Restoration of randomly blurred images via the maximum a posteriori criterion

The maximum a posteriori (MAP) estimation technique is applied to the problem of restoring images distorted by noisy point spread functions and additive noise. The resulting MAP estimator is nonlinear and is obtained by numerically maximizing a conditional probability density function. The energy nonnegativity constraint is incorporated in the optimization process. Although the deblurring results are slightly inferior to those obtained by applying the Wiener criterion, the advantage of the MAP estimator lies in its significant suppression of noise