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.
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References
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)
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)
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)
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)
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)
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)
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)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)
Gurevich, P., Stuke, H.: Learning uncertainty in regression tasks by deep neural networks. arXiv preprint arXiv:1707.07287 (2017)
Keelan, B.: Handbook of Image Quality: Characterization and Prediction. CRC Press, Boca Raton (2002)
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)
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)
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)
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)
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Uss, M., Vozel, B., Lukin, V., Chehdi, K. (2018). NoiseNet: Signal-Dependent Noise Variance Estimation with Convolutional Neural Network. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_35
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DOI: https://doi.org/10.1007/978-3-030-01449-0_35
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