fourier spectrum. A possibility is thus to attenuate it through a well-designed notch filter. In contrast to existing algorithms which require hand-tuned filter design, this research report presents an automated approach based on the expected power spectrum of a natural image. The resulting algorithm enables to eliminate a large range of repetitive structures, from simple periodic noises, whose influence on the image spectrum is limited to a few Fourier coefficients, to quasi-periodic noises with much more complex spectrum structures. The proposed algorithm is assessed on the basis of various experiments. A comparison with morphological component analysis (MCA), a blind source separation algorithm, is also provided. A Matlab implementation is available. This report is anextension of a published article.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Sur, Frederic" sort="Sur, Frederic" uniqKey="Sur F" first="Frédéric" last="Sur">Frédéric Sur</name></region><name sortKey="Grediac, Michel" sort="Grediac, Michel" uniqKey="Grediac M" first="Michel" last="Grediac">Michel Grediac</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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