# End-to-End United Video Dehazing and Detection

arXiv: https://arxiv.org/abs/1709.03919 · HTML: https://mlsanity.com/paper/1709.03919/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

## Built on by (most cited first, 24 of 24)

- [Contrastive Learning for Compact Single Image Dehazing](https://mlsanity.com/paper/2104.09367/) (2021)
- [Learning to Resize Images for Computer Vision Tasks](https://mlsanity.com/paper/2103.09950/) (2021)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior](https://mlsanity.com/paper/2303.09757/) (2023)
- [Self-Supervised Video Desmoking for Laparoscopic Surgery](https://mlsanity.com/paper/2403.11192/) (2024)
- [A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth](https://mlsanity.com/paper/2206.06427/) (2022)
- [Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video](https://mlsanity.com/paper/2412.11395/) (2024)
- [Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance](https://mlsanity.com/paper/2405.09996/) (2024)
- [Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning](https://mlsanity.com/paper/2104.01888/) (2021)
- [Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction](https://mlsanity.com/paper/2203.15325/) (2022)
- [Reliable Image Dehazing by NeRF](https://mlsanity.com/paper/2303.09153/) (2023)
- [MULLER: Multilayer Laplacian Resizer for Vision](https://mlsanity.com/paper/2304.02859/) (2023)
- [Progressive Deep Video Dehazing without Explicit Alignment Estimation](https://mlsanity.com/paper/2107.07837/) (2021)
- [RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal](https://mlsanity.com/paper/2306.07244/) (2023)
- [From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance](https://mlsanity.com/paper/2510.03906/) (2025)
- [DiffLoss: unleashing diffusion model as constraint for training image restoration network](https://mlsanity.com/paper/2406.19030/) (2024)
- [LMHaze: Intensity-aware Image Dehazing with a Large-scale Multi-intensity Real Haze Dataset](https://mlsanity.com/paper/2410.16095/) (2024)
- [LiBrA-Net: Lie-Algebraic Bilateral Affine Fields for Real-Time 4K Video Dehazing](https://mlsanity.com/paper/2605.11508/) (2026)
- [Haze-Aware Attention Network for Single-Image Dehazing](https://mlsanity.com/paper/2407.11505/) (2024)
- [ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions](https://mlsanity.com/paper/2506.15837/) (2025)
- [Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation](https://mlsanity.com/paper/2510.09228/) (2025)
- [A GAN-Based Input-Size Flexibility Model for Single Image Dehazing](https://mlsanity.com/paper/2102.09796/) (2021)
- [Parallel Cross Strip Attention Network for Single Image Dehazing](https://mlsanity.com/paper/2405.05811/) (2024)
- [Bridging the Gap Between Image Restoration and Navigational Safety in Hazy Conditions: A New Visibility Estimation Metric for Maritime Surveillance](https://mlsanity.com/paper/2606.30049/) (2026)

## Abstract (arXiv)

The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video.