Fitting instead of annihilation: Improved recovery of noisy FRI signals

Recently, classical sampling theory has been broadened to include a class of non-bandlimited signals that possess finite rate of innovation (FRI). In this paper we consider the reconstruction of a periodic stream of Diracs from noisy samples. We demonstrate that its noiseless FRI samples can be represented as a ratio of two polynomials. Using this structure as a model, we propose recovering the FRI signal using a model fitting approach rather than an annihilation method. We present an algorithm that fits this model to the noisy samples and demonstrate that it has low computation cost and is more reliable than two state-of-the-art methods.