mlsanityData to 14 Oct 2026

SAR image despeckling through convolutional neural networks

arXiv 1704.00275 ↗First posted 2 Apr 2017cs.CVcited by 30

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 5; 2023: 0; 2024: 8; 2025: 4; 2026: 7.

30 papers.

Sort:

By area:

  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. 2024A Survey on Integrated Sensing, Communication, and Computationcited by 12
  5. 2024SAR Despeckling via Regional Denoising Diffusion Probabilistic Modelcited by 5
  6. 2021A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networkscited by 4
  7. 2025Exploring Scalable Unified Modeling for General Low-Level Visioncited by 4
  8. 2021Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learningcited by 3
  9. 2025SAR Strikes Back: A New Hope for RSVQAcited by 3
  10. 2021Homography augumented momentum constrastive learning for SAR image retrievalcited by 2
  11. 2022SAR Despeckling Using Overcomplete Convolutional Networkscited by 2
  12. 2024A Tunable Despeckling Neural Network Stabilized via Diffusion Equationcited by 2
18 more
  1. 2021NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Functioncited by 1
  2. 2022Multi-temporal speckle reduction with self-supervised deep neural networkscited by 1
  3. 2024PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networkscited by 1
  4. 2024Frontiers of Deep Learning: From Novel Application to Real-World Deploymentcited by 1
  5. 2025Bayesian Despeckling of Structured Sourcescited by 1
  6. 2026Self-Supervised Score-Based Despeckling for SAR Imagery via Log-Domain Transformationcited by 1
  7. 2021Deep Learning and Earth Observation to Support the Sustainable Development Goalscited by 0
  8. 2022Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD imagescited by 0
  9. 2024SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameterscited by 0
  10. 2024Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removalcited by 0
  11. 2024Multi-task SAR Image Processing via GAN-based Unsupervised Manipulationcited by 0
  12. 2025SAR-FAH: A Frequency-Adaptive Hybrid Network based on Neural ODEs for Structural-Preserving SAR Despecklingcited by 0
  13. 2026Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reductioncited by 0
  14. 2026Comparative Study of Weighted and Coupled Second- and Fourth-Order PDEs for Image Despeckling in Grayscale, Color, SAR, and Ultrasoundcited by 0
  15. 2026A Coupled Fourth Order Telegraph Diffusion Framework Using Grayscale Indicators for Image Despecklingcited by 0
  16. 2026Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalizationcited by 0
  17. 2026Minimax Theory of Likelihood-Based Deep Learning for Speckle Regressioncited by 0
  18. 2026γ-Bridge: A Look-Parametric Diffusion Bridgecited by 0