{'fraction_non_alphanumeric': 0.026399155227032733, 'fraction_numerical': 0.0, 'mean_word_length': 5.2781456953642385, '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}}
Single Image Super-Resolution Via A Progressive Mixture Model

In this paper, a progressive mixture model (PMM) for single image super-resolution is proposed. Our model consists of an offline training stage and an online reconstruction stage, and both stages are conducted progressively by exploiting a uniform iterative scheme. In the training stage, the training dataset is clustered into finer and finer groups, and a set of mixture models are sequentially learned. In the reconstruction stage, residuals of the low-resolution (LR) input image are estimated by using the trained mixture models at increasing levels, and then they are progressively added to the LR image for producing the high-resolution (HR) image. Extensive experimental simulation results have clearly shown that the proposed model consistently delivers highly accurate and visually pleasant HR images, compared to that of the state-of the-art image super-resolution methods.