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Edge-aware filtering with local polynomial approximation and rectangle based weighting
University of New South Wales,Sydney, NSW, Australia.
degree in computer vision from Imperial College London, London U.K..
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
2015 (English)In: IEEE Trans Cybernetics, ISSN 2168-2267, no 99, 1-13 p.Article in journal (Refereed) Epub ahead of print
Abstract [en]

This paper presents a novel method for performing guided image filtering using local polynomial approximation (LPA) with range guidance. In our method, the LPA is introduced into a multipoint framework for reliable model regression and better preservation on image spatial variation which usually contains the essential information in the input image. In addition, we develop a weighting scheme which has the spatial flexibility during the filtering process. All components in our method are efficiently implemented and a constant computation complexity is achieved. Compared with conventional filtering methods, our method provides clearer boundaries and performs especially better in recovering spatial variation from noisy images. We conduct a number of experiments for different applications: depth image upsampling, joint image denoising, details enhancement, and image abstraction. Both quantitative and qualitative comparisons demonstrate that our method outperforms state-of-the-art methods.

Place, publisher, year, edition, pages
2015. no 99, 1-13 p.
Keyword [en]
Terms—Computer vision, depth enhancement, edgeaware filtering, local polynomial regression, rectangular
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-124599DOI: 10.1109/TCYB.2015.2485203PubMedID: 26513818OAI: diva2:901030
Available from: 2016-02-05 Created: 2016-02-05 Last updated: 2016-08-19

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Pham, Tuan
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Department of Biomedical EngineeringFaculty of Science & Engineering
Computer Vision and Robotics (Autonomous Systems)

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