# Joint Transmission Map Estimation and Dehazing using Deep Networks

arXiv: https://arxiv.org/abs/1708.00581 · HTML: https://mlsanity.com/paper/1708.00581/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 16 of 16)

- [TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions](https://mlsanity.com/paper/2111.14813/) (2021)
- [Restoring Images in Adverse Weather Conditions via Histogram Transformer](https://mlsanity.com/paper/2407.10172/) (2024)
- [SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency](https://mlsanity.com/paper/2303.07033/) (2023)
- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [AWRaCLe: All-Weather Image Restoration using Visual In-Context Learning](https://mlsanity.com/paper/2409.00263/) (2024)
- [RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.18047/) (2025)
- [GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing](https://mlsanity.com/paper/2103.13998/) (2021)
- [GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration](https://mlsanity.com/paper/2411.17687/) (2024)
- [Non-aligned supervision for Real Image Dehazing](https://mlsanity.com/paper/2303.04940/) (2023)
- [Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance](https://mlsanity.com/paper/2405.09996/) (2024)
- [Deep Variational Bayesian Modeling of Haze Degradation Process](https://mlsanity.com/paper/2412.03745/) (2024)
- [ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images](https://mlsanity.com/paper/2203.09275/) (2022)
- [AllWeatherNet:Unified Image Enhancement for Autonomous Driving under Adverse Weather and Lowlight-conditions](https://mlsanity.com/paper/2409.02045/) (2024)
- [Two-Step Image Dehazing with Intra-domain and Inter-domain Adaptation](https://mlsanity.com/paper/2102.03501/) (2021)
- [See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images](https://mlsanity.com/paper/2210.07594/) (2022)
- [Nighttime Dehaze-Enhancement](https://mlsanity.com/paper/2210.09962/) (2022)

## Abstract (arXiv)

Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved superior results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two-step procedure involving prior-based methods for estimating transmission map followed by calculation of dehazed image using the closed form solution. In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing. In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned directly from the loss function. Extensive experiments on synthetic and real datasets with challenging hazy images demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.