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

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  • 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
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    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).
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