[IPOL announce] new article: Constrained and Unconstrained Inverse Potts Modelling for Joint Image Super-Resolution and Segmentation
announcements about the IPOL journal
announce at list.ipol.im
Sat Apr 23 13:51:08 CEST 2022
A new article is available in IPOL: https://www.ipol.im/pub/art/2022/393/
Dario Mylonopoulos, Pasquale Cascarano, Luca Calatroni, and Elena Loli
Piccolomini,
Constrained and Unconstrained Inverse Potts Modelling for Joint Image
Super-Resolution and Segmentation,
Image Processing On Line, 12 (2022), pp. 92–110.
https://doi.org/10.5201/ipol.2022.393
Abstract
In this work we consider two methods for joint single-image
super-resolution and image partitioning. The proposed approaches rely on
a constrained and on an unconstrained version of the inverse Potts model
where an L0 regularization prior on the image gradient is used for
promoting piecewise constant solutions. For the numerical solution of
both models, we provide a unified implementation based on the
Alternating Direction Method of Multipliers (ADMM). Upon suitable
assumptions on both model operators and on the algorithmic parameters
involved, we show that all the ADMM subproblems admit closed-form
solutions, thus making the resulting algorithms computationally very
cheap even when high-dimensional data are considered. Numerical details
of the implementation of both models are given and several experiments
are carried out on both synthetic and natural images to underline the
accuracy and the computational efficiency of the models.
More information about the announce
mailing list