A Robust Method for Gaussian Profile Estimation in the Case of Overlapping Objects

The precise stellar object identification is one of the major research fields in astronomy. In astronomical images, the 2D Gaussian function provides a good approximation of stellar objects. This paper proposes a robust method for the estimation of the 2D Gaussian profiles in astronomical images when the stellar components are overlapped. The proposed method efficiently separates the regions of interest of close components without knowing neither their exact number nor accurate center positions. Therefore, fainter objects can be identified, and outliers can be detected. The proposed method is an iteratively reweighted least squares method where weights are updated in each step using a robust measure of residual error dispersion. Soft thresholding based on robust statistics increases the accuracy of the estimated 2D profile parameters and efficiently solves the problem of outliers. The comparison of the proposed method with the conventional least squares method showed a significant modeling gain for one particular set of contributing factors; the difference of magnitudes of close objects, their center-to-center distance, and the widths of their semiaxes relative to the imaging system resolution.