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arxiv: 1502.06933 · v1 · pith:VRQO6FWUnew · submitted 2015-02-24 · 🧮 math.OC

Asymptotic behaviour of total generalised variation

classification 🧮 math.OC
keywords alphabetamathrmregularisationtotalvariationbehaviourgeneralised
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The recently introduced second order total generalised variation functional $\mathrm{TGV}_{\beta,\alpha}^{2}$ has been a successful regulariser for image processing purposes. Its definition involves two positive parameters $\alpha$ and $\beta$ whose values determine the amount and the quality of the regularisation. In this paper we report on the behaviour of $\mathrm{TGV}_{\beta,\alpha}^{2}$ in the cases where the parameters $\alpha, \beta$ as well as their ratio $\beta/\alpha$ becomes very large or very small. Among others, we prove that for sufficiently symmetric two dimensional data and large ratio $\beta/\alpha$, $\mathrm{TGV}_{\beta,\alpha}^{2}$ regularisation coincides with total variation ($\mathrm{TV}$) regularisation.

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