# LED2Net: Deep Illumination-aware Dehazing with Low-light and Detail Enhancement

arXiv: https://arxiv.org/abs/1906.05119 · HTML: https://mlsanity.com/paper/1906.05119/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing](https://mlsanity.com/paper/2103.13998/) (2021)
- [Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration](https://mlsanity.com/paper/2504.09973/) (2025)
- [AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration](https://mlsanity.com/paper/2402.03738/) (2024)
- [Joint Correcting and Refinement for Balanced Low-Light Image Enhancement](https://mlsanity.com/paper/2309.16128/) (2023)

## Abstract (arXiv)

We present a novel dehazing and low-light enhancement method based on an illumination map that is accurately estimated by a convolutional neural network (CNN). In this paper, the illumination map is used as a component for three different tasks, namely, atmospheric light estimation, transmission map estimation, and low-light enhancement. To train CNNs for dehazing and low-light enhancement simultaneously based on the retinex theory, we synthesize numerous low-light and hazy images from normal hazy images from the FADE data set. In addition, we further improve the network using detail enhancement. Experimental results demonstrate that our method surpasses recent state-of-theart algorithms quantitatively and qualitatively. In particular, our haze-free images present vivid colors and enhance visibility without a halo effect or color distortion.