A number of different algorithms have recently been proposed
to identify the image and blur model parameters from an image that is
degraded by blur and noise. This paper gives an overview of the developments
in image and blur identification under a unifying maximum likelihood
framework. In fact, we show that various recently published image
and blur identification algorithms are different implementations of the
same maximum likelihood estimator resulting from different modeling
assumptions and/or considerations about the computational complexity.
The use of the maximum likelihood estimation in image and blur identification
is illustrated by numerical examples.