(45-5) 10 * << * >> * Russian * English * Content * All Issues

Methods for image noise level estimation
A.I. Novikov 1, A.V. Pronkin 1

1 Ryazan State Radio Engineering University named after V.F. Utkin,
390005, Ryazan, Russia, Gagarina 59/1

 PDF, 1595 kB

DOI: 10.18287/2412-6179-CO-894

Pages: 713-720.

Full text of article: Russian language.

Abstract:
The article presents a method for estimating the level of discrete white noise in an image, based on the use of linear difference operators with a vector mask. Two variants of a new method for estimating the noise level are proposed, which differ in the accuracy of the obtained estimates and computational complexity. The first version of the method can be attributed to the class of block methods, whereas the second one is intended for the rapid image analysis and is based on processing a small number of rows or columns of an image.

Keywords:
linear smoothing operators, difference operators, cancellation of the deterministic component of the image, noise suppression, noise dispersion.

Citation:
Novikov AI, Pronkin AV. Methods for image noise level estimation. Computer Optics 2021; 45(5): 713-720. DOI: 10.18287/2412-6179-CO-894.

Acknowledgements:
The work was partly funded by the Russian Foundation for Basic Research under project No 19-31-90113 (“Introduction”, “General method of signal linear super-resolution”, “Continuous-discrete observation model”, “Optimal restoration of discrete values of a continuous signal”, “Optimal restoration of discrete values of a continuous signal – frequency domain analysis”, “Error of the optimal restoration” and  “Optimal restoration of a whole continuous signal”) and research project No 19-07-00474 (“Experimental research of the proposed method”).

References:
  1. Kostyashkin LN, Nikiforov MB, eds. Image processing in aviation vision systems [In Russian]. Moscow: “Fizmatlil” Publisher; 2016.
  2. Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Pearson; 2007.
  3. Tomasi C. Manduchi R. Bilateral filtering for grey and color images. Proc 1998 IEEE Int Conf on Computer Vision 1998: 839-846.
  4. Lee JS. Digital image smoothing and the sigma filter. Comput Viz Gr Image Process 1983; 24(2): 255-269.
  5. Shcerbakov MA, Panov AP. Nonlinear filtering with adaptation to local properties of the image. Computer Optics 2014; 38(4): 818-824. DOI: 10.18287/0134-2452-2014-38-4-818-824.
  6. Tikhonov AN, Arsenin VYa. Methods for solving incorrect problems [In Russian]. Moscow: “Nauka” Publisher; 1986.
  7. Sizikov VS, Belov IA. Reconstruction of blurred and defocused images by regularization method [In Russian]. Opticheskii Zhurnal 2000; 76(4): 60-63.
  8. Voskoboinikov UE, Litasov VA. Analysis and synthesis of signals and images a stable image reconstruction algorithm for inexact point-spread function [In Russian]. Autometriya 2006; 42(6): 3-15.
  9. Donoho DL. De-noising by soft-thresholding. IEEE Trans Inf Theory 1995; 41(3): 613-627.
  10. Olsen SI. Noise variance estimation in images. 8th Scandinavian Conference on Inage Analysis 1993: 25-28.
  11. Kalinkina DA. Determining the noise level in an image based on averaging the variance in blocks [In Russian]. Source: <http://graphics.cs.msu.ru/ru/publications/text/l2005kal.pdf>.
  12. Kovalevsky V. Effective filtering and boundary detection [In Russian]. Source:        <http://irtc.org.ua/image/app/webroot/Files/presentations/Kovalevskiy/Kovalevski_Effiziente_Filterung_und_Kantendetektion_Kurz.pdf>.
  13. Ghazal M, Amer A, Ghrayeb A. Structure-oriented spatio-temporal video noise estimation. IEEE Int Conf on Acoustics Speech and Signal Processing 2006; 2: 845-848.
  14. Lapshenkov EM. No reference estimation of noise level of digital image is based on harmonic analysis [In Russian]. Computer Optics 2012; 36(3): 439-447.
  15. Voskoboinikov UE, Krysov DA. Estimation of the noise measurement characteristics in the model “Signal + Noise” [In Russian]. Automatcs and Software Enginery 2018; 3(25): 54-61.
  16. Kendall MG, Stuart A. The advanced theory of statistics. Volume 3: Design and analysis, and time series. 4th ed. Macmillan; 1983.
  17. Novikov AI. The formation of operators with given properties to solve original image processing tasks. Pattern Recogn Image Anal 2015; 25(2): 230-236. DOI: 10.1134/S1054661815020194.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20