mlsanityData to 7 Oct 2026

Deep Learning Techniques for Atmospheric Turbulence Removal: A Review

arXiv 2409.14587 ↗First posted 3 Sep 2024cs.CV astro-ph.IMcited by 10

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Abstract (arXiv)

The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Restoring a scene distorted by atmospheric turbulence is also a challenging problem. The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 53 of its 144 references that are in the map. First posted on arXiv 3 Sep 2024.

Builds on

53 of 144 references.

References · 53

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  6. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  7. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  8. 2016Densely Connected Convolutional Networkscited by 6,691
  9. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  10. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  11. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  12. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
38 more
  1. 2017Deep Image Priorcited by 1,097
  2. 2021Multi-Stage Progressive Image Restorationcited by 813
  3. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  4. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  5. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 574
  6. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  7. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
  8. 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 492
  9. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  10. 2019Noise2Self: Blind Denoising by Self-Supervisioncited by 274
  11. 2020Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulationcited by 225
  12. 2019Deep Learning on Image Denoising: An overviewcited by 128
  13. 2022Parallel Diffusion Models of Operator and Image for Blind Inverse Problemscited by 113
  14. 2023Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understandingcited by 111
  15. 2020Blind Video Temporal Consistency via Deep Video Priorcited by 76
  16. 2020Artificial Intelligence in the Creative Industries: A Reviewcited by 61
  17. 2022Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Datasetcited by 61
  18. 2021Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transformcited by 50
  19. 2024nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Modelcited by 45
  20. 2022Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Modelcited by 37
  21. 2022AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Modelscited by 33
  22. 2022Imaging through the Atmosphere using Turbulence Mitigation Transformercited by 31
  23. 2020Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulencecited by 30
  24. 2020Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficientscited by 27
  25. 2023Physics-Driven Turbulence Image Restoration with Stochastic Refinementcited by 24
  26. 2022Learning to restore images degraded by atmospheric turbulence using uncertaintycited by 23
  27. 2018Subsampled Turbulence Removal Networkcited by 22
  28. 2024Spatio-Temporal Turbulence Mitigation: A Translational Perspectivecited by 22
  29. 2021LTT-GAN: Looking Through Turbulence by Inverting GANscited by 21
  30. 2022Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Networkcited by 21
  31. 2019ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulencecited by 18
  32. 2018Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusioncited by 17
  33. 2022Tilt-then-Blur or Blur-then-Tilt? Clarifying the Atmospheric Turbulence Modelcited by 17
  34. 2022Real-Time Dense Field Phase-to-Space Simulation of Imaging through Atmospheric Turbulencecited by 16
  35. 2019A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasetscited by 13
  36. 2019Atmospheric turbulence removal using convolutional neural networkcited by 12
  37. 2024Long-range Turbulence Mitigation: A Large-scale Dataset and A Coarse-to-fine Frameworkcited by 8
  38. 2023Atmospheric Turbulence Correction via Variational Deep Diffusioncited by 7

50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 3.

10 papers.

  1. 2025MAMAT: 3D Mamba-Based Atmospheric Turbulence Removal and its Object Detection Capabilitycited by 5
  2. 2025Advances in Artificial Intelligence: A Review for the Creative Industriescited by 2
  3. 2025DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detectioncited by 1
  4. 2025Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniquescited by 1
  5. 2025RMFAT: Recurrent Multi-scale Feature Atmospheric Turbulence Mitigatorcited by 1
  6. 2026Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesiscited by 1
  7. 2025JDATT: A Joint Distillation Framework for Atmospheric Turbulence Mitigation and Target Detectioncited by 0
  8. 2025Active Convolved Illumination with Deep Transfer Learning for Complex Beam Transmission through Atmospheric Turbulencecited by 0
  9. 2026High-Quality and Efficient Turbulence Mitigation with Eventscited by 0
  10. 2026ReMATF: Recurrent Motion-Adaptive Multi-scale Turbulence Mitigation for Dynamic Scenescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning Phase Distortion with Selective State Space Models for Video Turbulence Mitigation
  2. 2024Probabilistic Prior Driven Attention Mechanism Based on Diffusion Model for Imaging Through Atmospheric Turbulence
  3. 2023ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations
  4. 2025Astrophotography turbulence mitigation via generative models
  5. 2025HazeFlow: Revisit Haze Physical Model as ODE and Non-Homogeneous Haze Generation for Real-World Dehazing
  6. 2022Towards Efficient Single Image Dehazing and Desnowing
  7. 2022Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
  8. 2026AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models