Using convex optimization of autocorrelation with constrained support and windowing for improved phase retrieval accuracy.

In imaging modalities recording diffraction data, such as the imaging of viruses at X-ray free electron laser facilities, the original image can be reconstructed assuming known phases. When phases are unknown, oversampling and a constraint on the support region in the original object can be used to solve a non-convex optimization problem using iterative alternating-projection methods. Such schemes are ill-suited for finding the optimum solution for sparse data, since the recorded pattern does not correspond exactly to the original wave function. Different iteration starting points can give rise to different solutions. We construct a convex optimization problem, where the only local optimum is also the global optimum. This is achieved using a modified support constraint and a maximum-likelihood treatment of the recorded data as a sample from the underlying wave function. This relaxed problem is solved in order to provide a new set of most probable "healed" signal intensities, without sparseness and missing data. For these new intensities, it should be possible to satisfy the support constraint and intensity constraint exactly, without conflicts between them. By making both constraints satisfiable, traditional phase retrieval with superior results is made possible. On simulated data, we demonstrate the benefits of our approach visually, and quantify the improvement in terms of the crystallographic R factor for the recovered scalar amplitudes relative to true simulations from .405 to .097, as well as the mean-squared error in the reconstructed image from .233 to .139. We also compare our approach, with regards to theory and simulation results, to other approaches for healing as well as noise-tolerant phase retrieval. These tests indicate that the COACS pre-processing allows for best-in-class results.