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Article Dans Une Revue Journal of Mathematical Imaging and Vision Année : 2017

The Shannon Total Variation

Rémy Abergel
Lionel Moisan

Résumé

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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Dates et versions

hal-01349516 , version 1 (27-07-2016)

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Rémy Abergel, Lionel Moisan. The Shannon Total Variation. Journal of Mathematical Imaging and Vision, 2017, 59 (2), pp.341--370. ⟨10.1007/s10851-017-0733-5⟩. ⟨hal-01349516⟩
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