{'fraction_non_alphanumeric': 0.0166073546856465, 'fraction_numerical': 0.004744958481613286, 'mean_word_length': 5.0285714285714285, '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 Coarse-to-fine Classification for Motion Blur Kernel Size Estimation with Cascaded Neural Networks

Blur kernel size estimation significantly affects the quality of image deblurring. However, existing methods have a large limitation in estimation accuracy as well as in the adaptability to large kernel sizes. In this paper, we propose a novel blur kernel size estimation method using CNNs. We first convert the 2-D kernel size estimation to a 1-D classification problem. A cascaded network model using homogeneous networks is then designed to cut the entire estimation space into 38 small intervals to have a more accurate kernel size estimation in a coarse-to-fine manner. Experiments show that the proposed method has reached a satisfying performance of kernel size estimation with a largely improved adaptability to various blur kernels.