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.