A review of ptychographic techniques for ultrashort pulse measurement

Kane, Daniel J.; Vakhtin, Andrei B.

doi:10.1016/j.pquantelec.2021.100364

Cited by 6 publications

(3 citation statements)

References 112 publications

(264 reference statements)

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“…Other interesting blind problem for full characterization of ultrashort optical pulses is to use frequency-resolved optical gating (FROG) [82,3,51].…”

Section: Mathematical Formulamentioning

confidence: 99%

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Fast Iterative Algorithms for Blind Phase Retrieval: A Survey

Chang

Yang

Marchesini

2022

Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

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In nanoscale imaging technique and ultrafast laser, the reconstruction procedure is normally formulated as a blind phase retrieval (BPR) problem, where one has to recover both the sample and the probe (pupil) jointly from phaseless data. This survey first presents the mathematical formula of BPR, related nonlinear optimization problems and then gives a brief review of the recent iterative algorithms. It mainly consists of three types of algorithms, including the operator-splitting based first-order optimization methods, second order algorithm with Hessian, and subspace methods. The future research directions for experimental issues and theoretical analysis are further discussed.

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“…Other interesting blind problem for full characterization of ultrashort optical pulses is to use frequency-resolved optical gating (FROG) [82,3,51].…”

Section: Mathematical Formulamentioning

confidence: 99%

“…That may limit practical applications, especially for reconstructing 2D images or volumes. It seems rather difficult to adopt the same convex method for other cases of BPR, since they cannot be rewritten as the same form as (51). Convexifying a general BPR problem should be an interesting research direction in future.…”

Section: Convex Programmingmentioning

confidence: 99%

Fast Iterative Algorithms for Blind Phase Retrieval: A Survey

Chang

Yang

Marchesini

2022

Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

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“…However, FROG is not a direct measurement, i.e., the electric field must be reconstructed from the FROG measurement, which is a spectrally-resolved second-order autocorrelation of the pulse. The pulse reconstruction problem is an inverse problem 5 , in which a forward model that produces a FROG measurement from the complex pulse electric field is well known and easy to implement, but its inversion is challenging. In spite of efforts to solve the FROG reconstruction problem with neural networks, the standard and most widely used methods are iterative Fourier replacement algorithms such as principal components generalized projections (PCGP) algorithm 6 .…”

Section: Introductionmentioning

confidence: 99%

Ultrafast pulse retrieval with one-shot unsupervised deep learning

Wilson

2023

Ultrafast Nonlinear Imaging and Spectroscopy XI

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Recently, much attention has been drawn to deep learning models, namely deep Convolutional Neural Networks (CNNs) trained in a supervised fashion. Supervised learning is time consuming, requires a large dataset where the answer is already known, and often fails to generalize to new cases with patterns or noise statistics that were not foreseen in the training data. Unsupervised learning, on the other hand, does not use prior knowledge of the answer, can adapt to new data at hand, and can learn from single examples. A CNN can solve inverse problems in this way by learning to produce a result that, when passed through a forward model, recovers the experimental measurement. For example, a CNN can be trained in a supervised fashion to recover the complex field of a pulse from its Frequency-Resolved Optical Gating (FROG) measurement, by training it on thousands of examples of FROG measurements (the network inputs) and their corresponding optical pulses (the target outputs). In unsupervised learning, the network learns to transform a FROG measurement into an optical pulse, that, when passed through a forward model, recovers the original FROG measurement. We find this succeeds even on single FROG measurements, opening up new possibilities for analyzing measurements that fall outside the foreseeable distribution of training data.

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