Using Convex Optimization of Autocorrelation with Constrained Support and Windowing for Improved Phase Retrieval Accuracy
This work addresses the problem of accurate phase retrieval from noisy diffraction data, which is critical for imaging modalities like coherent diffractive imaging.
The authors developed a convex optimization method for phase retrieval that uses a relaxed support constraint and maximum-likelihood treatment of data, achieving an improvement in crystallographic R-factor from 0.4 to 0.1 on highly noisy simulated data.
In imaging modalities recording diffraction data, 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. Such schemes are ill-suited to find the optimum solution for sparse data, since the recorded image does not correspond exactly to the original wave function. We construct a convex optimization problem using a relaxed support constraint and a maximum-likelihood treatment of the recorded data as a sample from the underlying wave function. We also stress the need to use relevant windowing techniques to account for the sampled pattern being finite. On simulated data, we demonstrate the benefits of our approach in terms of visual quality and an improvement in the crystallographic R-factor from .4 to .1 for highly noisy data.