The Shannon Total Variation

Abstract : Discretization schemes commonly used for total variation regularization lead to images that are difficult to interpolate, which is a real issue for applications requiring subpixel accuracy and aliasing control. In the present work, we reconciliate total variation with Shannon interpolation and study a Fourier-based estimate that behaves much better in terms of grid invariance, isotropy, artifact removal, and sub-pixel accuracy. We show that this new variant (called Shannon total variation) can be easily handled with classical primal-dual formulations, and illustrate its efficiency on several image processing tasks, including deblurring, spectrum extrapolation, and a new aliasing reduction algorithm.
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Contributeur : Lionel Moisan <>
Soumis le : mercredi 27 juillet 2016 - 16:59:16
Dernière modification le : mardi 10 octobre 2017 - 11:22:05

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  • HAL Id : hal-01349516, version 1

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Rémy Abergel, Lionel Moisan. The Shannon Total Variation. MAP5 2016-19. 2016. 〈hal-01349516〉

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