In this paper, we model the components of the compressive sensing (CS) problem, ie, the
signal acquisition process, the unknown signal coefficients and the model parameters for the …

In this paper we provide a review of the recent literature on Bayesian Blind Image
Deconvolution (BID) methods. We believe that two events have marked the recent history of …

In this paper, we address the super resolution (SR) problem from a set of degraded low
resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the …

A method for blind image deblurring is presented. The method only makes weak
assumptions about the blurring filter and is able to undo a wide variety of blurring …

Designing effective image priors is of great interest to image super-resolution (SR), which is
a severely under-determined problem. An edge smoothness prior is favored since it is able …

Regularized iterative reconstruction algorithms for imaging inverse problems require
selection of appropriate regularization parameter values. We focus on the challenging …

In this paper, we present novel algorithms for total variation (TV) based blind deconvolution
and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian …

There are two key issues in successfully solving the image restoration problem: 1)
estimation of the regularization parameter that balances data fidelity with the regularity of the …

Super-resolution (SR) is the term used to define the process of estimating a high-resolution
(HR) image or a set of HR images from a set of low-resolution (LR) observations. In this …

In this paper, we propose a variational Bayesian method for Retinex to simulate and
interpret how the human visual system perceives color. To construct a hierarchical Bayesian …