PALMS Image Partitioning - A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model
Lukas Kiefer, Martin Storath, Andreas Weinmann
→ BibTeX
@article{ipol.2020.295,
    title   = {{PALMS Image Partitioning - A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model}},
    author  = {Kiefer, Lukas and Storath, Martin and Weinmann, Andreas},
    journal = {{Image Processing On Line}},
    volume  = {10},
    pages   = {124--149},
    year    = {2020},
    doi     = {10.5201/ipol.2020.295},
}
% if your bibliography style doesn't support doi fields:
    note    = {\url{https://doi.org/10.5201/ipol.2020.295}}
published
2020-09-29
reference
Lukas Kiefer, Martin Storath, and Andreas Weinmann, PALMS Image Partitioning - A New Parallel Algorithm for the Piecewise Affine-Linear Mumford-Shah Model, Image Processing On Line, 10 (2020), pp. 124–149. https://doi.org/10.5201/ipol.2020.295

Communicated by Pascal Monasse
Demo edited by Pascal Monasse

Abstract

We present a method for computing approximate solutions of the piecewise affine-linear Mumford-Shah model - PALMS Image Partitioning. The piecewise affine-linear Mumford-Shah model is a variational approach to image partitioning. The underlying algorithm is based on a splitting approach using ADMM. The emerging subproblems are solved exactly and efficiently. We detail the solver for these subproblems which is based on dynamic programming and incorporates an acceleration strategy. The subproblems are solved in parallel in our implementation to provide an efficient overall algorithm. We conduct extended studies on the effects of the algorithmic parameters. Thereby, the implemented algorithm is further optimized w.r.t. runtime and efficiency. Finally, we underpin the efficiency of the algorithm by a comparison with the state-of-the-art which shows that the presented algorithm has lower computation times and yields lower mean functional values.

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