Performance analysis for a subspace decomposition point-source image restoration algorithm

This paper addresses the problem of resolving and localizing blurred point sources in intensity images, such as atmospherically blurred star images. It is shown that this problem can be modeled in a form similar to that used for sensor array data in direction of arrival (DOA) estimation. A new superresolving restoration algorithm is described as a generalization of eigenstructure decomposition methods popular in DOA estimation. The algorithm includes a signal subspace rank enhancement technique that works in the presence of non-uniform element weighting due to blur. A theoretical analysis of source localization error is presented, and an expression predicting error performance is derived. Restoration examples are presented illustrating the accuracy of the error analysis and demonstrating algorithm performance