Image Set for Verification of Methods of Denoising of SAR Images

Oleksandr Miroshnichenko; Mykola Ponomarenko; Sergey Abramov; Vladimir Lukin; Karen Egiazarian

doi:10.1007/978-3-031-94845-9_14

Image Set for Verification of Methods of Denoising of SAR Images

Oleksandr Miroshnichenko
, Mykola Ponomarenko^*
, Sergey Abramov
, Vladimir Lukin
, Karen Egiazarian

^*Corresponding author for this work

Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

This paper addresses the problem of evaluating noise suppression methods for images produced by synthetic aperture radar (SAR) systems. It is demonstrated that noise in SAR images is multiplicative and exhibits characteristics between Rayleigh and Gaussian distributions. It is shown also that such noise is typically spatially correlated and features an asymmetric spatial spectrum. Our study highlights that the information available about SAR image formation is insufficient to reliably estimate the noise spectrum directly, necessitating its estimation before noise suppression. A test set of images with noise characteristics closely resembling those of real SAR data is proposed. This dataset, called SAR400, enables effective benchmarking of noise suppression methods. Using the proposed test set, several existing methods for suppressing spatially correlated noise are evaluated, and their limitations are analyzed.

Original language	English
Title of host publication	Integrated Computer Technologies in Mechanical Engineering, 2024
Subtitle of host publication	Synergetic Engineering, Volume 1
Editors	Oleksii Lytvynov, Volodymyr Pavlikov, Dmytro Krytskyi
Publisher	Springer
Pages	162-173
ISBN (Electronic)	978-3-031-94845-9
ISBN (Print)	9783031948442
DOIs	https://doi.org/10.1007/978-3-031-94845-9_14
Publication status	Published - 2025
Publication type	A4 Article in conference proceedings
Event	International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering-Synergetic Engineering - Kharkiv, Ukraine Duration: 12 Dec 2024 → 14 Dec 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1473 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering-Synergetic Engineering
Country/Territory	Ukraine
City	Kharkiv
Period	12/12/24 → 14/12/24

Keywords

image denoising
SAR imaging
spatially correlated noise

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-94845-9_14

Cite this

Miroshnichenko, O., Ponomarenko, M., Abramov, S., Lukin, V., & Egiazarian, K. (2025). Image Set for Verification of Methods of Denoising of SAR Images. In O. Lytvynov, V. Pavlikov, & D. Krytskyi (Eds.), Integrated Computer Technologies in Mechanical Engineering, 2024: Synergetic Engineering, Volume 1 (pp. 162-173). (Lecture Notes in Networks and Systems; Vol. 1473 LNNS). Springer. https://doi.org/10.1007/978-3-031-94845-9_14

Miroshnichenko, Oleksandr ; Ponomarenko, Mykola ; Abramov, Sergey et al. / Image Set for Verification of Methods of Denoising of SAR Images. Integrated Computer Technologies in Mechanical Engineering, 2024: Synergetic Engineering, Volume 1. editor / Oleksii Lytvynov ; Volodymyr Pavlikov ; Dmytro Krytskyi. Springer, 2025. pp. 162-173 (Lecture Notes in Networks and Systems).

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title = "Image Set for Verification of Methods of Denoising of SAR Images",

abstract = "This paper addresses the problem of evaluating noise suppression methods for images produced by synthetic aperture radar (SAR) systems. It is demonstrated that noise in SAR images is multiplicative and exhibits characteristics between Rayleigh and Gaussian distributions. It is shown also that such noise is typically spatially correlated and features an asymmetric spatial spectrum. Our study highlights that the information available about SAR image formation is insufficient to reliably estimate the noise spectrum directly, necessitating its estimation before noise suppression. A test set of images with noise characteristics closely resembling those of real SAR data is proposed. This dataset, called SAR400, enables effective benchmarking of noise suppression methods. Using the proposed test set, several existing methods for suppressing spatially correlated noise are evaluated, and their limitations are analyzed.",

keywords = "image denoising, SAR imaging, spatially correlated noise",

author = "Oleksandr Miroshnichenko and Mykola Ponomarenko and Sergey Abramov and Vladimir Lukin and Karen Egiazarian",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering-Synergetic Engineering ; Conference date: 12-12-2024 Through 14-12-2024",

