# Single image dehazing for a variety of haze scenarios using back projected pyramid network

arXiv: https://arxiv.org/abs/2008.06713 · HTML: https://mlsanity.com/paper/2008.06713/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification](https://mlsanity.com/paper/2305.13659/) (2023)
- [WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal](https://mlsanity.com/paper/2401.04550/) (2024)
- [LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility](https://mlsanity.com/paper/2301.05434/) (2023)
- [Latent Graph Attention for Enhanced Spatial Context](https://mlsanity.com/paper/2307.04149/) (2023)
- [Nighttime Dehaze-Enhancement](https://mlsanity.com/paper/2210.09962/) (2022)
- [DedustNet: A Frequency-dominated Swin Transformer-based Wavelet Network for Agricultural Dust Removal](https://mlsanity.com/paper/2401.04750/) (2024)

## Abstract (arXiv)

Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks together for the generator and are amenable to learning through back projection. We have shown that our network can be trained without over-fitting using as few as 20 image pairs of hazy and non-hazy images. We report the state of the art performances on NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE 2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset.