Article
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Preserved in Portico This version is not peer-reviewed
Variational Anisotropic Gradient-Domain Image Processing
Version 1
: Received: 27 August 2021 / Approved: 31 August 2021 / Online: 31 August 2021 (12:44:20 CEST)
Version 2 : Received: 23 September 2021 / Approved: 24 September 2021 / Online: 24 September 2021 (10:24:26 CEST)
Version 2 : Received: 23 September 2021 / Approved: 24 September 2021 / Online: 24 September 2021 (10:24:26 CEST)
A peer-reviewed article of this Preprint also exists.
Farup, I. Variational Anisotropic Gradient-Domain Image Processing. J. Imaging 2021, 7, 196. Farup, I. Variational Anisotropic Gradient-Domain Image Processing. J. Imaging 2021, 7, 196.
Abstract
Gradient-domain image processing is a technique where, instead of operating directly on the image pixel values, the gradient of the image is computed and processed. The resulting image is obtained by reintegrating the processed gradient. This is normally done by solving the Poisson equation, most oftenly by means of a finite difference implementation of the gradient descent method. However, this technique in some cases lead to severe haloing artefacts in the resulting image. To deal with this, local or anisotropic diffusion has been added as an ad-hoc modification of the Poisson equation. In this paper, we show that a version of anisotropic gradient-domain image processing can result from a more general variational formulation through the minimisation of a functional formulated in terms of the eigenvalues of the structure tensor of the differences between the processed gradient and the gradient of the original image. Example applications of linear and non-linear local contrast enhancement and colour image daltonisation illustrate the behaviour of the method.
Keywords
variational methods; anisotropic diffusion; gradient-domain image processing; local contrast enhancement
Subject
Computer Science and Mathematics, Signal Processing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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