Phase retrieval from noisy data based on minimization of penalized I-divergence.

We study noise artifacts in phase retrieval based on minimization of an information-theoretic discrepancy measure called Csiszár's I-divergence. We specifically focus on adding Poisson noise to either the autocorrelation of the true image (as in astronomical imaging through turbulence) or the squared Fourier magnitudes of the true image (as in x-ray crystallography). Noise effects are quantified via various error metrics as signal-to-noise ratios vary. We propose penalized minimum I-divergence methods to suppress the observed noise artifacts. To avoid computational difficulties arising from the introduction of a penalty, we adapt Green's one-step-late approach for use in our minimum I-divergence framework.