Joint super-resolution and segmentation from a set of low resolution images using a bayesian approach with a Gauss-Markov-Potts prior

M. Mansouri, A. Mohammad-Djafari

Research output: Contribution to journalArticle

2 Citations (Scopus)


This paper addresses the problem of creating a Super-Resolution (SR) image from a set of Low Resolution (LR) images. SR image reconstruction can be viewed as a three-task process: registration or motion estimation, Point Spread Function (PSF) estimation and High Resolution (HR) image reconstruction. In the current work, we propose a new method based on the Bayesian estimation with a Gauss-Markov-Potts Prior Model (GMPPM) where the main objective is to get a new HR image from a set of severely blurred, noisy, rotated and shifted LR images. As a by-product of our prior model, we obtain jointly an SR image and an optimal segmentation of it. The proposed algorithm is unsupervised. A comparison of the performances of the proposed method with some classical and recent SR methods is provided in simulation.

Original languageEnglish
Pages (from-to)211-221
Number of pages11
JournalInternational Journal of Signal and Imaging Systems Engineering
Issue number4
Publication statusPublished - 1 Dec 2010



  • Bayesian estimation
  • Motion estimation
  • PSF estimation
  • Prior modelling
  • Segmentation
  • Super-resolution

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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