Title: Joint Super-Resolution and segmentation from a set of Low Resolution images using a Bayesian approach with a Gauss-Markov-Potts Prior
Authors: M. Mansouri, A. Mohammad-Djafari
Addresses: ICD/LM2S, University of Technology of Troyes, France. ' LSS, CNRS-Supelec-UNIV PARIS SUD 11, Gif-sur-Yvette, France
Abstract: 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.
Keywords: Bayesian estimation; motion estimation; PSF estimation; prior modelling; segmentation; super-resolution images; low resolution images; image reconstruction; point spread function.
International Journal of Signal and Imaging Systems Engineering, 2010 Vol.3 No.4, pp.211 - 221
Received: 13 May 2010
Accepted: 22 Oct 2010
Published online: 10 Jan 2011 *