{'fraction_non_alphanumeric': 0.021208907741251327, 'fraction_numerical': 0.0, 'mean_word_length': 4.652694610778443, '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}}
Deconvolution assuming two noise sources

Deconvolution of transient signals is investigated in this paper. Deconvolution is the inverse operation of convolution, i.e. restoration of a distortion caused by a linear system. The aim can be either to reconstruct the signal to be measured or to estimate the impulse response of the system. The problem is ill-posed, i.e. deconvolution amplifies the measurement noise in a great extent. The noise has to be suppressed with the price of a bias in the estimate. A tradeoff has to be found between the noisy and biased estimates. Because of the need of repeatability and to reduce the subjectivity the level of noise reduction has to be set algorithmically. This paper introduces a method which optimizes the parameter(s) of deconvolution algorithms, and controls the level of noise reduction. The novelty of the proposed method is that it assumes two noise sources, rather than only an output noise.