Abstract
To date, a large number of research works have been conducted on noise level estimation (NLE) that automatically and accurately estimates the unknown noise level for an observed noisy image. Nevertheless, the state-of-the-art NLE algorithms are still limited in efficiency, which will undermine the overall execution performance of the subsequent denoiser. By making full use of the powerful nonlinear modeling capabilities of convolutional neural networks (CNNs), a shallow CNN-based noise separator with high execution efficiency for natural images was proposed to obtain the coarse noise component (difference image) from a single observed noisy image. Based on the fact that the coarse noise component tends to follow a Gaussian-like distribution, we chose to model it with the generalized Gaussian distribution model, whose parameters are strongly sensitive to noise level and can be treated as features to characterize the degradation degree of a given noisy image. As such, the extracted features were instantly mapped to their corresponding noise level via a back-propagation (BP) neural network pre-trained on the representative training samples, resulting in a fast yet reliable NLE algorithm. Experiments demonstrate that our training-based NLE algorithm exploiting the shallow CNN-based noise separator and BP network outperforms the state-of-the-art counterparts on estimating noise level with the least executing time over a wide range of image contents and noise levels, providing a highly effective solution to blind denoising as the preprocessing module of a non-blind denoiser.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Xu, S., Liu, T., Zhang, G., Tang, Y.: A two-stage noise level estimation using automatic feature extraction and mapping model. IEEE Signal Process. Lett. 26(1), 179–183 (2019)
Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018)
Yang, D., Sun, J.: BM3D-Net: a convolutional neural network for transform-domain collaborative filtering. IEEE Signal Process. Lett. 25(1), 55–59 (2018)
Dong, L., Zhou, J., Tang, Y.Y.: Noise level estimation for natural images based on scale-invariant kurtosis and piecewise stationarity. IEEE Trans. Image Process. 26(2), 1017–1030 (2017)
Rakhshanfar, M., Amer, M.A.: Estimation of Gaussian, Poissonian–Gaussian, and processed visual noise and its level function. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 25(9), 4172–4185 (2016)
Camille, S., Charles-Alban, D., Jean-Francois, A.: Estimation of the noise level function based on a nonparametric detection of homogeneous image regions. SIAM J. Imaging Sci. 8(4), 2622–2661 (2015)
Immerkar, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64(2), 300–302 (1996)
Yang, S., Tai, S.: A design framework for hybrid approaches of image noise estimation and its application to noise reduction. J. Vis. Commun. Image Represent. 23(5), 812–826 (2012)
Pyatykh, S., Hesser, J., Zheng, L.: Image noise level estimation by principal component analysis. IEEE Trans Image Process. Publ. IEEE Signal Process. Soc. 22(2), 687 (2013)
Liu, X., Tanaka, M., Okutomi, M.: Single-image noise level estimation for blind denoising. IEEE Trans. Image Process. 22(12), 5226–5237 (2013)
Chen, G., Zhu, F., Heng, P.A.: An efficient statistical method for image noise level estimation. In: IEEE International Conference on Computer Vision, pp. 477–485 (2015)
Donoho, D.L., Johnstone, L.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432), 1200–1224 (1995)
Colom, M., Buades, A., Morel, J.-M.: Nonparametric noise estimation method for raw images. J. Opt. Soc. Am. Opt. Image Sci. Vis. 31(4), 863–871 (2014)
Zoran, D., Weiss, Y.: Scale invariance and noise in natural images. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2209–2216 (2009)
Liu, W., Lin, W.: Additive white Gaussian noise level estimation in SVD domain for images. IEEE Trans. Image Process. 22(3), 872–883 (2013)
Peng, X., Yuan, M., Yu, Z., Yau, W.Y., Zhang, L.: Semi-supervised subspace learning with L2 graph. Neurocomputing 208(October), 143–152 (2016)
Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29, 2352–2449 (2017)
Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26, 3142–3155 (2017)
Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 3350–3364 (2011)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Sharifi, K., Leron-Garcia, A.: Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video. IEEE Trans. Circuits Syst. Video Technol. 5(1), 52–56 (1995)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)
Xu, S., Zeng, X., Tang, Y.: Fast noise level estimation algorithm based on two-stage support vector regression. J. Comput. Aided Des. Comput. Graph. 30(3), 447–458 (2018)
Wen-Yi, P.: Research on optimization and implementation of BP neural network algorithm. In: 2014 7th International Conference on Intelligent Computation Technology and Automation, pp. 104–107 (2014)
Gupta, P., Bampis, C.G., Jin, Y., Bovik, A.C.: Natural scene statistics for noise estimation. In: 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 85–88 (2018)
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China under Grants 61662044, 61163023, and 51765042, and Jiangxi Provincial Natural Science Foundation under Grant 20171BAB202017.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Xu, S., Lin, Z., Zhang, G. et al. A fast yet reliable noise level estimation algorithm using shallow CNN-based noise separator and BP network. SIViP 14, 763–770 (2020). https://doi.org/10.1007/s11760-019-01608-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-019-01608-z