Efficiency of texture image filtering and its prediction
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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.
KeywordsFiltering efficiency Noise suppression Image enhancement Visual quality
This work was partially supported by Instituto Politecnico Nacional as a part of research Project 20161173.
- 2.Schowengerdt, R.: Remote Sensing: Models and Methods for Image Processing. Academic, Cambridge (2006)Google Scholar
- 3.Cheikh, F., Cramariuc, B., Gabbouj, M.: MUVIS: a system for content-based indexing and retrieval in large image databases. In: Proceedings Workshop on Very Low Bit Rate Coding, VLBV, pp. 41–44, Urbana, Oct (1998)Google Scholar
- 9.Buades, A., Coll, A., Morel, J.M.: A non-local algorithm for image denoising. In: Proceeding of Computer Vision and Pattern Recognition (CVPR), pp. 60–65, San Diego, June (2005)Google Scholar
- 11.Pogrebnyak, O., Lukin, V.: Wiener discrete cosine transform based image filtering. SPIE J. Electron. Imaging 21(4), 1–15 (2012)Google Scholar
- 15.Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Jin, L., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., Jay Kuo, C.-C.: Color image database TID2013: peculiarities and preliminary results. In: Proceedings of EUVIP, pp. 106–111, Paris, June (2013)Google Scholar
- 16.Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., Lukin, V.: On between-coefficient contrast masking of DCT basis functions. In: Proceeding of International Workshop on Video Processing and Quality Metrics VPQM-07, Scottsdale, Jan (2007)Google Scholar
- 19.Abramov, S., Krivenko, S., Roenko, A., Lukin, V., Djurovic, I., Chobanu, M.: Prediction of filtering efficiency for DCT-based image denoising. In: Proceeding of 2nd Mediterranean Conference on Embedded Computing (MECO), pp. 97–100, Budva, June (2013)Google Scholar
- 20.Rubel, O., Lukin, V.: An improved prediction of DCT-based filters efficiency using regression analysis. Inf. Telecommun. Sci. 5(1), 30–41 (2014)Google Scholar
- 23.Coifman, R.R., Donoho, D.: Translation-invariant denoising. In: Antoniadis, A., Oppenheim, G. (eds.) Wavelets and Statistics, pp. 125–150. Springer, New York (1995)Google Scholar
- 24.Vijay, M., Subha, S.V.: Spatially adaptive image restoration using LPG-PCA and JBF. In: Proceedings of International Conference on Machine Vision and Image Processing MVIP, pp. 53–56, Tamil Nadu, Dec (2012)Google Scholar