IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v237y2025icp1-17.html
   My bibliography  Save this article

A novel time-fractional decomposition model for image denoising integrating Caputo derivative

Author

Listed:
  • Zaabouli, Z.
  • Afraites, L.
  • Laghrib, A.

Abstract

In this work, we tackle the persistent problem of image restoration by developing a novel model that integrates a Caputo time fractional derivative into a reaction–diffusion framework. This approach exploits the memory effect of fractional derivatives for better diffusion control. With a thorough analysis employing the H-1 norm decomposition strategy and the Weickert filter, our model excels in noise reduction and image quality preservation. The task of establishing solution existence and uniqueness was managed using the fixed point method. The results reveal substantial improvements in denoising performance, highlighting the model’s potential.

Suggested Citation

  • Zaabouli, Z. & Afraites, L. & Laghrib, A., 2025. "A novel time-fractional decomposition model for image denoising integrating Caputo derivative," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 237(C), pages 1-17.
    • Handle: RePEc:eee:matcom:v:237:y:2025:i:c:p:1-17
    DOI: 10.1016/j.matcom.2025.04.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475425001442
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2025.04.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Qi & Ma, Jing & Yu, Siyuan & Tan, Liying, 2020. "Noise detection and image denoising based on fractional calculus," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    2. Al-Barakati, Abdullah A. & Mesdoui, Fatiha & Bekiros, Stelios & Kaçar, Sezgin & Jahanshahi, Hadi, 2024. "A variable-order fractional memristor neural network: Secure image encryption and synchronization via a smooth and robust control approach," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    3. Liu, Kai & Tian, Yanzhao, 2020. "Research and analysis of deep learning image enhancement algorithm based on fractional differential," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    4. Zeng, Biao & Wang, Shuhua, 2024. "Existence for fractional evolutionary inclusions involving nonlinear weakly continuous operators with applications," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    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. Peng, Qiu & Jian, Jigui, 2023. "Asymptotic synchronization of second-fractional -order fuzzy neural networks with impulsive effects," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Ali, Muhammad Aown & Chaudhary, Naveed Ishtiaq & Khan, Taimoor Ali & Mao, Wei-Lung & Lin, Chien-Chou & Raja, Muhammad Asif Zahoor, 2024. "Design of key term separated identification model for fractional input nonlinear output error systems: Auxiliary model based Runge Kutta optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    3. Poonam Pawar & Bharati Ainapure & Mamoon Rashid & Nazir Ahmad & Aziz Alotaibi & Sultan S. Alshamrani, 2022. "Deep Learning Approach for the Detection of Noise Type in Ancient Images," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    4. Wu, Kai & Tang, Ming & Ren, Han & Zhao, Liang, 2023. "Quantized pinning bipartite synchronization of fractional-order coupled reaction–diffusion neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:matcom:v:237:y:2025:i:c:p:1-17. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.