# Haze Visibility Enhancement: A Survey and Quantitative Benchmarking

arXiv: https://arxiv.org/abs/1607.06235 · HTML: https://mlsanity.com/paper/1607.06235/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2016.

## Built on by (most cited first, 17 of 17)

- [SeaThru-NeRF: Neural Radiance Fields in Scattering Media](https://mlsanity.com/paper/2304.07743/) (2023)
- [Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal](https://mlsanity.com/paper/2210.03061/) (2022)
- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing](https://mlsanity.com/paper/2106.02809/) (2021)
- [Dehazing-NeRF: Neural Radiance Fields from Hazy Images](https://mlsanity.com/paper/2304.11448/) (2023)
- [FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation](https://mlsanity.com/paper/2101.08465/) (2021)
- [Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing](https://mlsanity.com/paper/2108.11292/) (2021)
- [Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN](https://mlsanity.com/paper/2204.10970/) (2022)
- [Fourier-RWKV: A Multi-State Perception Network for Efficient Image Dehazing](https://mlsanity.com/paper/2512.08161/) (2025)
- [SANDFORMER: CNN and Transformer under Gated Fusion for Sand Dust Image Restoration](https://mlsanity.com/paper/2303.04365/) (2023)
- [ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions](https://mlsanity.com/paper/2506.15837/) (2025)
- [Hierarchical Semantic-Visual Fusion of Visible and Near-infrared Images for Long-range Haze Removal](https://mlsanity.com/paper/2507.03893/) (2025)
- [3D modelling of survey scene from images enhanced with a multi-exposure fusion](https://mlsanity.com/paper/2111.05541/) (2021)
- [Modeling Weather Uncertainty for Multi-weather Co-Presence Estimation](https://mlsanity.com/paper/2403.20092/) (2024)
- [Subjective and Objective Quality Assessment Methods of Stereoscopic Videos with Visibility Affecting Distortions](https://mlsanity.com/paper/2411.19522/) (2024)
- [TUGS: Physics-based Compact Representation of Underwater Scenes by Tensorized Gaussian](https://mlsanity.com/paper/2505.08811/) (2025)
- [OceanXL: Large-scale Underwater 3D Gaussian Splatting via Block Partitioning and Adaptive Pruning](https://mlsanity.com/paper/2609.29985/) (2026)

## 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.