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Journal Articles Journal of Mathematical Imaging and Vision Year : 2013

Fully smoothed ℓ1-TV models: Bounds for the minimizers and parameter choice

Abstract

We consider a class of convex functionals that can be seen as C1 smooth approximations of the ℓ1-TV model. The minimizers of such functionals were shown to exhibit a qualitatively different behavior compared to the nonsmooth ℓ1-TV model [12]. Here we focus on the way the parameters involved in these functionals determine the features of the minimizers u*. We give explicit relationships between the minimizers and these parameters. Given an input digital image f, we prove that the error ∥u*−f∥_infty obeys b−ε ≤ ∥u*−f∥_infty ≤b where b is a constant independent of the input image. Further we can set the parameters so that ε > 0 is arbitrarily close to zero. More precisely, we exhibit explicit formulae relating the model parameters, the input image f and the values b and ε. Conversely, we can fix the parameter values so that the error ∥u*−f∥_infty satisfy some prescribed b, ε. All theoretical results are confirmed using numerical tests on natural digital images of different sizes with disparate content and quality.
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Dates and versions

hal-00722743 , version 1 (05-08-2012)
hal-00722743 , version 2 (16-10-2012)
hal-00722743 , version 3 (04-02-2013)

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Cite

F. Baus, Mila Nikolova, Gabriele Steidl. Fully smoothed ℓ1-TV models: Bounds for the minimizers and parameter choice. Journal of Mathematical Imaging and Vision, 2013, ⟨10.1007/s10851-013-0420-0⟩. ⟨hal-00722743v3⟩
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