# A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer

arXiv: https://arxiv.org/abs/2304.07874 · HTML: https://mlsanity.com/paper/2304.07874/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 16 Apr 2023.

## Builds on (most cited first, 23 of 23)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Do Better ImageNet Models Transfer Better?](https://mlsanity.com/paper/1805.08974/) (2018)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [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)
- [Densely Connected Pyramid Dehazing Network](https://mlsanity.com/paper/1803.08396/) (2018)
- [Gated Fusion Network for Single Image Dehazing](https://mlsanity.com/paper/1804.00213/) (2018)
- [Domain Adaptation for Image Dehazing](https://mlsanity.com/paper/2005.04668/) (2020)
- [Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images](https://mlsanity.com/paper/1904.02904/) (2019)
- [O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images](https://mlsanity.com/paper/1804.05101/) (2018)
- [Gated Context Aggregation Network for Image Dehazing and Deraining](https://mlsanity.com/paper/1811.08747/) (2018)
- [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023)
- [DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing](https://mlsanity.com/paper/2104.08911/) (2021)
- [NTIRE 2020 Challenge on NonHomogeneous Dehazing](https://mlsanity.com/paper/2005.03457/) (2020)
- [A Data-Centric Approach for Training Deep Neural Networks with Less Data](https://mlsanity.com/paper/2110.03613/) (2021)
- [A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning](https://mlsanity.com/paper/2104.08902/) (2021)
- [CGGAN: A Context Guided Generative Adversarial Network For Single Image Dehazing](https://mlsanity.com/paper/2005.13884/) (2020)

## Built on by (most cited first, 9 of 9)

- [Restoring Images in Adverse Weather Conditions via Histogram Transformer](https://mlsanity.com/paper/2407.10172/) (2024)
- [NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2504.14582/) (2025)
- [Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation](https://mlsanity.com/paper/2404.19265/) (2024)
- [CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond](https://mlsanity.com/paper/2502.14493/) (2025)
- [Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond](https://mlsanity.com/paper/2407.14823/) (2024)
- [TSNet:A Two-stage Network for Image Dehazing with Multi-scale Fusion and Adaptive Learning](https://mlsanity.com/paper/2404.02460/) (2024)
- [Moir'eXNet: Adaptive Multi-Scale Demoir'eing with Linear Attention Test-Time Training and Truncated Flow Matching Prior](https://mlsanity.com/paper/2506.15929/) (2025)
- [Hierarchical Semantic-Visual Fusion of Visible and Near-infrared Images for Long-range Haze Removal](https://mlsanity.com/paper/2507.03893/) (2025)
- [Knowledge capture, adaptation and composition (KCAC): A framework for cross-task curriculum learning in robotic manipulation](https://mlsanity.com/paper/2505.10522/) (2025)

## Abstract (arXiv)

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenges. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation study to demonstrate the effectiveness of our proposed method.