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Efficiency of texture image filtering and its prediction

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Abstract

Textures are typical elements of natural scene images widely used in pattern recognition and image classification. Noise, often being present in acquired images, deteriorates texture features (characteristics), and it is desirable both to suppress it and to preserve a texture. This task is quite difficult even for the most advanced filters, and the resulting denoising efficiency can be quite low. Due to this, it is desirable to predict a denoising efficiency before filtering to decide whether it is worth filtering a given image or not. In this paper, we analyze several quantitative criteria (metrics) that can characterize filtering efficiency. Prediction strategy is described and its accuracy is studied. Several modern filtering techniques are analyzed and compared. Based on this, practical recommendations are given.

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Acknowledgments

This work was partially supported by Instituto Politecnico Nacional as a part of research Project 20161173.

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Authors and Affiliations

  1. Department of Receivers, Transmitters and Signal Processing, National Aerospace University, Kharkov, 61070, Ukraine

    Oleksii Rubel, Vladimir Lukin & Sergey Abramov

  2. IETR UMR CNRS 6164, University of Rennes 1, 22300, Enssat Lannion, France

    Benoit Vozel

  3. Department of Signal Processing, Tampere University of Technology, 33720, Tampere, Finland

    Karen Egiazarian

  4. Instituto Politecnico Nacional, Ave. Juan de Dios Batiz S/N, 07738, Mexico, D.F., Mexico

    Oleksiy Pogrebnyak

Authors
  1. Oleksii Rubel
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  2. Vladimir Lukin
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  3. Sergey Abramov
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  4. Benoit Vozel
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  5. Karen Egiazarian
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  6. Oleksiy Pogrebnyak
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Correspondence to Vladimir Lukin.

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Rubel, O., Lukin, V., Abramov, S. et al. Efficiency of texture image filtering and its prediction. SIViP 10, 1543–1550 (2016). https://doi.org/10.1007/s11760-016-0969-3

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  • DOI: https://doi.org/10.1007/s11760-016-0969-3

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