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A fourth-order PDE denoising model with an adaptive relaxation method

A fourth-order PDE denoising model with an adaptive relaxation method

Lai, Choi-Hong ORCID: 0000-0002-7558-6398, Liu, X.Y. and Pericleous, Kyriacos A. ORCID: 0000-0002-7426-9999 (2015) A fourth-order PDE denoising model with an adaptive relaxation method. International Journal of Computer Mathematics, 92 (3). pp. 608-622. ISSN 0020-7160 (Print), 1029-0265 (Online) (doi:https://doi.org/10.1080/00207160.2014.904854)

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Abstract

In this paper, an adaptive relaxation method and a discontinuity treatment of edges are proposed to improve the digital image denoising process by using the fourth-order partial differential equation (known as the YK model) first proposed by You and Kaveh. Since the YK model would generate some speckles into the denoised image, a relaxation method is incorporated into the model to reduce the formation of isolated speckles. An additional improvement is employed to handle the discontinuity on the edges of the image. In order to stop the iteration automatically, a control of the iteration is integrated into the denoising process. Numerical results demonstrate that such modifications not only make the denoised image look more natural, but also achieve a higher value of PSNR.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Computer Mathematics on 7/5/2014, available online: http://dx.doi.org/10.1080/00207160.2014.904854
Uncontrolled Keywords: Image denoise, Fourth-order PDE, Relaxation method
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Numerical Modelling & Process Analysis (CNMPA) > Computational Science & Engineering Group (CSEG)
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 02 Mar 2019 15:53
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT a
Selected for GREAT 2018: GREAT d
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/14672

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