A motion field estimation method for image sequence coding is presented. Motion vector field is estimated to remove the temporal redundancy between two successive images of a sequence. Motion estimation is an ill-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 motion field are taken into account by using a Markov random field (MRF) model. Discontinuities, which unavoidably appear at the edges of a moving object, can be modeled by a continuous line process, as introduced by Geman and Reynolds in 1992, via a regularization function that belongs to the Φ 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 nonconvex functional to get the maximum a posteriori (MAP) optimal solution. We derive a new deterministic relaxation algorithm associated with the Φ 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.