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PRODID:-//Département de mathématiques et applications - ECPv6.2.2//NONSGML v1.0//EN
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X-WR-CALNAME:Département de mathématiques et applications
X-ORIGINAL-URL:https://www.math.ens.psl.eu
X-WR-CALDESC:évènements pour Département de mathématiques et applications
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TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20170326T010000
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TZOFFSETFROM:+0200
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TZNAME:CET
DTSTART:20171029T010000
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20170330T140000
DTEND;TZID=Europe/Paris:20170330T150000
DTSTAMP:20260517T082047
CREATED:20170330T120000Z
LAST-MODIFIED:20211104T104203Z
UID:8373-1490882400-1490886000@www.math.ens.psl.eu
SUMMARY:Adapting to unknown noise level in super-resolution
DESCRIPTION:We study sparse spikes deconvolution over the space of complex-valued measures when the input measure is a finite sum of Dirac masses. We introduce a new procedure to handle the spike deconvolution when the noise level is unknown. Prediction and localization results will be presented for this approach. An insight on the probabilistic tools used in the proofs could be briefly given as well.
URL:https://www.math.ens.psl.eu/evenement/adapting-to-unknown-noise-level-in-super-resolution/
LOCATION:ENS Salle W
CATEGORIES:Séminaire Parisien des Mathématiques Appliquées à l’Imagerie
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20170330T150000
DTEND;TZID=Europe/Paris:20170330T150000
DTSTAMP:20260517T082047
CREATED:20170330T130000Z
LAST-MODIFIED:20211104T103401Z
UID:8355-1490886000-1490886000@www.math.ens.psl.eu
SUMMARY:Covariant LEAst-Square Re-fitting for image restoration
DESCRIPTION:We propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms\, with a special focus for image processing tasks. Generalizing ideas that emerged for l1 regularization\, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method\, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then\, we provide an approach that has a « twicing » flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks. Joint work with C.-A. Deledalle (IMBordeaux)\, J. Salmon (TELECOM ParisTech) and S. Vaiter (IMBourgogne).
URL:https://www.math.ens.psl.eu/evenement/covariant-least-square-re-fitting-for-image-restoration-2/
LOCATION:Salle W (ENS)
CATEGORIES:Séminaire Parisien des Mathématiques Appliquées à l’Imagerie
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