# SAR Image Despeckling Using a Convolutional Neural Network

arXiv: https://arxiv.org/abs/1706.00552 · HTML: https://mlsanity.com/paper/1706.00552/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2017.

## Built on by (most cited first, 24 of 24)

- [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 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)
- [Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?](https://mlsanity.com/paper/2204.11493/) (2022)
- [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)
- [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)
- [Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1](https://mlsanity.com/paper/2408.15678/) (2024)
- [Bayesian Despeckling of Structured Sources](https://mlsanity.com/paper/2501.11860/) (2025)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Deep learning based automatic detection of offshore oil slicks using SAR data and contextual information](https://mlsanity.com/paper/2204.06371/) (2022)
- [Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification](https://mlsanity.com/paper/2205.01920/) (2022)
- [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)
- [γ-Bridge: A Look-Parametric Diffusion Bridge](https://mlsanity.com/paper/2607.22719/) (2026)

## Abstract (arXiv)

Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Convolutional Neural Network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional layers along with batch normalization and rectified linear unit (ReLU) activation function and a component-wise division residual layer to estimate speckle and it is trained in an end-to-end fashion using a combination of Euclidean loss and Total Variation (TV) loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.