A two-level method for image denoising and image deblurring models using mean curvature regularization

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Fairag, Faisal and Chen, Ke and Brito-Loeza, Carlos and Ahmad, Shahbaz (2021) A two-level method for image denoising and image deblurring models using mean curvature regularization. International Journal of Computer Mathematics, 99 (4). pp. 693-713. ISSN 0020-7160 (https://doi.org/10.1080/00207160.2021.1929939)

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Abstract

The mean curvature (MC)-based image denoising and image deblurring models are used to enhance the quality of the denoised images and deblurred images respectively. These models are very efficient in removing staircase effect, preserving edges and other nice properties. However, high order derivatives appear in the Euler–Lagrange equations of the MC-based models which create problems in developing an efficient numerical algorithm. To overcome this difficulty, we present a robust and efficient Two-Level method for MC-based image denoising and image deblurring models. The Two-Level method consists of solving one small problem and one large problem. The small problem is a nonlinear system, having high order derivative, on Level I (image having small number of pixels). The large problem is one less expensive system, having low order derivative, on Level II (image having large number of pixels). The derivation of the optimal regularization parameter of Level II is studied and formula is presented. Numerical experiments on digital images are presented to exhibit the performance of the Two-Level method.

ORCID iDs

Fairag, Faisal, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623, Brito-Loeza, Carlos and Ahmad, Shahbaz;
  • Item type:Article
    ID code:87119
    Dates:
    Date
    Event
    27 May 2021
    Published
    27 May 2021
    Published Online
    29 April 2021
    Accepted
    28 November 2019
    Submitted
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:01 Nov 2023 12:15
    Last modified:14 Dec 2024 01:35
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/87119
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