radioelectronicsИзвестия высших учебных заведений России. РадиоэлектроникаJournal of the Russian Universities. Radioelectronics1993-89852658-4794Saint Petersburg Electrotechnical University10.32603/1993-8985-2021-24-4-6-18radioelectronics-537Research ArticleРАДИОТЕХНИЧЕСКИЕ СРЕДСТВА ПЕРЕДАЧИ, ПРИЕМА И ОБРАБОТКИ СИГНАЛОВRADIO ELECTRONIC FACILITIES FOR SIGNAL TRANSMISSION, RECEPTION AND PROCESSINGПодавление мультипликативного шума на радиолокационных изображенияхReduction of Multiplicative Noise in Radar Imageshttps://orcid.org/0000-0001-8471-450XТузоваА. А.TuzovaA. A.

Тузова Анна Андреевна – магистр (2020), инженер (2021)

ул. Лоцманская, д. 3, Санкт-Петербург, 190121

Anna A. Tuzova, Master (2020), engineer (2021)

3 Lotsmanskaya St., St Petersburg 190121 

tuzova@corp.smtu.ruhttps://orcid.org/0000-0003-0726-6613ПавловВ. А.PavlovV. A.

Павлов Виталий Александрович – кандидат технических наук (2020), ассистент (2020)

ул. Политехническая, д. 29, Санкт-Петербург, 195251

Vitalii A. Pavlov, Ph.D. (2020), assistant (2020)

29 Polytechnicheskaya St., St Petersburg 195251 

pavit@bk.ruhttps://orcid.org/0000-0003-0617-4514БеловА. А.BelovA. A.

Белов Андрей Александрович – специалист (1989), ведущий инженер (2018)

ул. Политехническая, д. 29, Санкт-Петербург, 195251

Andrei A. Belov, specialist (1989), Leading Engineer (2018)

29 Polytechnicheskaya St., St Petersburg 195251 

belov@spbstu.ruСанкт-Петербургский государственный морской технический университетРоссияSaint Petersburg State Marine Technical UniversityRussian FederationСанкт-Петербургский политехнический университет Петра ВеликогоРоссияPeter the Great St Petersburg Polytechnic UniversityRussian Federation202128092021244618Copyright © Тузова А.А., Павлов В.А., Белов А.А., 20212021Тузова А.А., Павлов В.А., Белов А.А.Tuzova A.A., Pavlov V.A., Belov A.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://re.eltech.ru/jour/article/view/537

Введение. Радиолокационное изображение (РЛИ) – это изображение, получаемое зондированием земной поверхности с помощью радиолокационного устройства. РЛИ обладает важной особенностью в виде спекл-шума, который вызывает зернистость фона. Данный шум необходимо фильтровать с целью улучшения качества РЛИ. Фильтры спекл-шума часто имеют в своей структуре один или несколько параметров, которые контролируют уровень сглаживания шума и значения которых приходится подбирать экспериментально. В статьях, посвященных фильтрации спекл-шума, авторы часто не поясняют, как были выбраны значения параметров фильтров.Цель работы. Представление методики для выбора оптимальных в смысле качества получаемого изображения параметров фильтров мультипликативного спекл-шума на РЛИ.Материалы и методы. Рассмотрена разработанная методика поиска оптимальных параметров фильтров спекл-шума применительно к наиболее часто используемым фильтрам. Поиск оптимальных параметров и тестирование работы фильтров проводятся на специально разработанном изображении, содержащем объекты, наиболее часто встречающиеся на РЛИ. Метрикой, оценивающей качество проведенной фильтрации, служил индекс структурного сходства SSIM (Structural Similarity Index Metric).Результаты. После нахождения оптимальных по SSIM параметров рассматриваемых фильтров проведено сравнение работы фильтров с точки зрения обработки РЛИ и найдены наилучшие фильтры для этой задачи. Также работа рассматриваемых фильтров протестирована на изображениях, содержащих различные типы объектов, а именно: большие объекты, мелкие объекты, резкие границы. Зная, какой фильтр наилучшим образом справляется со сглаживанием шума на той или иной области и какие для этого необходимы значения варьируемых параметров, можно использовать полученные результаты для фильтрации радиолокационных изображений. Фильтрация не только улучшает восприятие РЛИ человеком, но и позволяет снизить влияние спекл-шума на дальнейшую автоматизированную обработку РЛИ (детектирование объектов, сегментация областей и др.).Заключение. Предложенный алгоритм позволил найти оптимальные параметры для нескольких фильтров спекл-шума. Качество фильтрации оценивалось экспертным способом (визуально), посредством сравнения изображений до и после фильтрации, разностных изображений и одномерных срезов изображений. Фильтр Фроста и фильтр анизотропной диффузии с оптимальными параметрами показали лучшее качество обработки по SSIM.

Introduction. A radar image is an image obtained by remote sensing the earth's surface with a radar device. Radar images are characterized by background graininess caused by speckle noise, which should be filtered to improve the quality of radar images. The structure of speckle noise reduction filters often comprise one or more parameters to control the level of noise smoothing. The values of these parameters have to be selected experimentally. In works devoted to speckle noise filtering, the methods used for selecting filter paraments are rarely clarified.Aim. To present a methodology for selecting the parameters of multiplicative speckle noise filters on a radar image that are optimal in terms of the quality of the resulting image.Materials and methods. The article presents a method for determining the optimal parameters of speckle noise reduction filters. This method was applied to the most conventionally used filters. The search for optimal parameters and testing of the filters were carried out using a specially designed image, which contained the objects most frequently found on radar images. The structural similarity index (SSIM) metric was chosen as a metric that assesses the quality of filtration.Results. After determining the optimal (in terms of SSIM) parameters of speckle noise reduction filters, the filters were compared to select the best filters in terms of the quality of radar image processing. In addition, the operation of the filters under study was tested on images containing various types of objects, namely: large objects, small objects and sharp borders. Knowing which filter copes best with smoothing speckle noise in a particular area and what values of the variable parameters this requires, an optimal quality of radar images can be achieved. Filtering not only improves human perception of radar images, but also reduces the influence of speckle noise during their further processing (object detection, segmentation of areas, etc.).Conclusion. The proposed algorithm allowed optimal parameters for several speckle noise filters to be determined. The quality of filtration was assessed using an expert method (visually) by comparing images before and after filtration, differential images and one-dimensional image slices. The Frost filter and the anisotropic diffusion filter with optimal parameters showed the best processing quality according to the SSIM metric.

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The authors declare that there are no conflicts of interest present.