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dc.contributor.authorVeldhuizen, Todden 14:00:16 (GMT) 14:00:16 (GMT)
dc.description.abstractA 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.en
dc.format.extent11869646 bytes
dc.publisherUniversity of Waterlooen
dc.rightsCopyright: 1998, Veldhuizen, Todd . All rights reserved.en
dc.subjectMechanical Engineeringen
dc.subjectFabric Mechanicsen
dc.subjectFabric Bendingen
dc.subjectTextile Mechanicsen
dc.subjectCloth Simulationen
dc.subjectFriction Modelsen
dc.titleGrid Filters for Local Nonlinear Image Restorationen
dc.typeMaster Thesisen
dc.pendingfalseen Engineeringen
uws-etd.degreeMaster of Applied Scienceen

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