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Sparse approach to image ringing detection and suppression

  • Representation, Processing, Analysis, and Understanding of Images
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Abstract

In this work we discuss methods for image ringing detection and suppression that are based on the sparse representations approach and suggest a new ringing suppression method. The ringing detection algorithm is based on construction of the synthetic dictionary that is used to represent ringing effect as a sum of blurred edge and pure ringing component. This decomposition enables us to estimate image ringing level. We analyze two ringing suppression methods. First method is based on learning joint dictionaries and shows good performance for the whole image on average. However for high ringing levels the performance of this method decreases due to the influence of the ringing artefact on the sparse representation parameters. The second method is based on separate learning of natural images dictionary and pure ringing dictionary and it does not suffer from this problem. In this article we present a new ringing suppression method that is based on the method using separate dictionaries. The method works best in the areas of edges and for higher levels of ringing effect.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, 101000, Russia

    A. V. Umnov

  2. Lomonosov Moscow State University Faculty of Computer Science, Moscow, 119991, Russia

    A. S. Krylov

Authors
  1. A. V. Umnov
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  2. A. S. Krylov
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Correspondence to A. V. Umnov.

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Alexey Vitalievich Umnov, born 1990, graduated from Faculty of Mechanics and Mathematics, Lomonosov Moscow State University (MSU) in 2012. He is currently a junior researcher at the Faculty of Computer Science, National Research University Higher School of Economics (HSE). He has 9 publications in total, and his main interests lie in area of image processing using machine learning.

Andrey Serdzhevich Krylov, born 1956, graduated from the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University (MSU) in 1978. Received the PhD degree in 1983, the Dr.Sc. degree in 2009. He is a professor and the head of the Laboratory of Mathematical Methods of Image Processing at the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University (MSU). He has 137 publications in total, and his main research interests lie in mathematical methods of multimedia data processing and analysis.

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Umnov, A.V., Krylov, A.S. Sparse approach to image ringing detection and suppression. Pattern Recognit. Image Anal. 27, 754–762 (2017). https://doi.org/10.1134/S1054661817040186

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  • DOI: https://doi.org/10.1134/S1054661817040186

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