A motion field estimation method for image sequence coding is presente
d. Motion vector field is estimated to remove the temporal redundancy
between two successive images of a sequence. Motion estimation is an i
ll-posed inverse problem. Usually, the solution has been stabilized by
regularization, as proposed by Tikhonov in 1963, i.e., by assuming a
priori the smoothness of the solution. Here, discontinuities of the mo
tion field are taken into account by using a Markov random field (MRF)
model. Discontinuities, which unavoidably appear at the edges of a mo
ving object, can be modeled by a continuous line process, as introduce
d by Geman and Reynolds in 1992, via a regularization function that be
longs to the PHI function family. This line process leads to solutions
less sensitive to noise than an all-or-nothing Boolean line process.
Taking discontinuities into account leads to the minimization of a non
convex functional to get the maximum a posteriori (MAP) optimal soluti
on. We derive a new deterministic relaxation algorithm associated with
the PHI function, to minimize the nonconvex criterion. We apply this
algorithm in a coarse-to-fine multiresolution scheme, leading to more
accurate results. We show results on synthetic and real-life sequences
.