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Total Variation

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Computer Vision
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Definition

The total variation (TV) is a nonnegative, convex, and lower semicontinuous functional on the space of integrable functions. For a function \(u\in{{\mathcal L}^1_{\text{loc}}}(\Omega)\) on a domain \(\Omega\subset{\mathbb R}{n}\), it is defined as

$$\begin{array}{rcl} J(u)&:=& \sup\left\{ -\int_\Omega u\cdot{\rm div}({\boldsymbol{\xi}}) {\,{\rm d}x} : {\boldsymbol{\xi}}\in{\mathcal C}_c^\infty(\Omega, {\mathbb R}{n}),\right.\nonumber\\ &&\left.\|{{\boldsymbol{\xi}}}\|_\infty \leq 1 \vphantom{\sup -\int_\Omega}\right\}. \end{array}$$
(1)

For differentiable functions \(u\in{\mathcal C}^1(\Omega)\), this definition can be reduced to the familiar expression

$$ J(u) = \int_\Omega |{\nabla u}| {\,{\rm d}x}$$
(2)

with the help of Gauss' integral theorem. A function with J(u) < ∞ is called of bounded variation; the space of all functions on Ω with bounded variation is denoted by \({{\mathcal{BV}}(\Omega)}\).

Background

The total variation is a favorite prior term and regularizer in...

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Goldluecke, B. (2014). Total Variation. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_682

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