# Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2007.02075 · HTML: https://mlsanity.com/paper/2007.02075/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Self-supervised remote sensing feature learning: Learning Paradigms, Challenges, and Future Works](https://mlsanity.com/paper/2211.08129/) (2022)
- [As if by magic: self-supervised training of deep despeckling networks with MERLIN](https://mlsanity.com/paper/2110.13148/) (2021)
- [Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion](https://mlsanity.com/paper/2108.06073/) (2021)
- [The potential of self-supervised networks for random noise suppression in seismic data](https://mlsanity.com/paper/2109.07344/) (2021)
- [DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images](https://mlsanity.com/paper/2406.02833/) (2024)
- [Content-Preserving Diffusion Model for Unsupervised AS-OCT image Despeckling](https://mlsanity.com/paper/2306.17717/) (2023)
- [A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2104.09350/) (2021)
- [Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning](https://mlsanity.com/paper/2102.00682/) (2021)
- [Multi-temporal speckle reduction with self-supervised deep neural networks](https://mlsanity.com/paper/2207.11095/) (2022)
- [PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks](https://mlsanity.com/paper/2401.07503/) (2024)
- [Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack](https://mlsanity.com/paper/2407.07041/) (2024)
- [Just Project! Multi-Channel Despeckling, the Easy Way](https://mlsanity.com/paper/2408.11531/) (2024)
- [Bayesian Despeckling of Structured Sources](https://mlsanity.com/paper/2501.11860/) (2025)
- [Self-Supervised Score-Based Despeckling for SAR Imagery via Log-Domain Transformation](https://mlsanity.com/paper/2601.14334/) (2026)
- [SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition](https://mlsanity.com/paper/2606.31668/) (2026)
- [Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images](https://mlsanity.com/paper/2207.11111/) (2022)
- [Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal](https://mlsanity.com/paper/2407.05087/) (2024)
- [Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithms](https://mlsanity.com/paper/2504.08115/) (2025)
- [Self-Supervised Compression and Artifact Correction for Streaming Underwater Imaging Sonar](https://mlsanity.com/paper/2511.13922/) (2025)
- [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)
- [Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization](https://mlsanity.com/paper/2605.24716/) (2026)
- [A Deep Learning Iterative Framework for Sentinel-1 Stripmap Enhancement Based on Azimuth Doppler Decomposition](https://mlsanity.com/paper/2605.29088/) (2026)
- [High-Resolution Flood Mapping With Sentinel-1 and Sentinel-2 via Misalignment-Robust Cross-Sensor Learning and Generative Despeckling](https://mlsanity.com/paper/2606.30511/) (2026)
- [γ-Bridge: A Look-Parametric Diffusion Bridge](https://mlsanity.com/paper/2607.22719/) (2026)
- [Implicit Neural Speckle Denoising](https://mlsanity.com/paper/2608.06574/) (2026)
- [Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling](https://mlsanity.com/paper/2608.15028/) (2026)

## Abstract (arXiv)

Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms. The recent success of deep learning envisions a new generation of despeckling techniques that could outperform classical model-based methods. However, current deep learning approaches to despeckling require supervision for training, whereas clean SAR images are impossible to obtain. In the literature, this issue is tackled by resorting to either synthetically speckled optical images, which exhibit different properties with respect to true SAR images, or multi-temporal SAR images, which are difficult to acquire or fuse accurately. In this paper, inspired by recent works on blind-spot denoising networks, we propose a self-supervised Bayesian despeckling method. The proposed method is trained employing only noisy SAR images and can therefore learn features of real SAR images rather than synthetic data. Experiments show that the performance of the proposed approach is very close to the supervised training approach on synthetic data and superior on real data in both quantitative and visual assessments.