TY - GEN
T1 - Total variation for cyclic structures
T2 - Convex relaxation and efficient minimization
AU - Strekalovskiy, Evgeny
AU - Cremers, Daniel
PY - 2011
Y1 - 2011
N2 - We introduce a novel type of total variation regularizer, TV S1 , for cyclic structures such as angles or hue values. The method handles the periodicity of values in a simple and consistent way and is invariant to value shifts. The regularizer is integrated in a recent functional lifting framework which allows for arbitrary nonconvex data terms. Results are superior and more natural than with the simple total variation without special care about wrapping interval end points. In addition we propose an equivalent formulation which can be minimized with the same time and memory efficiency as the standard total variation.
AB - We introduce a novel type of total variation regularizer, TV S1 , for cyclic structures such as angles or hue values. The method handles the periodicity of values in a simple and consistent way and is invariant to value shifts. The regularizer is integrated in a recent functional lifting framework which allows for arbitrary nonconvex data terms. Results are superior and more natural than with the simple total variation without special care about wrapping interval end points. In addition we propose an equivalent formulation which can be minimized with the same time and memory efficiency as the standard total variation.
UR - http://www.scopus.com/inward/record.url?scp=80052875236&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2011.5995573
DO - 10.1109/CVPR.2011.5995573
M3 - Conference contribution
AN - SCOPUS:80052875236
SN - 9781457703942
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 1905
EP - 1911
BT - 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PB - IEEE Computer Society
ER -