Discontinuity-Adaptive De-Interlacing Scheme Using Markov Random Field Model

In this paper, a de-interlacing algorithm to find the optimal deinterlaced results given accuracy-limited motion information is proposed. The de-interlacing process is formulated as a maximum a posteriori (MAP)-Markov random field (MRF) problem. The MAP solution is the one that minimizes an energy function. The energy function imposes discontinuity adaptive smoothness constraint upon the deinterlaced frame. Simulation results show that the MAP-MRF formulation is efficient and the high frequency noise is removed in a few iterations.