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A review of deep-learning techniques for SAR image restoration

Abstract : The speckle phenomenon remains a major hurdle for the analysis of SAR images. The development of speckle reduction methods closely follows methodological progress in the field of image restoration. The advent of deep neural networks has offered new ways to tackle this longstanding problem. Deep learning for speckle reduction is a very active research topic and already shows restoration performances that exceed that of the previous generations of methods based on the concepts of patches, sparsity, wavelet transform or total variation minimization. The objective of this paper is to give an overview of the most recent works and point the main research directions and current challenges of deep learning for SAR image restoration.
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Contributor : Loïc Denis Connect in order to contact the contributor
Submitted on : Wednesday, January 27, 2021 - 2:40:13 PM
Last modification on : Saturday, June 25, 2022 - 9:24:17 AM
Long-term archiving on: : Wednesday, April 28, 2021 - 6:52:18 PM


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  • HAL Id : ujm-03123042, version 1
  • ARXIV : 2101.11852


Loïc Denis, Emanuele Dalsasso, Florence Tupin. A review of deep-learning techniques for SAR image restoration. IGARSS 2021, Jul 2021, Bruxelles (virtual), Belgium. ⟨ujm-03123042⟩



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