IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v439y2023ics0096300322006919.html
   My bibliography  Save this article

Spatio-temporal regularized shock-diffusion filtering with local entropy for restoration of degraded document images

Author

Listed:
  • Wang, Yan
  • Zhou, Lingxin
  • Zhang, Xuyuan

Abstract

This paper proposes a novel model for restoration of document images with degraded background caused by show-through/smear or deteriorated documents. The model is formulated as a partial differential equation (PDE) that consists of spatio-temporal regularization of shock filtering and diffusion process, as well as local entropy. Shock filtering is employed to enhance the brightness contrast between texts and background according to image gradients adaptively. Diffusion process is not only responsible for removing selectively noise in the input image, but also suppressing the amplified noise by the shock filtering. Local entropy based source term serves as degradation-corrected term that is responsible for reducing the influence of degradation. A splitting-based algorithm is developed to solve the proposed model numerically, where two typical splitting methods and finite difference are combined. We test the proposed model and numerical scheme on DIBCO 2009–2014 and 2016 datasets. Experimental results indicate that the proposed method works well in restoring document images with degraded background, and achieves comparable performances compared to seven relevant methods for seven DIBCO datasets.

Suggested Citation

  • Wang, Yan & Zhou, Lingxin & Zhang, Xuyuan, 2023. "Spatio-temporal regularized shock-diffusion filtering with local entropy for restoration of degraded document images," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    • Handle: RePEc:eee:apmaco:v:439:y:2023:i:c:s0096300322006919
    DOI: 10.1016/j.amc.2022.127618
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322006919
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127618?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Jacobs, B.A. & Momoniat, E., 2015. "A locally adaptive, diffusion based text binarization technique," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 464-472.
    2. Feng, Shu, 2022. "Effective document image binarization via a convex variational level set model," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    3. Jacobs, B.A. & Celik, T., 2022. "Unsupervised document image binarization using a system of nonlinear partial differential equations," Applied Mathematics and Computation, Elsevier, vol. 418(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. Du, Zhongjie & He, Chuanjiang, 2023. "Anisotropic diffusion with fuzzy-based source for binarization of degraded document images," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    2. Meijun Zhou & Jiayu Qin & Zenan Huo & Fabio Giampaolo & Gang Mei, 2022. "epSFEM: A Julia-Based Software Package of Parallel Incremental Smoothed Finite Element Method (S-FEM) for Elastic-Plastic Problems," Mathematics, MDPI, vol. 10(12), pages 1-25, June.
    3. Jacobs, B.A. & Celik, T., 2022. "Unsupervised document image binarization using a system of nonlinear partial differential equations," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    4. Guo, Jiebin & He, Chuanjiang & Zhang, Xiaoting, 2019. "Nonlinear edge-preserving diffusion with adaptive source for document images binarization," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 8-22.
    5. Feng, Shu, 2022. "Effective document image binarization via a convex variational level set model," Applied Mathematics and Computation, Elsevier, vol. 419(C).

    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:apmaco:v:439:y:2023:i:c:s0096300322006919. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.