Kernel regression for image processing and reconstruction.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Takeda, H; Farsiu, S; Milanfar, P

Published Date

  • February 2007

Published In

Volume / Issue

  • 16 / 2

Start / End Page

  • 349 - 366

PubMed ID

  • 17269630

Pubmed Central ID

  • 17269630

Electronic International Standard Serial Number (EISSN)

  • 1941-0042

International Standard Serial Number (ISSN)

  • 1057-7149

Digital Object Identifier (DOI)

  • 10.1109/tip.2006.888330

Language

  • eng