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On prediction of DCT-based denoising efficiency under spatially correlated noise conditions

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On prediction of DCT-based denoising efficiency under spatially correlated noise conditions. / Rubel, Oleksii; Lukin, Vladimir; Egiazarian, Karen.

2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET) . IEEE, 2016. p. 750-754.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Rubel, O, Lukin, V & Egiazarian, K 2016, On prediction of DCT-based denoising efficiency under spatially correlated noise conditions. in 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET) . IEEE, pp. 750-754, International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science, 1/01/00. https://doi.org/10.1109/TCSET.2016.7452171

APA

Rubel, O., Lukin, V., & Egiazarian, K. (2016). On prediction of DCT-based denoising efficiency under spatially correlated noise conditions. In 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET) (pp. 750-754). IEEE. https://doi.org/10.1109/TCSET.2016.7452171

Vancouver

Rubel O, Lukin V, Egiazarian K. On prediction of DCT-based denoising efficiency under spatially correlated noise conditions. In 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET) . IEEE. 2016. p. 750-754 https://doi.org/10.1109/TCSET.2016.7452171

Author

Rubel, Oleksii ; Lukin, Vladimir ; Egiazarian, Karen. / On prediction of DCT-based denoising efficiency under spatially correlated noise conditions. 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET) . IEEE, 2016. pp. 750-754

Bibtex - Download

@inproceedings{dd2a6ad17725409cb41650cd542b2e01,
title = "On prediction of DCT-based denoising efficiency under spatially correlated noise conditions",
abstract = "In this paper, results of image denoising efficiency prediction for filter based on discrete cosine transform (DCT) for the case of spatially correlated additive Gaussian Noise (SCGN) are given. The considered noise model is analyzed for different degrees of spatial correlation that produce varying non-homogeneous spectrum of the noise. PSNR metric is exploited to assess denoising efficiency. It is shown in this paper, that a prediction of denoising efficiency has high accuracy for data distorted by noise with different degrees of spatial correlation, and require low computational resources.",
keywords = "Denoising, Efficiency Prediction, Fitting, Spatially Correlated Noise",
author = "Oleksii Rubel and Vladimir Lukin and Karen Egiazarian",
year = "2016",
month = "4",
day = "12",
doi = "10.1109/TCSET.2016.7452171",
language = "English",
isbn = "9786176078067",
pages = "750--754",
booktitle = "2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET)",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - On prediction of DCT-based denoising efficiency under spatially correlated noise conditions

AU - Rubel, Oleksii

AU - Lukin, Vladimir

AU - Egiazarian, Karen

PY - 2016/4/12

Y1 - 2016/4/12

N2 - In this paper, results of image denoising efficiency prediction for filter based on discrete cosine transform (DCT) for the case of spatially correlated additive Gaussian Noise (SCGN) are given. The considered noise model is analyzed for different degrees of spatial correlation that produce varying non-homogeneous spectrum of the noise. PSNR metric is exploited to assess denoising efficiency. It is shown in this paper, that a prediction of denoising efficiency has high accuracy for data distorted by noise with different degrees of spatial correlation, and require low computational resources.

AB - In this paper, results of image denoising efficiency prediction for filter based on discrete cosine transform (DCT) for the case of spatially correlated additive Gaussian Noise (SCGN) are given. The considered noise model is analyzed for different degrees of spatial correlation that produce varying non-homogeneous spectrum of the noise. PSNR metric is exploited to assess denoising efficiency. It is shown in this paper, that a prediction of denoising efficiency has high accuracy for data distorted by noise with different degrees of spatial correlation, and require low computational resources.

KW - Denoising

KW - Efficiency Prediction

KW - Fitting

KW - Spatially Correlated Noise

U2 - 10.1109/TCSET.2016.7452171

DO - 10.1109/TCSET.2016.7452171

M3 - Conference contribution

SN - 9786176078067

SP - 750

EP - 754

BT - 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET)

PB - IEEE

ER -