Spatially adaptive regularized pel-recursive motion estimation based on the EM algorithm

Pel-recursive motion estimation is a well-established approach for motion estimation. However, in the presence of noise, it becomes an il-posed problem that requires regulation. In the past, regularization for pel-recursive estimations was addressed in an ad-hoc manner. In this paper, a Bayesian estimation framework is used to deal with this issue. More specifically, motion vectors and regularization parameters are estimated in an iterative fashion by means of the Expectation- Maximization (EM) algorithm and a Gaussian data model. The proposed algorithm utilizes the local image properties to regularize the motion vector estimates following a spatially adaptive approach. Numerical experiments are presented that demonstrate the merits of the proposed algorithm.