# On the Duality Between Retinex and Image Dehazing

arXiv: https://arxiv.org/abs/1712.02754 · HTML: https://mlsanity.com/paper/1712.02754/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2017.

## Built on by (most cited first, 10 of 10)

- [From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data](https://mlsanity.com/paper/2108.02934/) (2021)
- [A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning](https://mlsanity.com/paper/2106.03323/) (2021)
- [Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond](https://mlsanity.com/paper/2212.10772/) (2022)
- [Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding](https://mlsanity.com/paper/2306.05675/) (2023)
- [Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN](https://mlsanity.com/paper/2204.10970/) (2022)
- [Advanced Underwater Image Quality Enhancement via Hybrid Super-Resolution Convolutional Neural Networks and Multi-Scale Retinex-Based Defogging Techniques](https://mlsanity.com/paper/2410.14285/) (2024)
- [Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement](https://mlsanity.com/paper/2504.07810/) (2025)
- [The Topology-Overlap Trade-Off in Retinal Arteriole-Venule Segmentation](https://mlsanity.com/paper/2303.18022/) (2023)
- [SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing Image Dehazing Using Satellite-to-Ground NDVI Knowledge](https://mlsanity.com/paper/2306.06288/) (2023)
- [Variational Deep Unfolding with Mamba-Based Nonlocal Modeling for Underwater Image Enhancement](https://mlsanity.com/paper/2606.14781/) (2026)

## Abstract (arXiv)

Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.