A method is investigated for training neural networks for image enhancement, based on using information from the features of neural networks trained in image classification. Experiments are performed to identify the optimal loss function that achieves maximum precision in the classification of images superposed with noise or blurring. The dependence of the best configuration of training parameters on the type of detrimental influence and target is demonstrated. This is the first study, to our knowledge, to compare the influence of such a loss function on the precision of the restoration and recognition with the utilization of a single classifier trained under the influence of distorting factors. We show that it is reasonable to correct some simple distortions "outside" the classifier, while others are better corrected "inside." (C) 2021 Optical Society of America
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