Cumulant-based blur identification approach to image restoration

In this paper, we provide a novel method based on higher order statistic cumulants to identify nonminimum-phase point spread functions and enlarges the possible distribution type of the image formation field. In our method, we specify the blur identification problem as an ARMA model parameter identification problem, but we consider the image model as a realization of a colored signal instead of a normal zero-mean white Gaussian signal, which enlarges the range of image types. For the colored input ARMA model, the contributions of the bicepstrum of ARMA model are only along the axes and 45 degree(s) degree lines, so we extract the linear parts of cumulant of blur image to analysis, in which we use higher-order statistic techniques to estimate the ARMA parameters. The experiments are present in this paper.