Abstract

The general analytic expression for the polynomial smoothing function of any degree for equally spaced data points is presented. In addition to the explicit formula, a simple recursion relation is also given. The determination of numerical coefficients in the convolution equation involves only integer arithmetic. These results are further used to describe in some detail the effectiveness of digital polynomial smoothing, or filtering, of sampled spectral data in their dependence on the degree K of the polynomial, the number S of smoothing passes, and the range T of points in the smoothing interval. Then it can be shown that the sharpness of the frequency cutoff increases with the degree of the polynomial, the high-frequency attenuation increases with the number of smooths, and the cutoff of the filter moves toward lower frequencies as the range of points in the smoothing interval increases. The values of these three parameters should not be chosen entirely independently of one another, but the first two should be selected before the third.

© 1981 Optical Society of America

Zernike annular polynomials for imaging systems with annular pupils

Virendra N. Mahajan
J. Opt. Soc. Am. 71(1) 75-85 (1981)

Least-Squares Polynomial Filtering of Images by Convolution

Peter A. Jansson
J. Opt. Soc. Am. 62(2) 195-198 (1972)

Effects of different hypothetical detection mechanisms on the shape of spatial-frequency filters inferred from masking experiments: I. noise masks

G. Bruce Henning, B. Gevene Hertz, and J. L. Hinton
J. Opt. Soc. Am. 71(5) 574-581 (1981)

Degrees of freedom for scatterers with circular cross section

F. Gori and L. Ronchi
J. Opt. Soc. Am. 71(3) 250-258 (1981)

Optimum piecewise-constant Wiener filters

Leonard J. Cimini and Saleem A. Kassam
J. Opt. Soc. Am. 71(10) 1162-1171 (1981)