{'fraction_non_alphanumeric': 0.02245088868101029, 'fraction_numerical': 0.0, 'mean_word_length': 4.944444444444445, '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}}
Novel image fusion methods using intuitionistic index

In this paper, we present new methods for image fusion based on intuitionistic index in spatial domain and contourlet transform domain, furthermore we adopt two ways to fuse images in contourlet domain. When constructing an intuitionistic fuzzy set, we use the Gamma function to get the membership degree, and the Sugeno complementation to get the non-membership degree. Based on the information theory, the larger the hesitancy is, the more information it has. So we set up a fusion rule, by which the larger hesitancy will be chosen, to get a fused image from multi-focus images or remote sensing ones. We compare these new algorithms to some classical image fusion algorithms. The results show, for multi-focus image, these new algorithms are better comparing to other algorithms, and they can get a good fusion result, especially the contourlet transformation algorithm using the intuitionistic index. For remote sensing image, these new algorithms are not the best, but they can also get a well fusion result.