The problem of image denoising is considered from the viewpoint of visual quality of filtered images. Special experiments with a large number of observers have been carried out to determine probability that a denoised image is preferable compared to the corresponding original noisy one. It has been found that there are many practical cases when observers prefer noisy images. This usually happens if an image is highly textural, noise has either quite low or too high intensity, and a used filter performs not efficiently. It has been also shown that modern metrics, even those that take into account peculiarities of human vision system, often perform not adequately. The cases when it is really worth to carry out image denoising are considered.

1. Schowengerdt, R. A. Remote Sensing: Models and Methods for Image Processing, 3rd ed.; Academic Press, 2006.

2. Chatterjee, P.; Milanfar, P. Is Denoising Dead? IEEE Trans. Image Processing. 2010; 19 895-911. https://doi.org/10.1109/TIP.2009.2037087

3. Dabov, K.; Foi, A.; Katkovnik, V.; Egiazarian, K. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Transactions on Image Processing. 2007; 16 2080-2095. https://doi.org/10.1109/TIP.2007.901238

4. Lebrun, M.; Colom, M.; Buades, A.; Morel, J. M. Secrets of image denoising cuisine. Acta Numerica. 2012; 21, 475-576. https://doi.org/10.1017/S0962492912000062

5. Elad, M.; Aharon, M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing. 2006; 15( 12), 3736-3745. https://doi.org/10.1109/TIP.2006.881969

6. Solbo, S.; Eltoft, T. Homomorphic wavelet-based statistical despeckling of SAR images. IEEE Transactions on Geoscience and Remote Sensing. 2004; 42( 4), 711-721. https://doi.org/10.1109/TGRS.2003.821885

7. Pizurica, A. Image Denoising Algorithms: From Wavelet Shrinkage to Nonlocal Collaborative Filtering. Wiley Encyclopedia of Electrical and Electronics Engineering. 2017; p 17. https://doi.org/10.1002/047134608X.W8344

8. Enriquez, A.; Ponomaryov, V. Image denoising using block matching and discrete cosine transform with edge restoring. In Int. Conf. on Electronics, Communications and Computers (CONIELECOMP). - 2016; pp 140-147. https://doi.org/10.1109/CONIELECOMP.2016.7438566

9. Lin, W.; Jay Juo, C.-C. Perceptual visual quality metrics: A survey. J. of Visual Communication and Image Representation. 2011; 22( 4), 297-312. https://doi.org/10.1016/j.jvcir.2011.01.005

10. Rubel, A.; Lukin, V.; Pogrebniak, O. Efficiency of DCT-based denoising techniques applied to texture images. Proceedings of MCPR, Cancun, Mexico. June 2014; p 111-120. https://doi.org/10.1007/978-3-319-07491-7_27

11. Rubel, A.; Lukin, V.; Uss, M.; Vozel, B.; Egiazarian, K.; Pogrebnyak, O. Efficiency of texture image enhancement by DCT-based filtering. Neurocomputing. 2016; 175, 948-965. https://doi.org/10.1016/j.neucom.2015.04.119

12. Lukin, V.; Abramov, S.; Krivenko, S.; Kurekin, A.; Pogrebnyak, O. Analysis of classification accuracy for pre-filtered multichannel remote sensing data. J. of Expert Systems with Applications. 2013; 40( 16), 6400-6411. https://doi.org/10.1016/j.eswa.2013.05.061

13. Rubel, O.; Abramov, S.; Lukin, V.; Egiazarian, K.; Vozel, B.; Pogrebnyak A. Is texture denoising efficiency predictable? Int. J. on Pattern Recognition and Artificial Intelligence. 2018; 32( 1), pp 32. https://doi.org/10.1142/S0218001418600054

14. Buades, A.; Coll, B.; Morel, J.-M. A review of image denoising algorithms, with a new one. J. on Multiscale Modeling and Simulation. 2005; 4( 2), 490-530. https://doi.org/10.1137/040616024

15. Chatterjee, P.; and Milanfar, P. Practical Bounds on Image Denoising: From Estimation to Information. IEEE Transactions on Image Processing. 2011; 20( 5), 1221-1233. https://doi.org/10.1109/TIP.2010.2092440

16. Rubel, A.; Lukin, V. Regression-based Analysis of Visual Quality for Denoised Images. In Fourth Int. Scientific-Practical Conf. Problems of Infocommunications Science and Technology. Kharkiv, Ukraine . 2017; pp 219-222. https://doi.org/10.1109/INFOCOMMST.2017.8246383

17. Lukin, V.; Oktem, R.; Ponomarenko, N.; Egiazarian, K. Image filtering based on discrete cosine transform. Telecommunications and Radio Engineering. 2007; 66(18), 1685-1701. https://doi.org/10.1615/TelecomRadEng.v66.i18.70

18. Ponomarenko, N.; Silvestri, F.; Egiazarian, K.; Carli, M.; Astola, J.; Lukin, V. On between-coefficient contrast masking of DCT basis functions. In Proc. of the Third Int. Workshop on Video Processing and Quality Metrics, USA. Jan 2007. p 4.

19. Abramov, S.; Krivenko, S.; Roenko, A.; Lukin, V.; Djurovic, I.; Chobanu, M. Prediction of filtering efficiency for DCT-based image denoising. In 2nd Mediterranean Conf. on Embedded Computing. MECO, Budva, Montenegro. 2013; 97-100. https://doi.org/10.1109/MECO.2013.6601327

20. Rubel, O.; Lukin, V. An Improved Prediction of DCT-Based Filters Using Regression Analysis. Information and Telecommunications Sciences. 2014; 5(1), 30-41. https://doi.org/10.20535/2411-2976.12014.30-41

21. Uss, M.; Vozel, B.; Lukin, V.; Abramov, S.; Baryshev, I.; Chehdi, K. Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters. EURASIP J. on Advances in Signal Processing. 2011; 2011, p 12. https://doi.org/10.1109/ICASSP.2011.5946565

22. Lukin, V.; Ponomarenko, N.; Egiazarian, K.; Astola, J. Analysis of HVS-Metrics' Properties Using Color Image Database TID2013. In Proceedings of ACIVS, Italy. October 2015; 613-624. https://doi.org/10.1007/978-3-319-25903-1_53