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Enhancement of damaged-image prediction through Cahn-Hilliard image inpainting.

Authors
  • Carrillo, José A1
  • Kalliadasis, Serafim2
  • Liang, Fuyue2
  • Perez, Sergio P2, 3
  • 1 Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.
  • 2 Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
  • 3 Department of Mathematics, Imperial College London, London SW7 2AZ, UK.
Type
Published Article
Journal
Royal Society Open Science
Publisher
The Royal Society
Publication Date
May 19, 2021
Volume
8
Issue
5
Pages
201294–201294
Identifiers
DOI: 10.1098/rsos.201294
PMID: 34046183
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. We employ an appropriately modified Cahn-Hilliard equation as an image inpainting filter which is solved numerically with a finite-volume scheme exhibiting reduced computational cost and the properties of energy stability and boundedness. The benchmark dataset employed is Modified National Institute of Standards and Technology (MNIST) dataset, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based on dense layers with MNIST, and subsequently we contaminate the test set with damages of different types and intensities. We then compare the prediction accuracy of the neural network with and without applying the Cahn-Hilliard filter to the damaged images test. Our results quantify the significant improvement of damaged-image prediction by applying the Cahn-Hilliard filter, which for specific damages can increase up to 50% and is advantageous for low to moderate damage. © 2021 The Authors.

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