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Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

arXiv 2004.13388 ↗First posted 28 Apr 2020cs.CVcited by 229

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Abstract (arXiv)

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. The proposed method is designed based on two principles, boosting and error feedback, and we show that they are suitable for the dehazing problem. By incorporating the Strengthen-Operate-Subtract boosting strategy in the decoder of the proposed model, we develop a simple yet effective boosted decoder to progressively restore the haze-free image. To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme. We show that the dense feature fusion module can simultaneously remedy the missing spatial information from high-resolution features and exploit the non-adjacent features. Extensive evaluations demonstrate that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.

Key facts. Cited by 229 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Apr 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 26; 2022: 36; 2023: 40; 2024: 59; 2025: 41; 2026: 27.

229 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. 2021TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditionscited by 243
  4. 2021Contrastive Learning for Compact Single Image Dehazingcited by 219
  5. 2024InstructIR: High-Quality Image Restoration Following Human Instructionscited by 139
  6. 2023PromptIR: Prompting for All-in-One Blind Image Restorationcited by 113
  7. 2024AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulationcited by 108
  8. 2024OneRestore: A Universal Restoration Framework for Composite Degradationcited by 91
  9. 2023DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attentioncited by 90
  10. 2021Image-Adaptive YOLO for Object Detection in Adverse Weather Conditionscited by 85
  11. 2023Curricular Contrastive Regularization for Physics-aware Single Image Dehazingcited by 83
  12. 2023MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazingcited by 81
38 more
  1. 2023RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priorscited by 73
  2. 2021From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Datacited by 68
  3. 2023GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditionscited by 62
  4. 2024FreqMamba: Viewing Mamba from a Frequency Perspective for Image Derainingcited by 57
  5. 2023WaveDM: Wavelet-Based Diffusion Models for Image Restorationcited by 46
  6. 2024MaIR: A Locality- and Continuity-Preserving Mamba for Image Restorationcited by 43
  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. 2024Correlation Matching Transformation Transformers for UHD Image Restorationcited by 39
  10. 2023Improving Image Restoration through Removing Degradations in Textual Representationscited by 36
  11. 2022UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learningcited by 35
  12. 2021UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Spacecited by 34
  13. 2024Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazingcited by 32
  14. 2021Perceiving and Modeling Density is All You Need for Image Dehazingcited by 30
  15. 2022Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removalcited by 28
  16. 2022Deep Fourier Up-Samplingcited by 27
  17. 2024Efficient Visual State Space Model for Image Deblurringcited by 27
  18. 2024Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Networkcited by 26
  19. 2023SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistencycited by 24
  20. 2022Neural Compression-Based Feature Learning for Video Restorationcited by 23
  21. 2025Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazingcited by 23
  22. 2023Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Priorcited by 22
  23. 2024NightHaze: Nighttime Image Dehazing via Self-Prior Learningcited by 22
  24. 2022Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learningcited by 21
  25. 2023Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagationcited by 21
  26. 2022GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditionscited by 20
  27. 2021A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learningcited by 19
  28. 2023MixDehazeNet : Mix Structure Block For Image Dehazing Networkcited by 18
  29. 2023Mutual Information-driven Triple Interaction Network for Efficient Image Dehazingcited by 18
  30. 2022Multi-Scale Adaptive Network for Single Image Denoisingcited by 17
  31. 2022Source-Free Domain Adaptation for Real-world Image Dehazingcited by 17
  32. 2022Restoring Vision in Hazy Weather with Hierarchical Contrastive Learningcited by 17
  33. 2023Frequency Compensated Diffusion Model for Real-scene Dehazingcited by 17
  34. 2025Iterative Predictor-Critic Code Decoding for Real-World Image Dehazingcited by 16
  35. 2021Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramidscited by 15
  36. 2023SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazingcited by 15
  37. 2021GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazingcited by 14
  38. 2021Visible Watermark Removal via Self-calibrated Localization and Background Refinementcited by 14

50 most cited of 229 All 229 (JSON)