It is known that the distributions of wavelet coefficients of natural images at different scales and orientations can
be approximated by generalized Gaussian probability density functions. We exploit this prior knowledge within
a novel statistical framework for multi-frame image restoration based on the maximum a-posteriori (MAP) algorithm.
We describe an iterative algorithm for obtaining a high-fidelity object estimate from multiple warped,
blurred, and noisy low-resolution images. We compare our new method with several other techniques including
linear restoration, and restoration using Markov Random Field (MRF) object priors. We will discuss the
performances of the algorithms.