mlsanityData to 7 Oct 2026

Haze Visibility Enhancement: A Survey and Quantitative Benchmarking

arXiv 1607.06235 ↗First posted 21 Jul 2016cs.CVcited by 17

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Abstract (arXiv)

This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are grouped to multiple image methods, polarizing filters based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well known single-image methods, based on a recent dataset provided by Fattal and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jul 2016.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 3; 2024: 2; 2025: 4; 2026: 1.

17 papers.

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  1. 2023SeaThru-NeRF: Neural Radiance Fields in Scattering Mediacited by 75
  2. 2022Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removalcited by 28
  3. 2021A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learningcited by 19
  4. 2021T-Net: Deep Stacked Scale-Iteration Network for Image Dehazingcited by 8
  5. 2023Dehazing-NeRF: Neural Radiance Fields from Hazy Imagescited by 4
  6. 2021FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimationcited by 3
  7. 2021Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazingcited by 2
  8. 2022Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGANcited by 2
  9. 2023SANDFORMER: CNN and Transformer under Gated Fusion for Sand Dust Image Restorationcited by 1
  10. 2025ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditionscited by 1
  11. 2025Hierarchical Semantic-Visual Fusion of Visible and Near-infrared Images for Long-range Haze Removalcited by 1
  12. 2025Fourier-RWKV: A Multi-State Perception Network for Efficient Image Dehazingcited by 1
5 more
  1. 20213D modelling of survey scene from images enhanced with a multi-exposure fusioncited by 0
  2. 2024Modeling Weather Uncertainty for Multi-weather Co-Presence Estimationcited by 0
  3. 2024Subjective and Objective Quality Assessment Methods of Stereoscopic Videos with Visibility Affecting Distortionscited by 0
  4. 2025TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussiancited by 0
  5. 2026OceanXL: Large-scale Underwater 3D Gaussian Splatting via Block Partitioning and Adaptive Pruningcited by 0