A review of deep-learning techniques for SAR image restoration

TL;DR

This paper reviews recent deep learning methods for SAR image restoration, highlighting their superior performance over traditional techniques and discussing future research directions and challenges.

Contribution

It provides a comprehensive overview of recent deep learning approaches for SAR speckle reduction and identifies key research challenges and future directions.

Findings

Deep learning methods outperform traditional speckle reduction techniques.

Recent approaches leverage neural networks for improved SAR image restoration.

The paper discusses current challenges and future research directions in the field.

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.