# A PDE-Based Image Dehazing Method via Atmospheric Scattering Theory

arXiv: https://arxiv.org/abs/2506.08793 · HTML: https://mlsanity.com/paper/2506.08793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing](https://mlsanity.com/paper/1908.03245/) (2019)
- [Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network](https://mlsanity.com/paper/2406.07966/) (2024)
- [Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing](https://mlsanity.com/paper/2503.19262/) (2025)
- [Dropout the High-rate Downsampling: A Novel Design Paradigm for UHD Image Restoration](https://mlsanity.com/paper/2411.06456/) (2024)
- [MixNet: Efficient Global Modeling for Ultra-High-Definition Image Restoration](https://mlsanity.com/paper/2401.10666/) (2024)

## Abstract (arXiv)

This paper introduces a novel partial differential equation (PDE) framework for single-image dehazing. We embed the atmospheric scattering model into a PDE featuring edge-preserving diffusion and a nonlocal operator to maintain both local details and global structures. A key innovation is an adaptive regularization mechanism guided by the dark channel prior, which adjusts smoothing strength based on haze density. The framework's mathematical well-posedness is rigorously established by proving the existence and uniqueness of its weak solution in H01(Omega). An efficient, GPU-accelerated fixed-point solver is used for implementation. Experiments confirm our method achieves effective haze removal while preserving high image fidelity, offering a principled alternative to purely data-driven techniques.