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Kernel regression for image processing and reconstruction
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DOI：10.1109/TIP.2006.888330.png)
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Abstract
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In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples.
Keywords:
bilateral filter
denoising
fusion
interpolation
irregularly sampled data
kernel function
kernel regression
local polynomial
nonlinear filter
nonparametric
scaling
spatially adaptive
super-resolution
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