# An All-in-One Network for Dehazing and Beyond

arXiv: https://arxiv.org/abs/1707.06543 · HTML: https://mlsanity.com/paper/1707.06543/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 22 of 22)

- [From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data](https://mlsanity.com/paper/2108.02934/) (2021)
- [Lightweight Adaptive Feature De-drifting for Compressed Image Classification](https://mlsanity.com/paper/2401.01724/) (2024)
- [Towards Domain Invariant Single Image Dehazing](https://mlsanity.com/paper/2101.10449/) (2021)
- [Single UHD Image Dehazing via Interpretable Pyramid Network](https://mlsanity.com/paper/2202.08589/) (2022)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media](https://mlsanity.com/paper/2102.05847/) (2021)
- [Towards Efficient Single Image Dehazing and Desnowing](https://mlsanity.com/paper/2204.08899/) (2022)
- [Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN](https://mlsanity.com/paper/2204.10970/) (2022)
- [SimHaze: game engine simulated data for real-world dehazing](https://mlsanity.com/paper/2305.16481/) (2023)
- [TSNet:A Two-stage Network for Image Dehazing with Multi-scale Fusion and Adaptive Learning](https://mlsanity.com/paper/2404.02460/) (2024)
- [Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze](https://mlsanity.com/paper/2103.07065/) (2021)
- [A De-raining semantic segmentation network for real-time foreground segmentation](https://mlsanity.com/paper/2104.07877/) (2021)
- [SANDFORMER: CNN and Transformer under Gated Fusion for Sand Dust Image Restoration](https://mlsanity.com/paper/2303.04365/) (2023)
- [Nighttime Dehaze-Enhancement](https://mlsanity.com/paper/2210.09962/) (2022)
- [Visibility Enhancement for Low-light Hazy Scenarios](https://mlsanity.com/paper/2308.00591/) (2023)
- [A Machine Vision Method for Correction of Eccentric Error: Based on Adaptive Enhancement Algorithm](https://mlsanity.com/paper/2309.00514/) (2023)
- [Advancements and Trends in Ultra-High-Resolution Image Processing: An Overview](https://mlsanity.com/paper/2312.00250/) (2023)
- [Pseudo-Label Guided Real-World Image De-weathering: A Learning Framework with Imperfect Supervision](https://mlsanity.com/paper/2504.09949/) (2025)
- [CMAWRNet: Multiple Adverse Weather Removal via a Unified Quaternion Neural Architecture](https://mlsanity.com/paper/2505.01882/) (2025)
- [RealRep: Generalized SDR-to-HDR Conversion via Attribute-Disentangled Representation Learning](https://mlsanity.com/paper/2505.07322/) (2025)
- [Residual-based Efficient Bidirectional Diffusion Model for Image Dehazing and Haze Generation](https://mlsanity.com/paper/2508.11134/) (2025)
- [Tri-DehazeGS: Scene--Medium Decoupled Gaussian Splatting with Transmittance-Aware Optimization](https://mlsanity.com/paper/2609.11223/) (2026)

## Abstract (arXiv)

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN and training the joint pipeline from end to end, we witness a large improvement of the object detection performance on hazy images.