---
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标题: "HNDiff: Haze-Noise Diffusion for Image Dehazing"
原文链接: "https://arxiv.org/abs/2608.10995"
发布日期: "2026-08-11"
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---

## Computer Science > Computer Vision and Pattern Recognition

## Title:HNDiff: Haze-Noise Diffusion for Image Dehazing

Authors:[Jin-Ting He](https://arxiv.org/search/cs?searchtype=author&query=He,+J), [Fu-Jen Tsai](https://arxiv.org/search/cs?searchtype=author&query=Tsai,+F), [Yan-Tsung Peng](https://arxiv.org/search/cs?searchtype=author&query=Peng,+Y), [Min-Hung Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+M), [Chia-Wen Lin](https://arxiv.org/search/cs?searchtype=author&query=Lin,+C), [Yen-Yu Lin](https://arxiv.org/search/cs?searchtype=author&query=Lin,+Y)

[View PDF](https://arxiv.org/pdf/2608.10995) [HTML (experimental)](https://arxiv.org/html/2608.10995v1)

> Abstract:Existing diffusion-based methods have recently made significant progress in image dehazing. However, they typically neglect the physics of haze formation and reconstruct clean images from pure Gaussian noise, thereby limiting their restoration potential. To address this issue, we propose Haze-Noise Diffusion (HNDiff), a novel diffusion framework that embeds the atmospheric scattering model as an inductive bias. By grounding diffusion in physical principles, HNDiff ensures that the restoration aligns more closely with underlying mechanisms of haze formation. In its forward process, we introduce joint haze-noise diffusion with a haze-aware noise scheduler, which progressively adds both haze and noise to an image. Essentially, the scheduler adapts noise levels according to haze density, meaning that regions with heavier haze receive stronger noise injection to encourage content generation, while clearer regions receive lighter noise to better preserve details, which directly links the forward degradation process with the physics of haze. In the reverse process, we then derive a physically consistent dehazing-denoising process that simultaneously removes haze and noise to restore a clean image in a manner aligned with the forward degradation process. To further enhance practicality, we propose Latent HNDiff, which compiles clean latent priors that can be seamlessly integrated into existing dehazing networks to boost performance. Extensive experiments show that our work significantly improves leading dehazing backbones and achieves state-of-the-art results on benchmark datasets. The project page is available at [this https URL](https://jin-ting-he.github.io/HNDiff).

| Comments: |  |
| --- | --- |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | [arXiv:2608.10995](https://arxiv.org/abs/2608.10995) \[cs.CV\] |
|  | (or [arXiv:2608.10995v1](https://arxiv.org/abs/2608.10995v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10995](https://doi.org/10.48550/arXiv.2608.10995) |

## Submission history

From: Jin-Ting He \[[view email](https://arxiv.org/show-email/f1f02a08/2608.10995)\]  
**\[v1\]** Tue, 11 Aug 2026 14:47:38 UTC (4,467 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10995) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
