mlsanityData to 7 Oct 2026

Transformer-based SAR Image Despeckling

arXiv 2201.09355 ↗First posted 23 Jan 2022cs.CV eess.IVcited by 15

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Abstract (arXiv)

Synthetic Aperture Radar (SAR) images are usually degraded by a multiplicative noise known as speckle which makes processing and interpretation of SAR images difficult. In this paper, we introduce a transformer-based network for SAR image despeckling. The proposed despeckling network comprises of a transformer-based encoder which allows the network to learn global dependencies between different image regions - aiding in better despeckling. The network is trained end-to-end with synthetically generated speckled images using a composite loss function. Experiments show that the proposed method achieves significant improvements over traditional and convolutional neural network-based despeckling methods on both synthetic and real SAR images.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

10 of 16 references.

References · 10

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  3. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  4. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  5. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  6. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 864
  7. 2017SAR image despeckling through convolutional neural networkscited by 30
  8. 2017SAR Image Despeckling Using a Convolutional Neural Networkcited by 24
  9. 2020Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectivescited by 13
  10. 2019A New Ratio Image Based CNN Algorithm For SAR Despecklingcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 6; 2025: 2; 2026: 3.

15 papers.

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  1. 2023Large Selective Kernel Network for Remote Sensing Object Detectioncited by 98
  2. 2022Transformers in Remote Sensing: A Surveycited by 61
  3. 2022SAR Despeckling using a Denoising Diffusion Probabilistic Modelcited by 19
  4. 2024Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modelingcited by 4
  5. 2024Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentationcited by 2
  6. 2024A Tunable Despeckling Neural Network Stabilized via Diffusion Equationcited by 2
  7. 2023ReBotNet: Fast Real-time Video Enhancementcited by 1
  8. 2024PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networkscited by 1
  9. 2024Frontiers of Deep Learning: From Novel Application to Real-World Deploymentcited by 1
  10. 2024Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removalcited by 0
  11. 2025SAR-FAH: A Frequency-Adaptive Hybrid Network based on Neural ODEs for Structural-Preserving SAR Despecklingcited by 0
  12. 2025Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognitioncited by 0
3 more
  1. 2026A Deep Learning Iterative Framework for Sentinel-1 Stripmap Enhancement Based on Azimuth Doppler Decompositioncited by 0
  2. 2026γ-Bridge: A Look-Parametric Diffusion Bridgecited by 0
  3. 2026Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despecklingcited by 0