Spectrum estimation of two-dimensional signals via the Radon transform

Abstract

Summary form only given. The Radon transform has been applied to spectrum estimation of noisy 2-D signals. Estimation of the spectrum of noisy temporal signals is a classic signal processing problem, and a number of estimation algorithms have been developed. These include periodograms, the Blackman-Tukey method, and autoregressive moving average (ARMA) models. Extension of the first two algorithms to multidimensional signals is straightforward. However, the additional available degrees of freedom affect the applicability of ARMA models to multidimensional problems. It has been demonstrated that standard 1-D ARMA models can be applied to the individual projections and combined to estimate the 2-D spectrum. Limitations of the algorithm have been explored.<>