{'fraction_non_alphanumeric': 0.014629948364888123, 'fraction_numerical': 0.0, 'mean_word_length': 5.533707865168539, '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}}
An alternating extragradient method for total variation-based image restoration from Poisson data

Variational models are a valid tool for edge-preserving image restoration from data affected by Poisson noise. This paper deals with total variation and hypersurface regularization in combination with the Kullbach Leibler divergence as a data fidelity function. We propose an iterative method, based on an alternating extragradient scheme, which is able to solve, in a numerically stable way, the primal–dual formulation of both total variation and hypersurface regularization problems. In this method, tailored for general smooth saddle-point problems, the stepsize parameter can be adaptively computed so that the convergence of the scheme is proved under mild assumptions. In the numerical experience, we focus the attention on the artificial smoothing parameter that makes different the total variation and hypersurface regularization. A set of experiments on image denoising and deblurring problems is performed in order to evaluate the influence of this smoothing parameter on the stability of the proposed method and on the features of the restored images.