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Secrets of image denoising cuisine*

Published online by Cambridge University Press:  19 April 2012

M. Lebrun,
M. Colom,
A. Buades and
J. M. Morel
M. Lebrun
Affiliation:
CMLA, Ecole Normale Supérieure de Cachan, 61 Avenue du Président Wilson, 94235 Cachan CEDEX, France E-mail: marc.lebrun.ik@gmail.com
M. Colom
Affiliation:
Universitat de les Illes Balears, Crta de Valldemossa, km 7.5, 07122 Palma de Mallorca, Spain E-mail: Miguel.Colom@cmla.ens-cachan.fr
A. Buades
Affiliation:
Universitat de les Illes Balears, Crta de Valldemossa, km 7.5, 07122 Palma de Mallorca, Spain and MAP5, Université Paris Descartes, 45 Rue des Saints Pères, 75270 Paris CEDEX 06, France E-mail: toni.buades@uib.es
J. M. Morel
Affiliation:
CMLA, Ecole Normale Supérieure de Cachan, 61 Avenue du Président Wilson, 94235 Cachan CEDEX, France E-mail: morel@cmla.ens-cachan.fr
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Abstract

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Digital images are matrices of equally spaced pixels, each containing a photon count. This photon count is a stochastic process due to the quantum nature of light. It follows that all images are noisy. Ever since digital images have existed, numerical methods have been proposed to improve the signal-to-noise ratio. Such ‘denoising’ methods require a noise model and an image model. It is relatively easy to obtain a noise model. As will be explained in the present paper, it is even possible to estimate it from a single noisy image.

Information

Type
Research Article
Information
Acta Numerica , Volume 21 , May 2012, pp. 475 - 576
Copyright
Copyright © Cambridge University Press 2012

References

* Colour online for monochrome figures available at journals.cambridge.org/anu.