Variational Bayesian blind image deconvolution based on a sparse kernel model for the point spread function

In this paper we propose a variational Bayesian algorithm for the blind image deconvolution problem. The unknown point spread function (PSF) is modeled as a sparse linear combination of kernel basis functions. This model offers an effective mechanism to estimate for the first time both the support and the shape of the PSF. Numerical experiments demonstrate the effectiveness of the proposed methodology.