[IPOL announce] new article: Constrained and Unconstrained Inverse Potts Modelling for Joint Image Super-Resolution and Segmentation

[announcements about the IPOL journal]

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.