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dc.contributor.authorFukatsu, Mikuen_US
dc.contributor.authorYoshizawa, Shinen_US
dc.contributor.authorTakemura, Hiroshien_US
dc.contributor.authorYokota, Hideoen_US
dc.contributor.editorYang, Yinen_US
dc.contributor.editorParakkat, Amal D.en_US
dc.contributor.editorDeng, Bailinen_US
dc.contributor.editorNoh, Seung-Taken_US
dc.description.abstractSeparating shapes and textures of digital images at different scales is useful in computer graphics. The Rolling Guidance (RG) filter, which removes structures smaller than a specified scale while preserving salient edges, has attracted considerable attention. Conventional RG-based filters have some drawbacks, including smoothness/sharpness quality dependence on scale and non-uniform convergence. This paper proposes a novel RG-based image filter that has more stable filtering quality at varying scales. Our filtering approach is an adaptive and dynamic regularization for a recursive regression model in the RG framework to produce more edge saliency and appropriate scale convergence. Our numerical experiments demonstrated filtering results with uniform convergence and high accuracy for varying scales.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.subjectCCS Concepts: Computing methodologies --> Computational photography; Image processing
dc.subjectComputing methodologies
dc.subjectComputational photography
dc.subjectImage processing
dc.titleAdaptive and Dynamic Regularization for Rolling Guidance Image Filteringen_US
dc.description.seriesinformationPacific Graphics Short Papers, Posters, and Work-in-Progress Papers
dc.description.sectionheadersImage Enhancement
dc.identifier.pages6 pages

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License