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dc.contributor.authorJeon, Junhoen_US
dc.contributor.authorLee, Hyunjoonen_US
dc.contributor.authorKang, Henryen_US
dc.contributor.authorLee, Seungyongen_US
dc.contributor.editorEitan Grinspun and Bernd Bickel and Yoshinori Dobashien_US
dc.date.accessioned2016-10-11T05:18:47Z
dc.date.available2016-10-11T05:18:47Z
dc.date.issued2016
dc.identifier.issn1467-8659
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.13005
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13005
dc.description.abstractThis paper presents a novel method to enhance the performance of structure-preserving image and texture filtering. With conventional edge-aware filters, it is often challenging to handle images of high complexity where features of multiple scales coexist. In particular, it is not always easy to find the right balance between removing unimportant details and protecting important features when they come in multiple sizes, shapes, and contrasts. Unlike previous approaches, we address this issue from the perspective of adaptive kernel scales. Relying on patch-based statistics, our method identifies texture from structure and also finds an optimal per-pixel smoothing scale. We show that the proposed mechanism helps achieve enhanced image/texture filtering performance in terms of protecting the prominent geometric structures in the image, such as edges and corners, and keeping them sharp even after significant smoothing of the original signal.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.4.3 [Image Processing and Computer Vision]
dc.subjectEnhancement
dc.subjectSmoothing
dc.titleScale-aware Structure-Preserving Texture Filteringen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersTextures/Mapping
dc.description.volume35
dc.description.number7
dc.identifier.doi10.1111/cgf.13005
dc.identifier.pages77-86


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