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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Contributor : Lionel Moisan <>
Submitted on : Wednesday, July 27, 2016 - 4:59:16 PM
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Rémy Abergel, Lionel Moisan. The Shannon Total Variation. 2016. ⟨hal-01349516⟩

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