# DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing

arXiv: https://arxiv.org/abs/2104.08911 · HTML: https://mlsanity.com/paper/2104.08911/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2021.

## Built on by (most cited first, 30 of 38)

- [Vision Transformers for Single Image Dehazing](https://mlsanity.com/paper/2204.03883/) (2022)
- [Diffusion Probabilistic Model Made Slim](https://mlsanity.com/paper/2211.17106/) (2022)
- [Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank](https://mlsanity.com/paper/2303.09101/) (2023)
- [NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results](https://mlsanity.com/paper/2404.09790/) (2024)
- [Text2LiDAR: Text-guided LiDAR Point Cloud Generation via Equirectangular Transformer](https://mlsanity.com/paper/2407.19628/) (2024)
- [NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2404.14248/) (2024)
- [Multi-Agent Image Restoration](https://mlsanity.com/paper/2503.09403/) (2025)
- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing](https://mlsanity.com/paper/2304.08444/) (2023)
- [High-quality Image Dehazing with Diffusion Model](https://mlsanity.com/paper/2308.11949/) (2023)
- [Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution](https://mlsanity.com/paper/2404.11273/) (2024)
- [A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth](https://mlsanity.com/paper/2206.06427/) (2022)
- [A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer](https://mlsanity.com/paper/2304.07874/) (2023)
- [Towards Image Ambient Lighting Normalization](https://mlsanity.com/paper/2403.18730/) (2024)
- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing](https://mlsanity.com/paper/2109.07100/) (2021)
- [Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt](https://mlsanity.com/paper/2305.04430/) (2023)
- [Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform](https://mlsanity.com/paper/2110.11684/) (2021)
- [MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results](https://mlsanity.com/paper/2305.13770/) (2023)
- [CoA: Towards Real Image Dehazing via Compression-and-Adaptation](https://mlsanity.com/paper/2504.05590/) (2025)
- [AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations](https://mlsanity.com/paper/2411.10708/) (2024)
- [WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal](https://mlsanity.com/paper/2401.04550/) (2024)
- [After the Party: Navigating the Mapping From Color to Ambient Lighting](https://mlsanity.com/paper/2508.02168/) (2025)
- [Priors in Deep Image Restoration and Enhancement: A Survey](https://mlsanity.com/paper/2206.02070/) (2022)
- [Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization](https://mlsanity.com/paper/2405.14221/) (2024)
- [DiffLoss: unleashing diffusion model as constraint for training image restoration network](https://mlsanity.com/paper/2406.19030/) (2024)
- [DINOLight: Robust Ambient Light Normalization with Self-supervised Visual Prior Integration](https://mlsanity.com/paper/2603.12579/) (2026)
- [PANet: A Physics-guided Parametric Augmentation Net for Image Dehazing by Hazing](https://mlsanity.com/paper/2404.09269/) (2024)
- [FALCON: Frequency Adjoint Link with CONtinuous Density Mask for Fast Single Image Dehazing](https://mlsanity.com/paper/2407.00972/) (2024)
- [NTIRE 2025 Image Shadow Removal Challenge Report](https://mlsanity.com/paper/2506.15524/) (2025)

## Abstract (arXiv)

Hazy images are often subject to color distortion, blurring, and other visible quality degradation. Some existing CNN-based methods have great performance on removing homogeneous haze, but they are not robust in non-homogeneous case. The reasons are mainly in two folds. Firstly, due to the complicated haze distribution, texture details are easy to be lost during the dehazing process. Secondly, since the training pairs are hard to be collected, training on limited data can easily lead to over-fitting problem. To tackle these two issues, we introduce a novel dehazing network using 2D discrete wavelet transform, namely DW-GAN. Specifically, we propose a two-branch network to deal with the aforementioned problems. By utilizing wavelet transform in DWT branch, our proposed method can retain more high-frequency knowledge in feature maps. In order to prevent over-fitting, ImageNet pre-trained Res2Net is adopted in the knowledge adaptation branch. Owing to the robust feature representations of ImageNet pre-training, the generalization ability of our network is improved dramatically. Finally, a patch-based discriminator is used to reduce artifacts of the restored images. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively.