Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/11393
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dc.contributor.authorJidesh, P.
dc.contributor.authorGeorge, S.
dc.date.accessioned2020-03-31T08:31:16Z-
dc.date.available2020-03-31T08:31:16Z-
dc.date.issued2014
dc.identifier.citationJournal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an, 2014, Vol.37, 1, pp.122-133en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/11393-
dc.description.abstractIn this paper, we propose a third-order Gauss curvature-driven geometric diffusion Partial Differential Equation for inpainting and reconstructing images. In Gauss curvature-driven diffusion processes, the rate of diffusion is directly proportional to the Gauss curvature value of the level curve. Since the Gauss curvature is the product of principal curvatures, its value become zero when even one of the principal curvatures is zero. Therefore, when Gauss curvature is used as a driving function for diffusion, the evolution preserves some of the meaningful structures with nonzero mean curvature values (viz. curvy edges, corners, etc.). However, the noise features always have nonzero Gauss curvature value and hence the diffusion process effectively removes them. The inpainting property of geometric PDE based on the Gauss curvature is being used in this work for reconstructing lost or degraded information. A filter is proposed to reconstruct the original images from the observed blurred and noisy images along with inpainting the desired image domain. 2014 Copyright The Chinese Institute of Engineers.en_US
dc.titleGauss curvature-driven image inpainting for image reconstructionen_US
dc.typeArticleen_US
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