# A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing

arXiv: https://arxiv.org/abs/2202.04757 · HTML: https://mlsanity.com/paper/2202.04757/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https://mlsanity.com/paper/1711.07064/) (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)
- [Contrastive Learning for Compact Single Image Dehazing](https://mlsanity.com/paper/2104.09367/) (2021)
- [FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing](https://mlsanity.com/paper/2001.06968/) (2020)

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

- [Distilled Pooling Transformer Encoder for Efficient Realistic Image Dehazing](https://mlsanity.com/paper/2412.14220/) (2024)
- [Toward Improving Robustness of Object Detectors Against Domain Shift](https://mlsanity.com/paper/2403.12049/) (2023)
- [Feature Embedding Clustering using POCS-based Clustering Algorithm](https://mlsanity.com/paper/2305.00001/) (2023)
- [Haze Removal via Regional Saturation-Value Translation and Soft Segmentation](https://mlsanity.com/paper/2403.12054/) (2024)
- [Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance](https://mlsanity.com/paper/2507.13360/) (2025)
- [MetaFormer-driven Encoding Network for Robust Medical Semantic Segmentation](https://mlsanity.com/paper/2601.00922/) (2026)
- [Exploring SAM Supervision for Fine-Grained UAV Target Segmentation under Data Scarcity](https://mlsanity.com/paper/2607.03754/) (2026)

## Abstract (arXiv)

A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for single image dehazing scheme is proposed in this paper. The proposed EDN-GTM takes conventional RGB hazy image in conjunction with its transmission map estimated by adopting dark channel prior as the inputs of the network. The proposed EDN-GTM utilizes U-Net for image segmentation as the core network and utilizes various modifications including spatial pyramid pooling module and Swish activation to achieve state-of-the-art dehazing performance. Experiments on benchmark datasets show that the proposed EDN-GTM outperforms most of traditional and deep learning-based image dehazing schemes in terms of PSNR and SSIM metrics. The proposed EDN-GTM furthermore proves its applicability to object detection problems. Specifically, when applied to an image preprocessing tool for driving object detection, the proposed EDN-GTM can efficiently remove haze and significantly improve detection accuracy by 4.73% in terms of mAP measure. The code is available at: https://github.com/tranleanh/edn-gtm.