  In various capacities of statistical signal processing two-dimensional (2-D)
chirp models have been considered significantly, particularly in image
processing$-$ to model gray-scale and texture images, magnetic resonance
imaging, optical imaging etc. In this paper we address the problem of
estimation of the unknown parameters of a 2-D chirp model under the assumption
that the errors are independently and identically distributed (i.i.d.). The key
attribute of the proposed estimation procedure is that it is computationally
more efficient than the least squares estimation method. Moreover, the proposed
estimators are observed to have the same asymptotic properties as the least
squares estimators, thus providing computational effectiveness without any
compromise on the efficiency of the estimators. We extend the propounded
estimation method to provide a sequential procedure to estimate the unknown
parameters of a 2-D chirp model with multiple components and under the
assumption of i.i.d. errors we study the large sample properties of these
sequential estimators. Simulation studies and a synthetic data analysis show
that the proposed estimators perform satisfactorily.
