mlsanityData to 7 Oct 2026

FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

arXiv 1911.07559 ↗First posted 18 Nov 2019cs.CVcited by 307

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Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers. The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23db to 36.39db on the SOTS indoor test dataset. Code has been made available at GitHub.

Key facts. Cited by 307 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Nov 2019.

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Citing papers per year: 2021: 21; 2022: 41; 2023: 57; 2024: 76; 2025: 67; 2026: 45.

307 papers.

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  1. 2022MAXIM: Multi-Axis MLP for Image Processingcited by 301
  2. 2022Vision Transformers for Single Image Dehazingcited by 255
  3. 2021Contrastive Learning for Compact Single Image Dehazingcited by 219
  4. 2023Controlling Vision-Language Models for Multi-Task Image Restorationcited by 108
  5. 2024AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulationcited by 108
  6. 2023AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusioncited by 98
  7. 2024OneRestore: A Universal Restoration Framework for Composite Degradationcited by 91
  8. 2023DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attentioncited by 90
  9. 2021Image-Adaptive YOLO for Object Detection in Adverse Weather Conditionscited by 85
  10. 2023Curricular Contrastive Regularization for Physics-aware Single Image Dehazingcited by 83
  11. 2023MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazingcited by 81
  12. 2023RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priorscited by 73
38 more
  1. 2021From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Datacited by 68
  2. 2023GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditionscited by 62
  3. 2021Improving Image Restoration by Revisiting Global Information Aggregationcited by 60
  4. 2023WaveDM: Wavelet-Based Diffusion Models for Image Restorationcited by 46
  5. 2024MaIR: A Locality- and Continuity-Preserving Mamba for Image Restorationcited by 43
  6. 2024RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Modelscited by 41
  7. 2024U-shaped Vision Mamba for Single Image Dehazingcited by 40
  8. 2023Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolutioncited by 39
  9. 2025Degradation-Aware Feature Perturbation for All-in-One Image Restorationcited by 39
  10. 2023Language-driven All-in-one Adverse Weather Removalcited by 36
  11. 2023Improving Image Restoration through Removing Degradations in Textual Representationscited by 36
  12. 2025MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restorationcited by 36
  13. 2022UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learningcited by 35
  14. 20224D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancementcited by 34
  15. 2021Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separationcited by 32
  16. 2022SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perceptioncited by 32
  17. 2024Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazingcited by 32
  18. 2021Perceiving and Modeling Density is All You Need for Image Dehazingcited by 30
  19. 2022Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removalcited by 28
  20. 2024Efficient Visual State Space Model for Image Deblurringcited by 27
  21. 2024Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Networkcited by 26
  22. 2022Neural Compression-Based Feature Learning for Video Restorationcited by 23
  23. 2025Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazingcited by 23
  24. 2023Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Priorcited by 22
  25. 2023Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancementcited by 22
  26. 2023Prompt-based Ingredient-Oriented All-in-One Image Restorationcited by 22
  27. 2024NightHaze: Nighttime Image Dehazing via Self-Prior Learningcited by 22
  28. 2022Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learningcited by 21
  29. 2024ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformercited by 20
  30. 2021A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learningcited by 19
  31. 2023NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformercited by 19
  32. 2023MixDehazeNet : Mix Structure Block For Image Dehazing Networkcited by 18
  33. 2023Mutual Information-driven Triple Interaction Network for Efficient Image Dehazingcited by 18
  34. 2024Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restorationcited by 18
  35. 2021Towards Domain Invariant Single Image Dehazingcited by 17
  36. 2022Source-Free Domain Adaptation for Real-world Image Dehazingcited by 17
  37. 2022Restoring Vision in Hazy Weather with Hierarchical Contrastive Learningcited by 17
  38. 2023Frequency Compensated Diffusion Model for Real-scene Dehazingcited by 17

50 most cited of 307 All 307 (JSON)