# SAR Despeckling using a Denoising Diffusion Probabilistic Model

arXiv: https://arxiv.org/abs/2206.04514 · HTML: https://mlsanity.com/paper/2206.04514/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion](https://mlsanity.com/paper/1807.01569/) (2018)
- [SAR image despeckling through convolutional neural networks](https://mlsanity.com/paper/1704.00275/) (2017)
- [SAR Image Despeckling Using a Convolutional Neural Network](https://mlsanity.com/paper/1706.00552/) (2017)
- [Transformer-based SAR Image Despeckling](https://mlsanity.com/paper/2201.09355/) (2022)
- [Multi-Objective CNN Based Algorithm for SAR Despeckling](https://mlsanity.com/paper/2006.09050/) (2020)
- [SAR Despeckling Using Overcomplete Convolutional Networks](https://mlsanity.com/paper/2205.15906/) (2022)

## Built on by (most cited first, 19 of 19)

- [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024)
- [DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection](https://mlsanity.com/paper/2206.11892/) (2022)
- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [SAR Image Synthesis with Diffusion Models](https://mlsanity.com/paper/2405.07776/) (2024)
- [SDDPM: Speckle Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2311.10868/) (2023)
- [Improved Flood Insights: Diffusion-Based SAR to EO Image Translation](https://mlsanity.com/paper/2307.07123/) (2023)
- [SAR Despeckling via Regional Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2401.03122/) (2024)
- [Perception-based multiplicative noise removal using SDEs](https://mlsanity.com/paper/2408.10283/) (2024)
- [Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.19699/) (2025)
- [Self-Supervised Score-Based Despeckling for SAR Imagery via Log-Domain Transformation](https://mlsanity.com/paper/2601.14334/) (2026)
- [Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal](https://mlsanity.com/paper/2407.05087/) (2024)
- [Multi-task SAR Image Processing via GAN-based Unsupervised Manipulation](https://mlsanity.com/paper/2408.01553/) (2024)
- [Electrooptical Image Synthesis from SAR Imagery Using Generative Adversarial Networks](https://mlsanity.com/paper/2409.15331/) (2024)
- [Knowledge-guided Complex Diffusion Model for PolSAR Image Classification in Contourlet Domain](https://mlsanity.com/paper/2507.05666/) (2025)
- [Diffusion Posterior Sampler for Hyperspectral Unmixing with Spectral Variability Modeling](https://mlsanity.com/paper/2512.09871/) (2025)
- [Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reduction](https://mlsanity.com/paper/2601.01541/) (2026)
- [GeoDiff-SAR: A Geometric Prior Guided Diffusion Model for SAR Image Generation](https://mlsanity.com/paper/2601.03499/) (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)

## Abstract (arXiv)

Speckle is a multiplicative noise which affects all coherent imaging modalities including Synthetic Aperture Radar (SAR) images. The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications such as automatic target recognition and change detection. Thus, SAR despeckling is an important problem in remote sensing. In this paper, we introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method comprises of a Markov chain that transforms clean images to white Gaussian noise by repeatedly adding random noise. The despeckled image is recovered by a reverse process which iteratively predicts the added noise using a noise predictor which is conditioned on the speckled image. In addition, we propose a new inference strategy based on cycle spinning to improve the despeckling performance. Our experiments on both synthetic and real SAR images demonstrate that the proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.