(46-6) 09 * << * >> * Русский * English * Содержание * Все выпуски

An automated method for finding the optimal parameters of adaptive filters for speckle denoising of SAR images
V. Pavlov 1, A. Tuzova 2, A. Belov 1, Y. Matveev 1

Peter the Great St.Petersburg Polytechnic University,
195251, Russia, St.Petersburg, Polytechnicheskaya, 29;
Saint-Petersburg State Marine Technical University (SMTU),
190121, Russia, St.Petersburg, Lotsmanskaya, 3

 PDF, 2376 kB

DOI: 10.18287/2412-6179-CO-1132

Страницы: 914-920.

Язык статьи: English.

Many different filters can be used to reduce multiplicative speckle noise on radar images. Most of these filters have some parameters whose values influence the result of filtering. Finding optimal values of such parameters may be a non-trivial task. In this paper, a formal automated method for finding optimal parameters of speckle noise reduction filters is proposed. Using a specially designed test image, optimal parameters for the most commonly used filters were found using several image quality assessment metrics, including the Structural Similarity Index (SSIM) and Gradient Magnitude Similarity Deviation (GMSD). The use of filters with optimal parameters allows processing (detection, segmentation, etc.) of radar images with minimal in-fluence of speckle noise.

Ключевые слова:
speckle noise, radar image, SAR, noise reduction, image processing, SSIM, GMSD, optimal filter parameters.

The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of Worldclass Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17.11.2020).
     The results of the work were obtained using computa-tional resources of the Supercomputing Center in Peter the Great St. Petersburg Polytechnic University (www.spbstu.ru).

Pavlov V, Tuzova A, Belov A, Matveev Y. An automated method for finding the optimal parameters of adaptive filters for speckle denoising of SAR images. Computer Optics 2022; 46(6): 914-920. DOI: 10.18287/2412-6179-CO-1132.


