{
  "abstract": "Restoring clear visibility from a single hazy image remains a challenging inverse problem when multiple atmospheric phenomena jointly affect image formation. In real environments, light propagation can be influenced not only by particle scattering but also by turbulence-induced blur and wavelength-dependent attenuation. Most existing dehazing approaches primarily address scattering, which limits their physical consistency and robustness in heavy haze or long-range scenes. In this work, we introduce a differentiable multi-physics learning framework that is designed to integrate scattering, turbulence, and spectral attenuation within a unified image formation model. The degradation process is expressed as a compact linear observation equation, where turbulence is represented through a spatial convolution operator and spectral effects are modeled via channel-dependent attenuation. Since the governing parameters cannot be directly inferred from a single observation, the formulated as an iterative refinement process guided by a learned approximation of the physical model. A lightweight neural estimator predicts the observation operators, while a learnable spectral module captures wavelength-dependent behavior. An iterative regularization network stabilizes the reconstruction process and prevents error propagation. Experiments conducted on the RESIDE-SOTS benchmark show competitive performance, achieving PSNR/SSIM values of 27.3/0.94 for indoor scenes and 30.2/0.94 for outdoor scenes with only a few optimization steps. Additional analyses confirm improved robustness under noise perturbations and severe illumination variations, highlighting the effectiveness of differentiable learning with physics-inspired constraints for real-world atmospheric image restoration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Mohammad Omar Ibrahim Alhawarat"
    },
    {
      "name": "Maged Faihan Alotaibi"
    },
    {
      "name": "Reem Aljuaidi"
    },
    {
      "name": "Bandar Saad Alshreef"
    },
    {
      "name": "E. M. Shaban"
    },
    {
      "name": "Wedad M. Alawad"
    },
    {
      "name": "Maha Abdallah Alnuwaiser"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dx8dewz0r91q8.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-75175-y",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-75175-y",
  "id": "dx8dewz0r91q8",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-75175-y",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1707|Image Enhancement Techniques"
    ]
  ],
  "openalex": "W7221766162",
  "pdf_url": "https://www.nature.com/articles/s41598-026-75175-y.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/dx8dewz0r91q8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:28Z",
      "native_id": "10.1038/s41598-026-75175-y",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-75175-y"
    },
    {
      "first_seen": "2026-10-30T13:45:54Z",
      "native_id": "e3d352fb83f24f228355d6e5f6f19646",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-75175-y"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "A differentiable multi-physics learning model for robust single-image dehazing under complex atmospheric conditions",
  "title_text": "A differentiable multi-physics learning model for robust single-image dehazing under complex atmospheric conditions",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:54Z",
  "url": "https://research.draconitic.com/paper/dx8dewz0r91q8",
  "venue": "Scientific Reports",
  "versions": []
}