Blur identification using the expectation-maximization algorithm

The authors present a maximum-likelihood blur identification method, which estimates the required parameters from the observed noisy blurred image itself, and use the expectation-maximization algorithm to solve the resulting complicated problem of optimizing the likelihood function. A priori information about the unknown parameters in the form of initial conditions and parametric image and blur models are incorporated to make the algorithm applicable to realistic blurs and to improve the identification results. Experimental results are presented on a 256 pixel×256 pixel synthetically blurred by a 2-D Gaussian point spread function with various standard deviations. The approach results in a flexible iterative algorithm that is computationally far more efficient than directly optimizing the likelihood function