Model-independent noise-robust extension of ptychography

Konijnenberg, A. P.; Coene, W.; Urbach, H. P.

doi:10.1364/oe.26.005857

Cited by 16 publications

(11 citation statements)

References 25 publications

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“…Our conclusions suggest that more illumination power should be given to the part of object which is of most interest, although this may be difficult to realize in a practical ptychography experiment. As next steps of research, the performance of other ptychographic denoising algorithm [19,35,37,38,47] deserve further investigation. Investigating the CRLB and the statistic properties of the two algorithms for Gaussian noise and the mixed Poisson-Gaussian noise is also an interesting topic for further research.…”

Section: Discussionmentioning

confidence: 99%

“…Common choices for the noise model in ptychography are Poisson noise, Gaussian noise, and the mixed Poisson-Gaussian model. It has been demonstrated [22,40,41,47] that, by using the variance stabilization transform given by Bartlett [48] and Anscombe [49], one can approximate the maximum-likelihood method of Poisson noise by the amplitude-based cost minimization algorithm. Therefore, both the approach of maximum-likelihood and the amplitude-based cost-minimization algorithms can be used as a refinement method in ptychography with noisy data.…”

Section: Introductionmentioning

confidence: 99%

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Cramér-Rao lower bound and maximum-likelihood estimation in ptychography with Poisson noise

2020

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We investigate the performance of ptychography with noisy data by analyzing the Cramér-Rao lower bound. The lower bound of ptychography is derived and numerically computed for both top-hat plane wave and structured illumination. The influence of Poisson noise on the ptychography reconstruction is discussed. The computation result shows that, if the estimator is unbiased, the minimum variance for Poisson noise is mostly determined by the illumination power and the transmission function of the object. Monte Carlo analysis is conducted to validate our calculation results for different photon flux numbers. Furthermore, the performance of the maximum-likelihood method and the approach of amplitude-based cost-function minimization is studied in the Monte Carlo analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cramér-Rao lower bound and maximum-likelihood estimation in ptychography with Poisson noise

2020

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“…which is smooth everywhere. See Godard, Allain, Chamard and Rodenburg (2012), Zhang, Song and Dai (2017) and Konijnenberg, Coene and Urbach (2018) for more choices of loss functions. In general the amplitude-based Gaussian loss function (3.9) outperforms the intensity-based loss function (3.13) (Yeh et al 2015).…”

Section: Thermal Noisementioning

confidence: 99%

The numerics of phase retrieval

Fannjiang

Strohmer

2020

Acta Numerica

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Phase retrieval, i.e. the problem of recovering a function from the squared magnitude of its Fourier transform, arises in many applications, such as X-ray crystallography, diffraction imaging, optics, quantum mechanics and astronomy. This problem has confounded engineers, physicists, and mathematicians for many decades. Recently, phase retrieval has seen a resurgence in research activity, ignited by new imaging modalities and novel mathematical concepts. As our scientific experiments produce larger and larger datasets and we aim for faster and faster throughput, it is becoming increasingly important to study the involved numerical algorithms in a systematic and principled manner. Indeed, the past decade has witnessed a surge in the systematic study of computational algorithms for phase retrieval. In this paper we will review these recent advances from a numerical viewpoint.

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“…On the other hand, if only the intensity |E aS | 2 is recorded at the image plane, the reconstruction issue resembles better that of Optical Fourier Ptychography Microscopy (FPM) where the complex index of refraction is retrieved numerically from several intensity images of the sample obtained under various angles of illumination in a transmission microscope [25,[36][37][38][39][40][41].…”

Section: Inversion Procedures For Cars Fourier Ptychography Microscopy (Cars Fpm)mentioning

confidence: 99%

Coherent anti-Stokes Raman Fourier ptychography

Heuke¹,

Unger²,

Khadir³

et al. 2019

Opt. Express

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We present a theoretical and numerical study of coherent anti-Stokes Raman scattering Fourier ptychography microscopy (CARS-FPM), a scheme that has not been considered so far in the previously reported CARS wide-field imaging schemes. In this approach, the distribution of the Raman scatterer density of the sample is reconstructed numerically from CARS images obtained under various angles of incidences of the pump or Stokes beam. Our inversion procedure is based on an accurate vectorial model linking the CARS image to the sample and yields both the real and imaginary parts of the susceptibility, the latter giving access to the Raman information, with an improved resolution.

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Model-independent noise-robust extension of ptychography

Cited by 16 publications

References 25 publications

Cramér-Rao lower bound and maximum-likelihood estimation in ptychography with Poisson noise

Cramér-Rao lower bound and maximum-likelihood estimation in ptychography with Poisson noise

The numerics of phase retrieval

Coherent anti-Stokes Raman Fourier ptychography

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