Computational super-resolution phase retrieval from multiple phase-coded diffraction patterns: simulation study and experiments

Katkovnik, Vladimir; Shevkunov, Igor; Petrov, Nikolay V.; Егиазарян, Карен

doi:10.1364/optica.4.000786

Cited by 79 publications

(43 citation statements)

References 31 publications

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“…These phase masks enable strong diffraction of the wavefield introduced in order to achieve the phase diversity sufficient for reconstruction of the complex-valued object from intensity measurements. As it was described in [34], we use the Gaussian random phase masks. These phase masks are implemented on SLM.…”

Section: Setup Of Experimentsmentioning

confidence: 99%

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Multiwavelength Absolute Phase Retrieval from Noisy Diffractive Patterns: Wavelength Multiplexing Algorithm

et al. 2018

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We study the problem of multiwavelength absolute phase retrieval from noisy diffraction patterns. The system is lensless with multiwavelength coherent input light beams and random phase masks applied for wavefront modulation. The light beams are formed by light sources radiating all wavelengths simultaneously. A sensor equipped by a Color Filter Array (CFA) is used for spectral measurement registration. The developed algorithm targeted on optimal phase retrieval from noisy observations is based on maximum likelihood technique. The algorithm is specified for Poissonian and Gaussian noise distributions. One of the key elements of the algorithm is an original sparse modeling of the multiwavelength complex-valued wavefronts based on the complex-domain block-matching 3D filtering. Presented numerical experiments are restricted to noisy Poissonian observations. They demonstrate that the developed algorithm leads to effective solutions explicitly using the sparsity for noise suppression and enabling accurate reconstruction of absolute phase of high-dynamic range.

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Section: Setup Of Experimentsmentioning

confidence: 99%

“…The transform domain phase and amplitude sparsity for phase imaging is developed in [29][30][31]. For the phase retrieval these techniques are applied in [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Multiwavelength Absolute Phase Retrieval from Noisy Diffractive Patterns: Wavelength Multiplexing Algorithm

et al. 2018

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“…The free propagation of the wavefronts to the sensor is given by the Rayleigh-Sommerfeld model with the transfer function defined through the angular spectrum (AS). 33 For the proper numerical AS propagation without aliasing effects, the zero-padding of the object must be applied. Its size is obtained from the inequality 34 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 1 3 ; 6 3 ; 3 9 7 N ≥…”

Section: Proposed Setupmentioning

confidence: 99%

“…As it was described in Ref. 33, we use the Gaussian random phase masks. Thus, in our simulations the propagation operator P s;λ;d in Eq.…”

Section: Proposed Setupmentioning

confidence: 99%

Multiwavelength surface contouring from phase-coded noisy diffraction patterns: wavelength-division optical setup

et al. 2018

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We propose an algorithm for absolute phase retrieval from multiwavelength noisy phase coded diffraction patterns. A lensless optical system is considered with a set of successive single wavelength experiments (wavelength-division setup). The phase masks are applied for modulation of the multiwavelength object wavefronts. The algorithm uses the forward/backward propagation for coherent light beams and sparsely encoding wavefronts, which leads to the complex-domain block-matching three-dimensional filtering. The key-element of the algorithm is an original aggregation of the multiwavelength object wavefronts for high-dynamic-range absolute phase reconstruction. Simulation tests demonstrate that the developed approach leads to the effective solutions explicitly using the sparsity for noise suppression and high-accuracy object absolute phase reconstruction from noisy data.

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“…Both intensity-coded and phase-coded structured illumination methods further promote the performance by their better multiplexing ability compared with conventional plane-wave illumination. 18,19 However, these methods suffer from the great degradation when the scattering effects become significant in the sample. The beam propagation method (BPM) 20 is then applied in phase tomography to provide a more accurate model by considering the nonlinear light propagation with scattering.…”

Section: Introductionmentioning

confidence: 99%

GPU-based deep convolutional neural network for tomographic phase microscopy with ℓ1 fitting and regularization

Qiao

et al. 2018

J. Biomed. Opt.

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Abstract. Tomographic phase microscopy (TPM) is a unique imaging modality to measure the three-dimensional refractive index distribution of transparent and semitransparent samples. However, the requirement of the dense sampling in a large range of incident angles restricts its temporal resolution and prevents its application in dynamic scenes. Here, we propose a graphics processing unit-based implementation of a deep convolutional neural network to improve the performance of phase tomography, especially with much fewer incident angles. As a loss function for the regularized TPM, the l 1 -norm sparsity constraint is introduced for both datafidelity term and gradient-domain regularizer in the multislice beam propagation model. We compare our method with several state-of-the-art algorithms and obtain at least 14 dB improvement in signal-to-noise ratio. Experimental results on HeLa cells are also shown with different levels of data reduction. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Computational super-resolution phase retrieval from multiple phase-coded diffraction patterns: simulation study and experiments

Cited by 79 publications

References 31 publications

Multiwavelength Absolute Phase Retrieval from Noisy Diffractive Patterns: Wavelength Multiplexing Algorithm

Multiwavelength Absolute Phase Retrieval from Noisy Diffractive Patterns: Wavelength Multiplexing Algorithm

Multiwavelength surface contouring from phase-coded noisy diffraction patterns: wavelength-division optical setup

GPU-based deep convolutional neural network for tomographic phase microscopy with ℓ1 fitting and regularization

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