Automated Smoothing of Image and Other Regularly Spaced Data

Authors:
M. Berman
Affiliations:
-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1994

Citing 4
Cited 4

An efficient method for calculating smoothing splines using orthogonal transformations

Numerische Mathematik
A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of thin-plate splines

SIAM Journal on Scientific and Statistical Computing
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

On Image Analysis by Moments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Constant time 0(1) pixel averaging with applicability to kernel filtering

Computers & Geosciences
Spectral estimation of a structural thin-plate smoothing model

Computational Statistics & Data Analysis
A statistical approach to the problem of restoring damaged and contaminated images

Pattern Recognition

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Abstract

This paper is primarily motivated by the problem of automatically removing unwanted noise from high-dimensional remote sensing imagery. The initial step involves the transformation of the data to a space of intrinsically lower dimensionality and the smoothing of images in the new space. Different images require different amounts of smoothing. The signal (assumed to be mostly smooth with relatively few discontinuities) is estimated from the data using the method of generalized cross-validation. It is shown how the generalized cross-validated thin-plate smoothing spline with observations on a regular grid (in d-dimensions) is easily approximated and computed in the Fourier domain. Space domain approximations are also investigated. The technique is applied to some remote sensing data.