Phase retrieval and design with automatic differentiation: tutorial

@article{Wong2021PhaseRA,
  title={Phase retrieval and design with automatic differentiation: tutorial},
  author={Alison P. Wong and Benjamin J. S. Pope and Louis Desdoigts and Peter G. Tuthill and Barnaby R. M. Norris and Christopher H. Betters},
  journal={Journal of the Optical Society of America B},
  year={2021}
}
The principal limitation in many areas of astronomy, especially for directly imaging exoplanets, arises from instability in the point spread function (PSF) delivered by the telescope and instrument. To understand the transfer function, it is often necessary to infer a set of optical aberrations given only the intensity distribution on the sensor — the problem of phase retrieval. This can be important for post-processing of existing data, or for the design of optical phase masks to engineer PSFs… Expand

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References

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TLDR
A variant of the Fienup algorithm is developed to incorporate the condition of sparsity and to successively estimate and refine the phase starting from the magnitude measurements and it is shown that the proposed iterative algorithm possesses Cauchy convergence properties. Expand
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TLDR
This work uses the regularization-by-denoising framework and a convolutional neural network denoiser to create prDeep, a new phase retrieval algorithm that is both robust and broadly applicable and test and validate in simulation to demonstrate that it is robust to noise and can handle a variety of system models. Expand
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TLDR
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TLDR
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