  1. Pavlov VA, Belov AA, Tuzova AA. Implementation of synthetic aperture radar processing algorithms on the Jetson TX1 platform. IEEE Int Conf on Electrical Engineering and Photonics (EExPolytech) 2019: 90-93.
  2. Ozdemii C. Inverse synthetic aperture radar imaging with MATLAB Algorithms. Hoboken, New Jersey: John Wiley & Sons Inc; 2012.
  3. Skolnik M. Radar handbook. McGraw-Hill; 2008.
  4. Goodman JW. Some fundamental properties of speckle. J Opt Soc Am 1976; 66(11): 1145-1150.
  5. Frost VS, Stiles JA, Shanmugan KS, Holtzman JC. A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell 1982; PAMI-4(2): 157-166.
  6. Goldfinger AD. Estimation of spectra from speckled images. IEEE Trans Aerosp Electron Syst 1982; AES-18(5): 675-681.
  7. Fursov V, Zherdev D, Kazanskiy N. Support subspaces method for synthetic aperture radar automatic target recognition. Int J Adv Robot Syst 2016; 13(5): 1-11. DOI: 10.1177/1729881416664848.
  8. Pavlov VA, Belov AA, Tuzova AA. Investigation of the influence of speckle noise on the accuracy of object detection by convolutional neural networks. 2021 Int Conf on Electrical Engineering and Photonics (EExPolytech) 2021: 47-50.
  9. Prakash KB, Babu RV, VenuGopal B. Image independent filterfor removal of speckle noise. International Journal of Computer Science Issues 2011; 8(5): 196-201.
  10. Gifani P, Behnam H, Sani ZA. Noise reduction of echocardiographic images based on temporal information. IEEE Trans Ultrason Ferroelectr Freq Control 2014; 61(4): 620-630.
  11. Sarode MV, Deshmukh PR. Reduction of speckle noise and image enhancement of images using filtering technique. Int J Adv Technol 2011; 2: 30-38.
  12. Domg Y, Milne AK, Forster BC. Toward edge sharpening: a SAR speckle filtering algorithm. IEEE Trans Geosci Remote Sens 2001; 39(4): 851-863.
  13. Yu Y, Acton ST. Speckle reducing anisotropic diffusion. IEEE Trans Image Process 2002; 11: 1260-1270.
  14. Gomez L, Buemi ME, Jacobo-Berlles JC, Mejail ME. A new image quality index for objectively evaluating despeckling filtering in SAR images. IEEE J Sel Top Appl Earth Obs Remote Sens 2016; 9(3): 1297-1307.
  15. Huang X, Jia Z, Zhou J, Yang J, Kasabov N. Speckle reduction of reconstructions of digital holograms using gamma-correction and filtering. IEEE Access 2018; 6: 5227-5235.
  16. Lopera O, Heremans R, Pizurica A, Dupont Y. Filtering speckle noise in SAS images to improve detection and identification of seafloor targets. 2010 Int WaterSide Security Conf 2010; 1-4.
  17. Touzi R. A review of speckle filtering in the context of estimation theory. IEEE Trans Geosci Remote Sens 2002; 40(11): 2392-2404.
  18. Aja-Fernandez S, Alberola-Lopez C. On the estimation of the coefficientof variation for anisotropic diffusion speckle filtering. IEEE Trans Image Process 2006; 15(9): 2694-2701.
  19. Krissian K, Westin CF, Kikinis R, Vosburgh KG. Oriented speckle reducing anisotropic diffusion. IEEE Trans Image Process 2007; 16(5): 1412-1424.
  20. Lee J-S, Wen J-H, Ainsworth TL. Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans Geosci Remote Sens 2016; 47(1): 202-213.
  21. Dong X, Zhang D, Cui K. Spatial filtering strategies on deforestation detection using SAR image textures. CIE Int Conf on Radar (RADAR) 2016; 1-4.
  22. Karabchevsky S, Kahana D, Ben-Harush O, Guterman H. FPGA-based adaptive speckle suppression filter for underwater imaging sonar. IEEE J Ocean Eng 2011; 36(4): 646-657.
  23. Gonzalez RC, Woods RE. Digital image processing. Upper Saddle River, New Jersey: Pearson Education Inc; 2008.
  24. Lee JS. Digital image enhancement and noise filtering by using local statistics. IEEE Trans Pattern Anal Mach Intell 1980; PAMI-2(2): 165-168.
  25. Kuan D, Sawchuk A, Strand T, Chavel P. Adaptive restoration of images with speckle. IEEE Trans Acoust Speech Signal Process 1987; 25(3): 373-383.
  26. Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Sixth Int Conf on Computer Vision 1998: 839-846.
  27. Lopes A, Nezry E, Touzi R, Laur H. Structure detection and statistical adaptive speckle filtering in SAR images. Int J Remote Sens 1993; 14(9): 1735-1758.
  28. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990; 12(7): 629-639.
  29. Sheikh HR, Bovik AC. Image information and visual quality. IEEE Trans Image Process 2006; 15(2): 430-444.
  30. Wang Z, Bovik AC, Sheikh HR. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 2004; 13(4): 600-612.
  31. Xue W, Zhang L, Mou X, Bovik AC. Gradient magnitude similarity deviation: A highly efficient perceptual image quality index. IEEE Trans Image Process 2014; 23(2): 684-695.
  32. Tuzova AA, Pavlov VA, Belov AA, Volvenko SV. Comparison of image quality assessment metrics for evaluation of performance of anisotropic diffusion filter for SAR images. IEEE Int Conf on Electrical Engineering and Photonics (EExPolytech) 2020: 176-179.
  33. Belov AA, Pavlov VA, Tuzova AA. A method of finding optimal parameters of speckle noise reduction filters. Internet of things, smart spaces, and next generation networks and systems. Cham: Springer International Publishing; 2020: 133-141.
  34. Choi H, Jeong J. Speckle noise reduction technique for SAR images using statistical characteristics of speckle noise and discrete wavelet transform. Remote Sens 2019; 11: 1184.
  35. Xie H, Pierce LE, Ulaby FT. Statistical properties of logarithmically transformed speckle. IEEE Trans Geosci Remote Sens 2002; 40(3): 721-727.
  36. Chernoff H, Lehmann EL. The use of maximum likelihood estimates in χ2 tests for goodness of fit. Ann Math Statist 1954; 25(3): 579-586.
  37. Pearson K. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. In Book: Kotz S, Johnson NL, eds. Breakthroughs in statistics: Methodology and distribution. NewYork, NY: Springer; 1992: 11-28.
  38. 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 2017; 26(7): 3142-3155.
  39. The project to find the optimal parameters for speckle noise reduction filters. Source: <https://github.com/AnnaTuzova/Speckle-noise-project>.
  40. ICEYE. Source: <https://www.iceye.com/>.
  41. Capella Space. Source: <https://www.capellaspace.com/>.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20