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Is Texture Denoising Efficiency Predictable?

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Article number1860005
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number1
Publication statusPublished - 2018
Publication typeA1 Journal article-refereed


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.


  • image processing, noise suppression, Texture denoising, visual quality

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