# From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data

arXiv: https://arxiv.org/abs/2108.02934 · HTML: https://mlsanity.com/paper/2108.02934/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [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)
- [GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing](https://mlsanity.com/paper/1908.03245/) (2019)
- [ParseNet: Looking Wider to See Better](https://mlsanity.com/paper/1506.04579/) (2015)
- [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)
- [Exploring Disentangled Feature Representation Beyond Face Identification](https://mlsanity.com/paper/1804.03487/) (2018)
- [An All-in-One Network for Dehazing and Beyond](https://mlsanity.com/paper/1707.06543/) (2017)
- [Single Image Dehazing Using Ranking Convolutional Neural Network](https://mlsanity.com/paper/2001.05246/) (2020)
- [On the Duality Between Retinex and Image Dehazing](https://mlsanity.com/paper/1712.02754/) (2017)
- [A Cascaded Convolutional Neural Network for Single Image Dehazing](https://mlsanity.com/paper/1803.07955/) (2018)
- [Semantic Single-Image Dehazing](https://mlsanity.com/paper/1804.05624/) (2018)

## Built on by (most cited first, 30 of 68)

- [Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2308.09388/) (2023)
- [DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention](https://mlsanity.com/paper/2301.04805/) (2023)
- [Curricular Contrastive Regularization for Physics-aware Single Image Dehazing](https://mlsanity.com/paper/2303.14218/) (2023)
- [Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank](https://mlsanity.com/paper/2303.09101/) (2023)
- [I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing](https://mlsanity.com/paper/2408.14180/) (2024)
- [GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions](https://mlsanity.com/paper/2305.17863/) (2023)
- [MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restoration](https://mlsanity.com/paper/2501.04486/) (2025)
- [Perceiving and Modeling Density is All You Need for Image Dehazing](https://mlsanity.com/paper/2111.09733/) (2021)
- [SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images](https://mlsanity.com/paper/2302.08269/) (2023)
- [Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior](https://mlsanity.com/paper/2303.09757/) (2023)
- [Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning](https://mlsanity.com/paper/2203.07677/) (2022)
- [MixDehazeNet : Mix Structure Block For Image Dehazing Network](https://mlsanity.com/paper/2305.17654/) (2023)
- [Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing](https://mlsanity.com/paper/2308.06998/) (2023)
- [Source-Free Domain Adaptation for Real-world Image Dehazing](https://mlsanity.com/paper/2207.06644/) (2022)
- [Frequency Compensated Diffusion Model for Real-scene Dehazing](https://mlsanity.com/paper/2308.10510/) (2023)
- [Rethinking Performance Gains in Image Dehazing Networks](https://mlsanity.com/paper/2209.11448/) (2022)
- [Prompt-based test-time real image dehazing: a novel pipeline](https://mlsanity.com/paper/2309.17389/) (2023)
- [Semi-Supervised Video Desnowing Network via Temporal Decoupling Experts and Distribution-Driven Contrastive Regularization](https://mlsanity.com/paper/2410.07901/) (2024)
- [Semi-Supervised Teacher-Reference-Student Architecture for Action Quality Assessment](https://mlsanity.com/paper/2407.19675/) (2024)
- [Image Editing with Diffusion Models: A Survey](https://mlsanity.com/paper/2504.13226/) (2025)
- [Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration](https://mlsanity.com/paper/2210.01069/) (2022)
- [CoA: Towards Real Image Dehazing via Compression-and-Adaptation](https://mlsanity.com/paper/2504.05590/) (2025)
- [Bilevel Layer-Positioning LoRA for Real Image Dehazing](https://mlsanity.com/paper/2603.10872/) (2026)
- [Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework](https://mlsanity.com/paper/2302.11795/) (2023)
- [Deep Variational Bayesian Modeling of Haze Degradation Process](https://mlsanity.com/paper/2412.03745/) (2024)
- [Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle](https://mlsanity.com/paper/2203.09430/) (2022)
- [LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image Enhancement](https://mlsanity.com/paper/2408.16235/) (2024)
- [HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing](https://mlsanity.com/paper/2409.17432/) (2024)
- [3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results](https://mlsanity.com/paper/2501.10343/) (2025)
- [SmokeBench: A Real-World Dataset for Surveillance Image Desmoking in Early-Stage Fire Scenes](https://mlsanity.com/paper/2509.12701/) (2025)

## Abstract (arXiv)

Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing methods. To address this issue, we propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps, i.e. the latent haze-free image, the transmission map, and the global atmospheric light estimate, respecting the physical model of a haze process. Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network. Then a disentangled-consistency mean-teacher network (DMT-Net) is employed to collaborate unlabeled real data for boosting single image dehazing. Specifically, we encourage the coarse predictions and refinements of each disentangled component to be consistent between the student and teacher networks by using a consistency loss on unlabeled real data. We make comparison with 13 state-of-the-art dehazing methods on a new collected dataset (Haze4K) and two widely-used dehazing datasets (i.e., SOTS and HazeRD), as well as on real-world hazy images. Experimental results demonstrate that our method has obvious quantitative and qualitative improvements over the existing methods.