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This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from observed multiple low-resolution images. A compound Gaussian MRF model provides a preferable prior for natural images that preserves edges. PM is the optimal estimator for the objective function of...
Super-resolution (SR) is a technique to enhance the resolution of an image without changing the camera resolution, through using software algorithms. In this context, this paper proposes a fully automatic SR algorithm, using a recent nonparametric Bayesian inference method based on numerical integration, known in the statistical literature as integrated nested Laplace approximation (INLA). By applying...
In this paper, we consider the problem of parameter estimation on the super resolution and Bayesian methodology for pansharpening using contourlet transform. The used methodology is able to incorporate prior knowledge on the expected characteristics of the multispec-tral images, include information on the unknown parameters in the form of hyperprior distributions and estimate the unknown parameters...
Influence diagrams (IDs) are powerful tools for representing and solving complex decision making problems. This paper presents a simulation-based approach for solving decision making problems formulated by hybrid IDs, which involve both discrete and continuous decision and chance variables. In the proposed method, Monte-Carlo simulation is applied in both approximating the expected conditional utility...
This paper is devoted to the combination of image priors in Super Resolution (SR) image reconstruction. Taking into account that each combination of a given observation model and a prior model produces a different posterior distribution of the underlying High Resolution (HR) image, the use of variational posterior distribution approximation on each posterior will produce as many posterior approximations...
In this paper the application of image prior combinations to the Bayesian Super Resolution (SR) image registration and reconstruction problem is studied. A sparse image prior based on the horizontal and vertical first order differences is combined with a non-sparse SAR prior. Since, for a given observation model, each prior produces a different posterior distribution of the underlying High Resolution...
In this paper we address the problem of sparse representation (SR) within a Bayesian framework. We assume that the observations are generated from a Bernoulli-Gaussian process and consider the corresponding Bayesian inference problem. Tractable solutions are then proposed based on the “mean-field” approximation and the variational Bayes EM algorithm. The resulting SR algorithms are shown to have a...
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