# Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

arXiv: https://arxiv.org/abs/2005.05999 · HTML: https://mlsanity.com/paper/2005.05999/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

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

- [Degradation-Aware Feature Perturbation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.12630/) (2025)
- [Perceiving and Modeling Density is All You Need for Image Dehazing](https://mlsanity.com/paper/2111.09733/) (2021)
- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image](https://mlsanity.com/paper/2105.07174/) (2021)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images](https://mlsanity.com/paper/2303.15848/) (2023)
- [WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal](https://mlsanity.com/paper/2401.04550/) (2024)
- [MSR-Net: Multi-Scale Relighting Network for One-to-One Relighting](https://mlsanity.com/paper/2107.06125/) (2021)
- [Designing An Illumination-Aware Network for Deep Image Relighting](https://mlsanity.com/paper/2207.10582/) (2022)
- [LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility](https://mlsanity.com/paper/2301.05434/) (2023)
- [Efficient Re-parameterization Residual Attention Network For Nonhomogeneous Image Dehazing](https://mlsanity.com/paper/2109.05479/) (2021)
- [DedustNet: A Frequency-dominated Swin Transformer-based Wavelet Network for Agricultural Dust Removal](https://mlsanity.com/paper/2401.04750/) (2024)
- [AdaMixT: Adaptive Weighted Mixture of Multi-Scale Expert Transformers for Time Series Forecasting](https://mlsanity.com/paper/2509.18107/) (2025)
- [Multi-Branch Non-Homogeneous Image Dehazing via Concentration Partitioning and Image Fusion](https://mlsanity.com/paper/2605.00885/) (2026)
- [Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance](https://mlsanity.com/paper/2608.09482/) (2026)

## Abstract (arXiv)

Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.