{
  "abstract": "Low-light image enhancement is an important problem in image processing. In this paper, we propose a novel adaptive variational model based on fractional-order gradients for low-light image enhancement. Specifically, the low-light part of the low-light image is enhanced, that is, the contrast of the low-light image is enhanced. The core concept of the proposed technique involves partitioning the image domain into low-light and brighter regions by applying an optimal threshold value. Then, a fitting term is further used for the low-light parts, so that the pixels in the low-light area can be enhanced by adjusting the contrast and brightness parameters. The existence of a minimizer is established under the stated assumptions, and the uniqueness of the solution is proved under the corresponding convexity conditions. An efficient split Bregman method is developed to solve the proposed variational model. Experimental results demonstrate the effectiveness of the proposed method for low-light image enhancement.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mathematics and Information Sciences, Nanchang Hangkong University, Nanchang 330063, China",
    "School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510002, China",
    "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Bao Chen",
      "orcid": "0000-0002-9342-4616"
    },
    {
      "affs": [
        0
      ],
      "name": "Tianlan Xiong"
    },
    {
      "affs": [
        1
      ],
      "name": "Juanjuan Gao"
    },
    {
      "affs": [
        2
      ],
      "name": "Yuchao Tang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7y8gpyb6h6a3e.bib",
  "cited_by": 0,
  "doi": "10.3390/fractalfract10110755",
  "fulltext_url": null,
  "id": "7y8gpyb6h6a3e",
  "issns": [
    "2504-3110"
  ],
  "landing_url": "https://www.mdpi.com/2504-3110/10/11/755",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Image Enhancement Techniques"
    ]
  ],
  "openalex": "W7224106860",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7y8gpyb6h6a3e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:32Z",
      "native_id": "10.3390/fractalfract10110755",
      "source": "crossref",
      "url": "https://www.mdpi.com/2504-3110/10/11/755"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2504-3110",
      "subfield": "analysis-pdes"
    }
  ],
  "title": "Fractional-Order Dynamic Illumination Adjustment for Low-Light Enhancement Using Luminance Segmentation",
  "title_text": "Fractional-Order Dynamic Illumination Adjustment for Low-Light Enhancement Using Luminance Segmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:32Z",
  "url": "https://research.draconitic.com/paper/7y8gpyb6h6a3e",
  "venue": "Fractal and Fractional",
  "versions": []
}