TY - CHAP
T1 - Asymptotic Behaviour of Total Generalised Variation
AU - Papafitsoros, Konstantinos
AU - Valkonen, Tuomo
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-I1-007-43
Acknowledgements: This work is supported by the King Abdullah University for Science and Technology (KAUST) Award No. KUK-I1-007-43. The first author acknowledges further support by the Cambridge Centre for Analysis (CCA) and the Engineering and Physical Sciences Research Council (EPSRC). The second author acknowledges further support from EPSRC grant EP/M00483X/1 “Efficient computational tools for inverse imaging problems”.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2015/4/28
Y1 - 2015/4/28
N2 - © Springer International Publishing Switzerland 2015. The recently introduced second order total generalised variation functional TGV2 β,α has been a successful regulariser for image processing purposes. Its definition involves two positive parameters α and β whose values determine the amount and the quality of the regularisation. In this paper we report on the behaviour of TGV2 β,α in the cases where the parameters α, β as well as their ratio β/α becomes very large or very small. Among others, we prove that for sufficiently symmetric two dimensional data and large ratio β/α, TGV2 β,α regularisation coincides with total variation (TV) regularization
AB - © Springer International Publishing Switzerland 2015. The recently introduced second order total generalised variation functional TGV2 β,α has been a successful regulariser for image processing purposes. Its definition involves two positive parameters α and β whose values determine the amount and the quality of the regularisation. In this paper we report on the behaviour of TGV2 β,α in the cases where the parameters α, β as well as their ratio β/α becomes very large or very small. Among others, we prove that for sufficiently symmetric two dimensional data and large ratio β/α, TGV2 β,α regularisation coincides with total variation (TV) regularization
UR - http://hdl.handle.net/10754/597621
UR - http://link.springer.com/10.1007/978-3-319-18461-6_56
UR - http://www.scopus.com/inward/record.url?scp=84931098237&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-18461-6_56
DO - 10.1007/978-3-319-18461-6_56
M3 - Chapter
SN - 9783319184609
SP - 702
EP - 714
BT - Scale Space and Variational Methods in Computer Vision
PB - Springer Nature
ER -