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• W2972437937 abstract "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." @default.
• W2972437937 created "2019-09-19" @default.
• W2972437937 creator A5001574275 @default.
• W2972437937 date "2020-02-01" @default.
• W2972437937 modified "2023-09-30" @default.
• W2972437937 title "Ptychographic phase retrieval by proximal algorithms" @default.
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• W2972437937 doi "https://doi.org/10.1088/1367-2630/ab704e" @default.
• W2972437937 hasPublicationYear "2020" @default.
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