The distribution of edge values for an image of a general scene often
has a sharp peak with a long tail. This property, which can be well de
scribed by a Lorentzian probability function, has been used to develop
an efficient nonlinear image restoration algorithm for reducing the v
arious artifacts that often arise in the restored images. The algorith
m starts with a Wiener filter solution which is used to model the edge
image by the Lorentzian function so that the likelihood of the image
can be estimated. A nonlinear correction term is then introduced which
increases this image likelihood under the mean square error criterion
. This process ensures that the resulting image retains its sharpness
while reducing the noise and ringing artifacts. An iterative procedure
has been developed to implement this method. Computer simulated resul
ts show that the algorithm is robust in reducing artifacts and easily
implemented. The algorithm also possesses a superresolution capability
due to the highly nonlinear property of the correction term.