Statistical imaging and complexity regularization

Abstract

We apply complexity regularization to statistical ill-posed inverse problems in imaging. We formulate a natural distortion measure in image space and develop nonasymptotic bounds on estimation performance in terms of an index of resolvability that characterizes the compressibility of the true image. These bounds extend previous results that were obtained under simpler observational models.