Synthetic aperture radar image and its despeckling using variational methods: A Review of recent trends

被引:6
作者
Baraha, Satyakam [1 ]
Sahoo, Ajit Kumar [1 ]
机构
[1] Natl Inst Technol Rourkela, Dept Elect & Commun Engn, Rourkela, Orissa, India
关键词
Remote sensing; SAR; Speckle; Inverse-imaging; Regularization; Convex optimization; Variational methods; TOTAL GENERALIZED VARIATION; SPECKLE NOISE-REDUCTION; MULTICHANNEL HIGH-RESOLUTION; WIDE-SWATH SAR; MULTIPLICATIVE NOISE; QUALITY ASSESSMENT; ULTRASOUND IMAGES; MODEL; REMOVAL; SUPPRESSION;
D O I
10.1016/j.sigpro.2023.109156
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Synthetic aperture radar (SAR) has drawn significant interest from the remote sensing community due to its ability to provide high-resolution images irrespective of weather and illumination conditions. It has numerous applications in a variety of fields, including military surveillance, geological exploration, disaster management, and mapping of natural phenomena like climate change, forest management, and volcanic eruptions. The image acquired by coherent sensors such as SAR is corrupted by granular multiplicative noise, also known as speckle. It has an adverse influence on the visual interpretation of SAR images and prevents the use of various post-processing tasks. Despeckling addresses the removal of such noise from SAR images while preserving the image details. This work provides a brief summary of the theory, applications, speckle statistics, performance measures, and recent developments in despeckling filters using variational methods for SAR images. The primary focus is given to recently proposed despeckling filters, discussing their typical usage, benefits, and drawbacks.& COPY; 2023 Elsevier B.V. All rights reserved.
引用
收藏
页数:24
相关论文
共 156 条
  • [1] A combined higher order non-convex total variation with overlapping group sparsity for Poisson noise removal
    Adam, Tarmizi
    Paramesran, Raveendran
    Ratnavelu, Kuru
    [J]. COMPUTATIONAL & APPLIED MATHEMATICS, 2022, 41 (04)
  • [2] Image reconstruction under multiplicative speckle noise using total variation
    Afonso, M.
    Miguel Sanches, J.
    [J]. NEUROCOMPUTING, 2015, 150 : 200 - 213
  • [3] A total variation recursive space-variant filter for image denoising
    Afonso, Manya V.
    Sanches, Joao M. R.
    [J]. DIGITAL SIGNAL PROCESSING, 2015, 40 : 101 - 116
  • [4] Blind Inpainting Using l0 and Total Variation Regularization
    Afonso, Manya V.
    Raposo Sanches, Joao Miguel
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2015, 24 (07) : 2239 - 2253
  • [5] Aghababaei H., 2023, APERTURE RADAR REMOT
  • [6] Nonlocal Model-Free Denoising Algorithm for Single- and Multichannel SAR Data
    Aghababaei, Hossein
    Ferraioli, Giampaolo
    Vitale, Sergio
    Zamani, Roghayeh
    Schirinzi, Gilda
    Pascazio, Vito
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [7] The Beltrami SAR Framework for Multichannel Despeckling
    Amao-Oliva, Joel
    Torres-Roman, Deni
    Yanez-Vargas, Israel
    Reigber, Andreas
    Jaeger, Marc
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 12 (08) : 2989 - 3003
  • [8] A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images
    Argenti, Fabrizio
    Lapini, Alessandro
    Alparone, Luciano
    Bianchi, Tiziano
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2013, 1 (03): : 6 - 35
  • [9] A variational approach to removing multiplicative noise
    Aubert, Gilles
    Aujol, Jean-Francois
    [J]. SIAM JOURNAL ON APPLIED MATHEMATICS, 2008, 68 (04) : 925 - 946
  • [10] A new nonconvex approach for image restoration with Gamma noise
    Bai, Lufeng
    [J]. COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2019, 77 (10) : 2627 - 2639