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Kernel Regression based image denosing
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In this paper, we make contact with the field of non-parametric statistics and present a development and generalization
of tools and results for use in image processing and reconstruc-tion. 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 pop-ular 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
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