# Haze Removal via Regional Saturation-Value Translation and Soft Segmentation

arXiv: https://arxiv.org/abs/2403.12054 · HTML: https://mlsanity.com/paper/2403.12054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 7 Jan 2024.

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

- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [DehazeNet: An End-to-End System for Single Image Haze Removal](https://mlsanity.com/paper/1601.07661/) (2016)
- [Gated Fusion Network for Single Image Dehazing](https://mlsanity.com/paper/1804.00213/) (2018)
- [Gated Context Aggregation Network for Image Dehazing and Deraining](https://mlsanity.com/paper/1811.08747/) (2018)
- [You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network](https://mlsanity.com/paper/2006.16829/) (2020)
- [Sky Optimization: Semantically aware image processing of skies in low-light photography](https://mlsanity.com/paper/2006.10172/) (2020)
- [A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing](https://mlsanity.com/paper/2202.04757/) (2022)
- [Toward Improving Robustness of Object Detectors Against Domain Shift](https://mlsanity.com/paper/2403.12049/) (2023)

## Abstract (arXiv)

This paper proposes a single image dehazing prior, called Regional Saturation-Value Translation (RSVT), to tackle the color distortion problems caused by conventional dehazing approaches in bright regions. The RSVT prior is developed based on two key observations regarding the relationship between hazy and haze-free points in the HSV color space. First, the hue component shows marginal variation between corresponding hazy and haze-free points, consolidating a hypothesis that the pixel value variability induced by haze primarily occurs in the saturation and value spaces. Second, in the 2D saturation-value coordinate system, most lines passing through hazy-clean point pairs are likely to intersect near the atmospheric light coordinates. Accordingly, haze removal for the bright regions can be performed by properly translating saturation-value coordinates. In addition, an effective soft segmentation method based on a morphological min-max channel is introduced. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is devised. Experimental results on various synthetic and realistic hazy image datasets demonstrate that the proposed scheme successfully addresses color distortion issues and restores visually appealing images. The code of this work is available at https://github.com/tranleanh/rsvt.