<title>Maximum-likelihood blur identification</title>

Abstract

In this paper we discuss the use of maximum likelihood estimation procedures for the identification of unknowns blur from a blurred image. The main focus will be on the problem of estimating the coefficients of relatively large point-spread functions, and the estimation of the support size of point-spread functions in general. Two improved blur identification techniques are proposed which are both based on the expectation- maximization algorithm. In the first method we describe the point-spread function by a parametric model, while in the second method resolution pyramids are employed to identify the point-spread function in a hierarchical manner.