Grid Filters for Local Nonlinear Image Restoration
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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.
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Todd Veldhuizen (1998). Grid Filters for Local Nonlinear Image Restoration. UWSpace. http://hdl.handle.net/10012/943