{'fraction_non_alphanumeric': 0.01997780244173141, 'fraction_numerical': 0.0, 'mean_word_length': 6.333333333333333, '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}}
Image Super-Resolution Reconstruction using Multigrid and Krylov Subspace Accelerative Algorithm

Two fast image super-resolution reconstruction algorithms are proposed based on multigrid (MG) and Krylov subspace accelerative algorithms. After briefly introduction of image super-resolution reconstruction model, MG and Krylov subspace accelerative algorithms, two accelerative MG algorithm named MG-CG and MG-GMRES are proposed to solve the sparse symmetric positive definite and non-symmetric linear equation, which are often occurred in image super-resolution reconstruction. The restriction, prolongation and smoothing operators of each algorithm are thoroughly studied and the convergence is analyzed respectively. Experimental results demonstrate that the proposed algorithms can greatly improve the convergence rate compared with MG, two Krylov accelerative algorithms and Richardson iteration.