Skip to main content

2018 | OriginalPaper | Buchkapitel

NoiseNet: Signal-Dependent Noise Variance Estimation with Convolutional Neural Network

verfasst von : Mykhail Uss, Benoit Vozel, Vladimir Lukin, Kacem Chehdi

Erschienen in: Advanced Concepts for Intelligent Vision Systems

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this paper, the problem of blind estimation of uncorrelated signal-dependent noise parameters in images is formulated as a regression problem with uncertainty. It is shown that this regression task can be effectively solved by a properly trained deep convolution neural network (CNN), called NoiseNet, comprising regressor branch and uncertainty quantifier branch. The former predicts noise standard deviation (STD) for a 32 \(\times \) 32 pixels image patch, while the latter predicts STD of regressor error. The NoiseNet architecture is proposed and peculiarities of its training are discussed. Signal-dependent noise parameters are estimated by robust iterative processing of many local estimates provided by the NoiseNet. The comparative analysis for real data from NED2012 database is carried out. Its results show that the NoiseNet provides accuracy better than the state-of-the-art existing methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Abramova, V., Abramov, S., Lukin, V., Vozel, B., Chehdi, K.: Scatter-plot-based method for noise characteristics evaluation in remote sensing images using adaptive image clustering procedure. In: Proceedings of the SPIE, vol. 10004, pp. 10004–10004-11 (2016) Abramova, V., Abramov, S., Lukin, V., Vozel, B., Chehdi, K.: Scatter-plot-based method for noise characteristics evaluation in remote sensing images using adaptive image clustering procedure. In: Proceedings of the SPIE, vol. 10004, pp. 10004–10004-11 (2016)
2.
Zurück zum Zitat Almeida, M.S.C., Figueiredo, M.A.T.: Parameter estimation for blind and non-blind deblurring using residual whiteness measures. IEEE Trans. Image Process. 22(7), 2751–2763 (2013)MathSciNetCrossRefMATH Almeida, M.S.C., Figueiredo, M.A.T.: Parameter estimation for blind and non-blind deblurring using residual whiteness measures. IEEE Trans. Image Process. 22(7), 2751–2763 (2013)MathSciNetCrossRefMATH
3.
Zurück zum Zitat Alparone, L., Selva, M., Aiazzi, B., Baronti, S., Butera, F., Chiarantini, L.: Signal-dependent noise modelling and estimation of new-generation imaging spectrometers. In: First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2009, pp. 1–4 (2009) Alparone, L., Selva, M., Aiazzi, B., Baronti, S., Butera, F., Chiarantini, L.: Signal-dependent noise modelling and estimation of new-generation imaging spectrometers. In: First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2009, pp. 1–4 (2009)
4.
Zurück zum Zitat Ce, L., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 299–314 (2008)CrossRef Ce, L., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 299–314 (2008)CrossRef
5.
Zurück zum Zitat Danielyan, A., Foi, A.: Noise variance estimation in nonlocal transform domain. In: International Workshop on Local and Non-Local Approximation in Image Processing, pp. 41–45 (2009) Danielyan, A., Foi, A.: Noise variance estimation in nonlocal transform domain. In: International Workshop on Local and Non-Local Approximation in Image Processing, pp. 41–45 (2009)
6.
Zurück zum Zitat Fevralev, D., Ponomarenko, N., Lukin, V., Abramov, S., Egiazarian, K.O., Astola, J.T.: Efficiency analysis of DCT-based filters for color image database. In: Image Processing: Algorithms and Systems IX, vol. 7870, p. 78700R. International Society for Optics and Photonics (2011) Fevralev, D., Ponomarenko, N., Lukin, V., Abramov, S., Egiazarian, K.O., Astola, J.T.: Efficiency analysis of DCT-based filters for color image database. In: Image Processing: Algorithms and Systems IX, vol. 7870, p. 78700R. International Society for Optics and Photonics (2011)
7.
Zurück zum Zitat Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical poissonian-gaussian noise modeling and fitting for single-image raw-data. IEEE Trans. Image Process. 17(10), 1737–1754 (2008)MathSciNetCrossRefMATH Foi, A., Trimeche, M., Katkovnik, V., Egiazarian, K.: Practical poissonian-gaussian noise modeling and fitting for single-image raw-data. IEEE Trans. Image Process. 17(10), 1737–1754 (2008)MathSciNetCrossRefMATH
8.
Zurück zum Zitat Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)MATH Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)MATH
9.
Zurück zum Zitat Gurevich, P., Stuke, H.: Learning uncertainty in regression tasks by deep neural networks. arXiv preprint arXiv:1707.07287 (2017) Gurevich, P., Stuke, H.: Learning uncertainty in regression tasks by deep neural networks. arXiv preprint arXiv:​1707.​07287 (2017)
10.
Zurück zum Zitat Keelan, B.: Handbook of Image Quality: Characterization and Prediction. CRC Press, Boca Raton (2002)CrossRef Keelan, B.: Handbook of Image Quality: Characterization and Prediction. CRC Press, Boca Raton (2002)CrossRef
11.
Zurück zum Zitat Tsin, Y., Ramesh, V., Kanade, T.: Statistical calibration of ccd imaging process. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 480–487 (2001) Tsin, Y., Ramesh, V., Kanade, T.: Statistical calibration of ccd imaging process. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 480–487 (2001)
12.
Zurück zum Zitat 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. Adv. Signal Process. 2011(1), 806516 (2011)CrossRef 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. Adv. Signal Process. 2011(1), 806516 (2011)CrossRef
13.
Zurück zum Zitat Uss, M., Vozel, B., Lukin, V., Chehdi, K.: Maximum likelihood estimation of spatially correlated signal-dependent noise in hyperspectral images. Opt. Eng. 51(11), 111712-1–111712-11 (2012)CrossRef Uss, M., Vozel, B., Lukin, V., Chehdi, K.: Maximum likelihood estimation of spatially correlated signal-dependent noise in hyperspectral images. Opt. Eng. 51(11), 111712-1–111712-11 (2012)CrossRef
14.
Zurück zum Zitat Uss, M., Vozel, B., Lukin, V.V., Chehdi, K.: Image informative maps for component-wise estimating parameters of signal-dependent noise. J. Electron. Imaging 22(1), 013019–013019 (2013)CrossRef Uss, M., Vozel, B., Lukin, V.V., Chehdi, K.: Image informative maps for component-wise estimating parameters of signal-dependent noise. J. Electron. Imaging 22(1), 013019–013019 (2013)CrossRef
Metadaten
Titel
NoiseNet: Signal-Dependent Noise Variance Estimation with Convolutional Neural Network
verfasst von
Mykhail Uss
Benoit Vozel
Vladimir Lukin
Kacem Chehdi
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-01449-0_35