Bayesian-driven criterion to automatically select the regularization parameter in the l1-Potts model - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

Bayesian-driven criterion to automatically select the regularization parameter in the l1-Potts model

Abstract

This contribution focuses, within the ℓ1-Potts model, on the automated estimation of the regularization parameter balancing the ℓ1 data fidelity term and the TVℓ0 penalization. Variational approaches based on total variation gained considerable interest to solve piecewise constant denoising problems thanks to their deterministic setting and low computational cost. However, the quality of the achieved solution strongly depends on the tuning of the regularization parameter. While recent works have tailored various hierarchical Bayesian procedures to additionally estimate the regularization parameter for Gaussian noise, less attention has been granted to Laplacian noise, of interested in numerous applications. This contribution promotes a fast and parameter-free denoising procedure for piecewise constant signals corrupted by Laplacian noise, that includes automated selection of the regularization parameter. It relies on the minimization of a Bayesian-driven criterion whose similarities with the ℓ1-Potts model permit to derive a computationally efficient algorithm.
Fichier principal
Vignette du fichier
frecon_18927.pdf (444.79 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01757351 , version 1 (03-04-2018)

Identifiers

  • HAL Id : hal-01757351 , version 1
  • OATAO : 18927

Cite

Jordan Frecon, Nelly Pustelnik, Nicolas Dobigeon, Herwig Wendt, Patrice Abry. Bayesian-driven criterion to automatically select the regularization parameter in the l1-Potts model. 42nd IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. pp. 1. ⟨hal-01757351⟩
92 View
127 Download

Share

Gmail Facebook X LinkedIn More