# SAR image despeckling through convolutional neural networks

arXiv: https://arxiv.org/abs/1704.00275 · HTML: https://mlsanity.com/paper/1704.00275/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2017.

## Built on by (most cited first, 30 of 30)

- [SAR Despeckling using a Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2206.04514/) (2022)
- [As if by magic: self-supervised training of deep despeckling networks with MERLIN](https://mlsanity.com/paper/2110.13148/) (2021)
- [Transformer-based SAR Image Despeckling](https://mlsanity.com/paper/2201.09355/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [SAR Despeckling via Regional Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2401.03122/) (2024)
- [A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2104.09350/) (2021)
- [Exploring Scalable Unified Modeling for General Low-Level Vision](https://mlsanity.com/paper/2507.14801/) (2025)
- [Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning](https://mlsanity.com/paper/2102.00682/) (2021)
- [SAR Strikes Back: A New Hope for RSVQA](https://mlsanity.com/paper/2501.08131/) (2025)
- [Homography augumented momentum constrastive learning for SAR image retrieval](https://mlsanity.com/paper/2109.10329/) (2021)
- [SAR Despeckling Using Overcomplete Convolutional Networks](https://mlsanity.com/paper/2205.15906/) (2022)
- [A Tunable Despeckling Neural Network Stabilized via Diffusion Equation](https://mlsanity.com/paper/2411.15921/) (2024)
- [NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function](https://mlsanity.com/paper/2108.11573/) (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)
- [Frontiers of Deep Learning: From Novel Application to Real-World Deployment](https://mlsanity.com/paper/2407.14386/) (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)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images](https://mlsanity.com/paper/2207.11111/) (2022)
- [SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters](https://mlsanity.com/paper/2403.01210/) (2024)
- [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)
- [SAR-FAH: A Frequency-Adaptive Hybrid Network based on Neural ODEs for Structural-Preserving SAR Despeckling](https://mlsanity.com/paper/2511.05890/) (2025)
- [Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reduction](https://mlsanity.com/paper/2601.01541/) (2026)
- [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)
- [Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression](https://mlsanity.com/paper/2607.14064/) (2026)
- [γ-Bridge: A Look-Parametric Diffusion Bridge](https://mlsanity.com/paper/2607.22719/) (2026)

## Abstract (arXiv)

In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.