Edge-aware filtering with local polynomial approximation and rectangle based weighting
2015 (English)In: IEEE Trans Cybernetics, ISSN 2168-2267, no 99, 1-13 p.Article in journal (Refereed) Epub ahead of print
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.
Terms—Computer vision, depth enhancement, edgeaware filtering, local polynomial regression, rectangular
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-124599DOI: 10.1109/TCYB.2015.2485203PubMedID: 26513818OAI: oai:DiVA.org:liu-124599DiVA: diva2:901030