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Grid Filters for Local Nonlinear Image Restoration

A new approach to local nonlinear image restoration is described, based on approximating functions using a regular grid of points in a many-dimensional space. Symmetry reductions and compression of the sparse grid make it feasible to work with twelve-dimensional grids as large as 22<sup>12</sup>. Unlike polynomials and neural networks whose filtering complexity per pixel is linear in the number of filter co-efficients, grid filters have O(1) complexity per pixel. Grid filters require only a single presentation of the training samples, are numerically stable, leave unusual image features unchanged, and are a superset of order statistic filters. Results are presented for additive noise, blurring, and superresolution.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/943
Date January 1998
CreatorsVeldhuizen, Todd
PublisherUniversity of Waterloo
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatapplication/pdf, 11869646 bytes, application/pdf
RightsCopyright: 1998, Veldhuizen, Todd . All rights reserved.

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