year = "2025",

doi = "10.1007/978-3-031-94845-9\_14",

language = "English",

isbn = "9783031948442",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "162--173",

editor = "Oleksii Lytvynov and Volodymyr Pavlikov and Dmytro Krytskyi",

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Miroshnichenko, O, Ponomarenko, M, Abramov, S, Lukin, V & Egiazarian, K 2025, Image Set for Verification of Methods of Denoising of SAR Images. in O Lytvynov, V Pavlikov & D Krytskyi (eds), Integrated Computer Technologies in Mechanical Engineering, 2024: Synergetic Engineering, Volume 1. Lecture Notes in Networks and Systems, vol. 1473 LNNS, Springer, pp. 162-173, International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering-Synergetic Engineering, Kharkiv, Ukraine, 12/12/24. https://doi.org/10.1007/978-3-031-94845-9_14

Image Set for Verification of Methods of Denoising of SAR Images. / Miroshnichenko, Oleksandr; Ponomarenko, Mykola; Abramov, Sergey et al.
Integrated Computer Technologies in Mechanical Engineering, 2024: Synergetic Engineering, Volume 1. ed. / Oleksii Lytvynov; Volodymyr Pavlikov; Dmytro Krytskyi. Springer, 2025. p. 162-173 (Lecture Notes in Networks and Systems; Vol. 1473 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Image Set for Verification of Methods of Denoising of SAR Images

AU - Miroshnichenko, Oleksandr

AU - Ponomarenko, Mykola

AU - Abramov, Sergey

AU - Lukin, Vladimir

AU - Egiazarian, Karen

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - This paper addresses the problem of evaluating noise suppression methods for images produced by synthetic aperture radar (SAR) systems. It is demonstrated that noise in SAR images is multiplicative and exhibits characteristics between Rayleigh and Gaussian distributions. It is shown also that such noise is typically spatially correlated and features an asymmetric spatial spectrum. Our study highlights that the information available about SAR image formation is insufficient to reliably estimate the noise spectrum directly, necessitating its estimation before noise suppression. A test set of images with noise characteristics closely resembling those of real SAR data is proposed. This dataset, called SAR400, enables effective benchmarking of noise suppression methods. Using the proposed test set, several existing methods for suppressing spatially correlated noise are evaluated, and their limitations are analyzed.

AB - This paper addresses the problem of evaluating noise suppression methods for images produced by synthetic aperture radar (SAR) systems. It is demonstrated that noise in SAR images is multiplicative and exhibits characteristics between Rayleigh and Gaussian distributions. It is shown also that such noise is typically spatially correlated and features an asymmetric spatial spectrum. Our study highlights that the information available about SAR image formation is insufficient to reliably estimate the noise spectrum directly, necessitating its estimation before noise suppression. A test set of images with noise characteristics closely resembling those of real SAR data is proposed. This dataset, called SAR400, enables effective benchmarking of noise suppression methods. Using the proposed test set, several existing methods for suppressing spatially correlated noise are evaluated, and their limitations are analyzed.

KW - image denoising

KW - SAR imaging

KW - spatially correlated noise

U2 - 10.1007/978-3-031-94845-9_14

DO - 10.1007/978-3-031-94845-9_14

M3 - Conference contribution

AN - SCOPUS:105011067353

SN - 9783031948442

T3 - Lecture Notes in Networks and Systems

SP - 162

EP - 173

BT - Integrated Computer Technologies in Mechanical Engineering, 2024

A2 - Lytvynov, Oleksii

A2 - Pavlikov, Volodymyr

A2 - Krytskyi, Dmytro

PB - Springer

T2 - International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering-Synergetic Engineering

Y2 - 12 December 2024 through 14 December 2024

ER -

Miroshnichenko O, Ponomarenko M, Abramov S, Lukin V, Egiazarian K. Image Set for Verification of Methods of Denoising of SAR Images. In Lytvynov O, Pavlikov V, Krytskyi D, editors, Integrated Computer Technologies in Mechanical Engineering, 2024: Synergetic Engineering, Volume 1. Springer. 2025. p. 162-173. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-94845-9_14

Image Set for Verification of Methods of Denoising of SAR Images

Abstract

Publication series

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

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Cite this