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Application of Neural Networks to Diagnose the Type and Parameters of Image Distortions

  • THEORY AND METHODS OF INFORMATION PROCESSING
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Abstract—Image capturing is often performed with distortions caused by inaccurate focusing, displacement of the scene or recorder, radiation dispersion in the signal transmission medium, and similar aberrations. The distortion operator, which is commonly unknown, is needed for image restoration. Therefore, the determination of type and parameters of distortions using the observed signal is actual. In the paper a new approach to distortions diagnostics of video information by means of deep neural networks is proposed. The task of determination of the type and parameters of the main linear homogeneous distortion operators (circular with rectangular profile, circular with Gaussian profile, and linear with rectangular profile) is considered. An application of neural networks with the ResNet50, ResNet29, and ResNet18 architectures to identify the type and to determine the distortions parameters is studied. The research shows that the efficiency of the neural network is no less than that of methods based on direct spectral analysis.

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology, 141700, Dolgoprudnyi, Moscow oblast, Russia

    Ia. K. Solomentsev

  2. Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, 127051, Moscow, Russia

    P. A. Chochia

Authors
  1. Ia. K. Solomentsev
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  2. P. A. Chochia
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Correspondence to Ia. K. Solomentsev or P. A. Chochia.

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Translated by A. Chikishev

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Solomentsev, I.K., Chochia, P.A. Application of Neural Networks to Diagnose the Type and Parameters of Image Distortions. J. Commun. Technol. Electron. 65, 1499–1504 (2020). https://doi.org/10.1134/S1064226920120165

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  • DOI: https://doi.org/10.1134/S1064226920120165

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