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Edge-Aware Filtering with Local Polynomial Approximation and Rectangle-Based Weighting
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DOI：10.1109/TCYB.2015.2485203.png)
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摘要
En 中文
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.
Keyword:
Computer vision
depth enhancement
edge-aware filtering
local polynomial regression
rectangular weights (RWs)
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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