{'fraction_non_alphanumeric': 0.015492957746478873, 'fraction_numerical': 0.0, 'mean_word_length': 5.7075471698113205, '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 network of networks processing model for image regularization

We introduce a network of networks (NoN) model to solve image regularization problems. The method is motivated by the fact that natural image formation involves both local processing and globally coordinated parallel processing. Both forms are readily implemented using an NoN architecture. The modeling is very powerful in that it achieves high-quality adaptive processing, and it reduces the computational difference between inhomogeneous and homogeneous conditions. This method is able to provide fast, quality imaging in early vision, and its replicating structure and sparse connectivity readily lend themselves to hardware implementations.