Joint Super-Resolution and segmentation from a set of Low Resolution images using a Bayesian approach with a Gauss-Markov-Potts Prior
نویسندگان
چکیده
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.
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