Maximum likelihood image and blur identification: a unifying approach

Abstract

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.