IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i18p3834-d1234835.html
   My bibliography  Save this article

Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model

Author

Listed:
  • Chenping Zhao

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
    School of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, China)

  • Wenlong Yue

    (School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Jianlou Xu

    (School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China)

  • Huazhu Chen

    (School of Mathematics and Information Sciences, Zhongyuan University of Technology, Zhengzhou 451191, China)

Abstract

It is well known that images taken in low-light conditions frequently suffer from unknown noise and low visibility, which can pose challenges for image enhancement. The majority of Retinex-based decomposition algorithms usually attempt to directly design prior regularization for illumination or reflectance. Nevertheless, noise can be involved in such schemes. To address these issues, a new Retinex-based decomposition model for simultaneous enhancement and denoising has been developed. In this paper, an extended decomposition scheme is introduced to extract the illumination and reflectance components, which helps to better describe the prior information on illumination and reflectance. Subsequently, spatially adaptive weights are designed for two regularization terms. The main motivation is to provide a small amount of smoothing in near edges or bright areas and stronger smoothing in dark areas, which could preserve useful information and remove noise effectively during image-enhancement processing. Finally, the proposed algorithm is validated on several common datasets: LIME, LOL, and NPE. Extensive experiments show that the presented method is superior to state-of-the-art methods both in objective index comparisons and visual quality.

Suggested Citation

  • Chenping Zhao & Wenlong Yue & Jianlou Xu & Huazhu Chen, 2023. "Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model," Mathematics, MDPI, vol. 11(18), pages 1-14, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3834-:d:1234835
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/18/3834/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/18/3834/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hafiz Syed Muhammad Muslim & Sajid Ali Khan & Shariq Hussain & Arif Jamal & Hafiz Syed Ahmed Qasim, 2019. "A knowledge-based image enhancement and denoising approach," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 108-121, June.
    2. Lucero Verónica Lozano-Vázquez & Jun Miura & Alberto Jorge Rosales-Silva & Alberto Luviano-Juárez & Dante Mújica-Vargas, 2022. "Analysis of Different Image Enhancement and Feature Extraction Methods," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Chondro & Qazi Mazhar ul Haq & Shanq-Jang Ruan & Lieber Po-Hung Li, 2020. "Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs," Mathematics, MDPI, vol. 8(5), pages 1-15, May.
    2. Hancheng Zhu & Yong Zhou & Zhiwen Shao & Wenliang Du & Guangcheng Wang & Qiaoyue Li, 2022. "Personalized Image Aesthetics Assessment via Multi-Attribute Interactive Reasoning," Mathematics, MDPI, vol. 10(22), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3834-:d:1234835. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.