Maximum-likelihood edge detection in digital signals

Abstract This paper treats the problem of edge detection in noisy piecewise-constant digital signals, using a maximum likelihood approach. Conventional edge detectors usually assume that the noise is Gaussian, and do not take advantage of prior knowledge about the ensemble of signals (aside from the assumption that the signals are piecewise constant). Our approach can handle noise that has an arbitrary probability density function; it also makes use of prior probability densities for the piece sizes and values in the signal ensemble.