{'fraction_non_alphanumeric': 0.01279317697228145, 'fraction_numerical': 0.0, 'mean_word_length': 6.014925373134329, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Bayesian Network-Based Deinterlacing Scheme

This paper proposes a novel motion adaptive interpolation algorithm for video deinterlacing. We employ a Bayesian network, which provides accurate weightings among 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.