Application of Bayesian Network for Fuzzy Rule-Based Video Deinterlacing

This paper proposes a fuzzy reasoning interpolation method for video deinterlacing. We propose edge detection parameters to measure the amount of entropy in the spatial and temporal domains. The shape of the membership functions is designed adaptively, according to those parameters and can be utilized to determine edge direction. Our proposed fuzzy edge direction detector operates by identifying small pixel variations in nine orientations in each domain and uses rules to infer the edge direction. We employ a Bayesian network, which provides accurate weightings between the proposed deinterlacing method and common existing deinterlacing methods. It successively builds approximations of the deinterlaced sequence by weighting interpolation methods. The results of computer simulations show that the proposed method outperforms a number of methods in the literature.