| Authors |
Pock Thomas, D. Cremers, Bischof Horst, A. Chambolle |
| Appeared in |
SIAM Journal on Imaging Sciences |
| Volume |
3 |
| Number |
4 |
| Pages |
1122–1145 |
| Date |
2010 |
| Abstract |
We propose an algorithmic framework to compute global solutions of variational
models with convex regularity terms that permit quite arbitrary data terms. While the minimiza-
tion of variational problems with convex data and regularity terms is straight forward (using for
example gradient descent), this is no longer trivial for functionals with non-convex data terms. Us-
ing the theoretical framework of calibrations the original variational problem can be written as the
maximum flux of a particular vector field going through the boundary of the subgraph of the un-
known function. Upon relaxation this formulation turns the problem into a convex problem, however,
in higher dimension. In order to solve this problem, we propose a fast primal dual algorithm which
significantly outperforms existing algorithms. In experimental results we show the application of our
method to outlier filtering of range images and disparity estimation in stereo images using a variety
of convex regularity terms.
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| Link |
URL
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