title: Kernel regression for image processing and reconstruction.
abstract: 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.
authors.affiliation: Electrical Engineering Department, University of California, Santa Cruz 95064, USA. htakeda@soe.ucsc.edu
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Takeda
authors.forename: Sina
authors.initials: S
authors.lastname: Farsiu
authors.forename: Peyman
authors.initials: P
authors.lastname: Milanfar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted