Отрывок: 5) The AD filter with quadratic and exponential func- tion g(x) smooths noise well enough, and highlights the boundaries of objects. It can be recommended for radar image processing 6) The neural network cancels noise about as well as the Frost and AD filters. However, visual inspection shows that some noise pixels remain, albeit more dis- tant from each other. This is likely be...
Полная запись метаданных
Поле DC | Значение | Язык |
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dc.contributor.author | Pavlov, V. | - |
dc.contributor.author | Tuzova, A. | - |
dc.contributor.author | Belov, A. | - |
dc.contributor.author | Matveev, Y. | - |
dc.date.accessioned | 2023-12-29 12:58:53 | - |
dc.date.available | 2023-12-29 12:58:53 | - |
dc.date.issued | 2022-12 | - |
dc.identifier | Dspace\SGAU\20231226\107747 | ru |
dc.identifier.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. | ru |
dc.identifier.uri | https://dx.doi.org/10.18287/2412-6179-CO-1132 | - |
dc.identifier.uri | http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/An-automated-method-for-finding-the-optimal-parameters-of-adaptive-filters-for-speckle-denoising-of-SAR-images-107747 | - |
dc.description.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 in-fluence of speckle noise. | ru |
dc.description.sponsorship | 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). | ru |
dc.language.iso | en | ru |
dc.publisher | Самарский национальный исследовательский университет | ru |
dc.relation.ispartofseries | 46;6 | - |
dc.subject | speckle noise | ru |
dc.subject | radar image | ru |
dc.subject | SAR | ru |
dc.subject | noise reduction | ru |
dc.subject | image processing | ru |
dc.subject | SSIM | ru |
dc.subject | GMSD | ru |
dc.subject | optimal filter parameters | ru |
dc.title | An automated method for finding the optimal parameters of adaptive filters for speckle denoising of SAR images | ru |
dc.type | Article | ru |
dc.textpart | 5) The AD filter with quadratic and exponential func- tion g(x) smooths noise well enough, and highlights the boundaries of objects. It can be recommended for radar image processing 6) The neural network cancels noise about as well as the Frost and AD filters. However, visual inspection shows that some noise pixels remain, albeit more dis- tant from each other. This is likely be... | - |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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2412-6179_2022_46_6_914-920.pdf | 2.38 MB | Adobe PDF | Просмотреть/Открыть |
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