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A Survey on Nonlinear Second-Order Diffusion-Based Techniques for Additive Denoising

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Soft Computing Applications (SOFA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1221))

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Abstract

An overview of additive noise reduction algorithms using nonlinear second-order partial differential equations (PDEs) is provided here. First, the state-of-the-art anisotropic diffusion models that are used for image restoration are described here. The second-order PDE-based denoising approaches using variational schemes are addressed next. Our most important contributions in these image processing fields are also discussed in this work and method comparison examples are provided, too.

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Acknowledgement

This research presented here was supported from the project PN-III-P4-ID-PCE-2016-0011, which is financed by UEFISCDI Romania.

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Authors and Affiliations

  1. Institute of Computer Science of the Romanian Academy, Iași, Romania

    Tudor Barbu

Authors
  1. Tudor Barbu
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Correspondence to Tudor Barbu .

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Editors and Affiliations

  1. “Aurel Vlaicu” University of Arad, Faculty of Engineering, Arad, Romania

    Valentina Emilia Balas

  2. Faculty of Engineering and Information Technology, University of Technology Sydney, Centre for Artificial Intelligence, Sydney, Australia

    Lakhmi C. Jain

  3. Faculty of Engineering, “Aurel Vlaicu” University of Arad, Arad, Arad, Romania

    Marius Mircea Balas

  4. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

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Barbu, T. (2021). A Survey on Nonlinear Second-Order Diffusion-Based Techniques for Additive Denoising. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1221. Springer, Cham. https://doi.org/10.1007/978-3-030-51992-6_15

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