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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Signal and Imaging Systems Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
