Fast algorithm for implementing the minimum-negativity constraint for Fourier spectrum extrapolation Part 2
Samuel J. Howard · Applied Optics · 1988
The basic theory and essential ideas of a new algorithm based on the minimum-negativity constraint for restoring the Fourier spectrum and improving resolution were set forth in an earlier paper. The method was proved to work for artificial noise-free data of 1024 data points. In the present paper the efficacy of the method will be demonstrated for an experimental data set of 32K data points in length (K = 1024). Further details of the new algorithm will be discussed. A brief comparison will be made between this method and some of the other popular methods for Fourier spectrum extrapolation. Also, a modification of the algorithm that will allow the computations to be implemented on a parallel-processing computer will be presented.