Ptychographic phase-retrieval by proximal algorithms

We derive a set of ptychography phase-retrieval iterative engines based on proximal algorithms originally developed in convex optimization theory, and discuss their connections with existing ones. The use of proximal operator creates a simple frame work that allows us to incorporate the effect of noise from a maximum-likelihood principle. We focus on three particular algorithms, namely proximal minimization, alternating direction method of multiplier and accelerated proximal gradient, and benckmark their performance with numerical simulations and experimental x-ray data. Among them, accelerated proximal gradient shows superior performance in terms of both accuracy and convergence rate for a noisy dataset.

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