# Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids

arXiv: https://arxiv.org/abs/2111.05700 · HTML: https://mlsanity.com/paper/2111.05700/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [DehazeNet: An End-to-End System for Single Image Haze Removal](https://mlsanity.com/paper/1601.07661/) (2016)
- [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [Unsupervised Single Image Dehazing Using Dark Channel Prior Loss](https://mlsanity.com/paper/1812.07051/) (2018)
- [Rank-One Prior: Toward Real-Time Scene Recovery](https://mlsanity.com/paper/2103.17126/) (2021)

## Built on by (most cited first, 15 of 15)

- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Dual-Scale Single Image Dehazing Via Neural Augmentation](https://mlsanity.com/paper/2209.05913/) (2022)
- [Linear Video Transformer with Feature Fixation](https://mlsanity.com/paper/2210.08164/) (2022)
- [HazeCLIP: Towards Language Guided Real-World Image Dehazing](https://mlsanity.com/paper/2407.13719/) (2024)
- [Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions](https://mlsanity.com/paper/2409.01500/) (2024)
- [Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens Flare Removal](https://mlsanity.com/paper/2502.10714/) (2025)
- [Model-Based Single Image Deep Dehazing](https://mlsanity.com/paper/2111.10943/) (2021)
- [HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution](https://mlsanity.com/paper/2112.14608/) (2021)
- [UR2P-Dehaze: Learning a Simple Image Dehaze Enhancer via Unpaired Rich Physical Prior](https://mlsanity.com/paper/2501.06818/) (2025)
- [MvKSR: Multi-view Knowledge-guided Scene Recovery for Hazy and Rainy Degradation](https://mlsanity.com/paper/2401.03800/) (2024)
- [End-to-End Optimized Image Compression with the Frequency-Oriented Transform](https://mlsanity.com/paper/2401.08194/) (2024)
- [Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement](https://mlsanity.com/paper/2409.01641/) (2024)
- [Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network](https://mlsanity.com/paper/2209.07808/) (2022)
- [Gaussian highpass guided image filtering](https://mlsanity.com/paper/2503.03284/) (2025)
- [Excavate the potential of Single-Scale Features: A Decomposition Network for Water-Related Optical Image Enhancement](https://mlsanity.com/paper/2508.04123/) (2025)

## Abstract (arXiv)

Model driven single image dehazing was widely studied on top of different priors due to its extensive applications. Ambiguity between object radiance and haze and noise amplification in sky regions are two inherent problems of model driven single image dehazing. In this paper, a dark direct attenuation prior (DDAP) is proposed to address the former problem. A novel haze line averaging is proposed to reduce the morphological artifacts caused by the DDAP which enables a weighted guided image filter with a smaller radius to further reduce the morphological artifacts while preserve the fine structure in the image. A multi-scale dehazing algorithm is then proposed to address the latter problem by adopting Laplacian and Guassian pyramids to decompose the hazy image into different levels and applying different haze removal and noise reduction approaches to restore the scene radiance at different levels of the pyramid. The resultant pyramid is collapsed to restore a haze-free image. Experiment results demonstrate that the proposed algorithm outperforms state of the art dehazing algorithms and the noise is indeed prevented from being amplified in the sky region.