Is Texture Denoising Efficiency Predictable?

Abstract : Images of different origin contain textures, and textural features in such regions are frequently employed in pattern recognition, image classification, information extraction, etc. Noise often present in analyzed images might prevent a proper solution of basic tasks in the aforementioned applications and is worth suppressing. This is not an easy task since even the most advanced denoising methods destroy texture in a more or less degree while removing noise. Thus, it is desirable to predict the filtering behavior before any denoising is applied. This paper studies the efficiency of texture image denoising for different noise intensities and several filter types under different visual quality criteria (quality metrics). It is demonstrated that the most efficient existing filters provide very similar results. From the obtained results, it is possible to generalize and employ the prediction strategy earlier proposed for denoising techniques based on the discrete cosine transform. Accuracy of such a prediction is studied and the ways to improve it are considered. Some practical recommendations concerning a decision to undertake whether it is worth applying a filter are given. © 2018 The Author(s).
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International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32 (1), pp.1860005. 〈10.1142/S0218001418600054〉
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01622383
Contributeur : Laurent Jonchère <>
Soumis le : mardi 24 octobre 2017 - 13:26:45
Dernière modification le : mercredi 16 mai 2018 - 11:23:51

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O. Rubel, V. Lukin, S. Abramov, B. Vozel, O. Pogrebnyak, et al.. Is Texture Denoising Efficiency Predictable?. International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32 (1), pp.1860005. 〈10.1142/S0218001418600054〉. 〈hal-01622383〉

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