Abstract

Experimental data in digital form are often subjected to refinement using a curve-fitting procedure. For example, the least-squares determination of the "best fit" straight line or polynomial of higher degree allows a functional representation of digital data to be unambiguously established. Furthermore, functions calculated in this way can serve as bases for subsequent data smoothing or interpolation.

Least-Squares Polynomial Filtering of Images by Convolution

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

Least-squares fitting of orthogonal polynomials to the wave-aberration function

J. L. Rayces
Appl. Opt. 31(13) 2223-2228 (1992)

Spectrum constructing with nonuniform samples using least-squares approximation by cosine polynomials

Cong Feng, Jingqiu Liang, and Zhongzhu Liang
Appl. Opt. 50(34) 6377-6383 (2011)

Chromatic Visibility Coefficients by the Method of Least Squares

Deane B. Judd
J. Opt. Soc. Am. 10(6) 635-647 (1925)

Method of Least Squares and Curve Fitting

H. S. Uhler
J. Opt. Soc. Am. 7(11) 1043-1066 (1923)