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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

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

 PDF, 2376 kB

DOI: 10.18287/2412-6179-CO-1132

Pages: 914-920.

Full text of article: English language.

Abstract:
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 influence of speckle noise.

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

Citation:
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.

Acknowledgements:
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 computational resources of the Supercomputing Center in Peter the Great St. Petersburg Polytechnic University (www.spbstu.ru) .

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