{'fraction_non_alphanumeric': 0.01257396449704142, 'fraction_numerical': 0.0, 'mean_word_length': 5.903061224489796, '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}}
Multichannel regularized iterative restoration of image sequences

The recent advances in visual communications make restoration of image sequences an increasingly important problem. In addition, this problem finds applications in other fields such as robot guidance and target tracking. Restoring the individual frames of an image sequence independently is a suboptimal approach because the between frame relations of the image sequence are not explicitly incorporated into the restoration algorithm. In this paper we address this problem by proposing a family of multichannel algorithms that restore the multiple time frames (channels) simultaneously. This is accomplished by using a multichannel regularized formulation in which the regularization operator captures both within and between- frame (channel) properties of the image sequence. More specifically, this operator captures both the spatial within-frame smoothness and the temporal along the direction of the motion between-frame smoothness. We propose a number of different methods to define multichannel regularization operators and a family of algorithms to iteratively obtain the restored images. We also present experiments that demonstrate beyond any doubt that the proposed approach produces significant improvements over traditional independent frame restoration of image sequences.