# A review of deep-learning techniques for SAR image restoration

arXiv: https://arxiv.org/abs/2101.11852 · HTML: https://mlsanity.com/paper/2101.11852/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2021.

## Built on by (most cited first, 5 of 5)

- [As if by magic: self-supervised training of deep despeckling networks with MERLIN](https://mlsanity.com/paper/2110.13148/) (2021)
- [CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation](https://mlsanity.com/paper/2603.12008/) (2026)
- [Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images](https://mlsanity.com/paper/2207.11111/) (2022)
- [Comparative Study of Weighted and Coupled Second- and Fourth-Order PDEs for Image Despeckling in Grayscale, Color, SAR, and Ultrasound](https://mlsanity.com/paper/2604.23612/) (2026)
- [A Coupled Fourth Order Telegraph Diffusion Framework Using Grayscale Indicators for Image Despeckling](https://mlsanity.com/paper/2605.00881/) (2026)

## Abstract (arXiv)

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.