Mapping-based image diffusion

• Verfasst von: Åström, Freddie [VerfasserIn]
• Verfasst von: Felsberg, Michael [VerfasserIn]
• Verfasst von: Baravdish, George [VerfasserIn]
• Titel: Mapping-based image diffusion
• Verf.angabe: Freddie Åström, Michael Felsberg, George Baravdish
• Jahr: 2017
• Jahr des Originals: 2016
• Umfang: 31 S.
• Fussnoten: First online: 17 August 2016 ; Gesehen am 17.08.2018
• Titel Quelle: Enthalten in: Journal of mathematical imaging and vision
• Ort Quelle: Dordrecht [u.a.] : Springer Science + Business Media B.V, 1992
• Jahr Quelle: 2017
• Band/Heft Quelle: 57(2017), 3, Seite 293-323
• ISSN Quelle: 1573-7683
• DOI: doi:10.1007/s10851-016-0672-6
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1580166768

Abstract

In this work, we introduce a novel tensor-based functional for targeted image enhancement and denoising. Via explicit regularization, our formulation incorporates application-dependent and contextual information using first principles. Few works in literature treat variational models that describe both application-dependent information and contextual knowledge of the denoising problem. We prove the existence of a minimizer and present results on tensor symmetry constraints, convexity, and geometric interpretation of the proposed functional. We show that our framework excels in applications where nonlinear functions are present such as in gamma correction and targeted value range filtering. We also study general denoising performance where we show comparable results to dedicated PDE-based state-of-the-art methods.