Inhomogeneous Gaussian image models for estimation and restoration
Two inhomogeneous Gaussian-image models are presented for estimation and restoration. By incorporating the local statistics of an image, a homogeneous autoregressive (AR) random field can be extended to an inhomogeneous AR field. This inhomogeneous random field can provide a better description of the image than the homogeneous one. As a consequence of this improved modeling, a minimum-mean-square-error estimator (MMSE), based on the inhomogeneous Gaussian model, can produce good results in both subjective and objective criteria. Two image models are proposed for use in image estimation and restoration: a residual image model (original image minus the space-variant mean) and a normalized image model (residual image divided by space-variant standard variation). The novel aspect of these models is the use of an autoregressive dynamical model for residual and normalized images. Some aspects of parameter estimation are discussed and simulation results are presented. >