mlsanityData to 7 Oct 2026

SAR Image Despeckling Using a Convolutional Neural Network

arXiv 1706.00552 ↗First posted 2 Jun 2017cs.CVcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2017.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 0; 2024: 6; 2025: 2; 2026: 5.

24 papers.

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  1. 2022SAR Despeckling using a Denoising Diffusion Probabilistic Modelcited by 19
  2. 2021As if by magic: self-supervised training of deep despeckling networks with MERLINcited by 18
  3. 2022Transformer-based SAR Image Despecklingcited by 15
  4. 2021A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networkscited by 4
  5. 2021Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learningcited by 3
  6. 2022Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?cited by 3
  7. 2022SAR Despeckling Using Overcomplete Convolutional Networkscited by 2
  8. 2024A Tunable Despeckling Neural Network Stabilized via Diffusion Equationcited by 2
  9. 2021NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Functioncited by 1
  10. 2024PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networkscited by 1
  11. 2024Frontiers of Deep Learning: From Novel Application to Real-World Deploymentcited by 1
  12. 2024Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1cited by 1
12 more
  1. 2025Bayesian Despeckling of Structured Sourcescited by 1
  2. 2021Deep Learning and Earth Observation to Support the Sustainable Development Goalscited by 0
  3. 2022Deep learning based automatic detection of offshore oil slicks using SAR data and contextual informationcited by 0
  4. 2022Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classificationcited by 0
  5. 2024Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removalcited by 0
  6. 2024Multi-task SAR Image Processing via GAN-based Unsupervised Manipulationcited by 0
  7. 2025SAR-FAH: A Frequency-Adaptive Hybrid Network based on Neural ODEs for Structural-Preserving SAR Despecklingcited by 0
  8. 2026Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reductioncited by 0
  9. 2026Comparative Study of Weighted and Coupled Second- and Fourth-Order PDEs for Image Despeckling in Grayscale, Color, SAR, and Ultrasoundcited by 0
  10. 2026A Coupled Fourth Order Telegraph Diffusion Framework Using Grayscale Indicators for Image Despecklingcited by 0
  11. 2026Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalizationcited by 0
  12. 2026γ-Bridge: A Look-Parametric Diffusion Bridgecited by 0