Spectrally-spatial fourier-holography

Каленков, С. Г.; Kalenkov, G. S.; Shtanko, A. E.

doi:10.1364/oe.21.024985

Cited by 29 publications

(8 citation statements)

References 3 publications

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“…We shown that theoretically in few recent works [3][4][5] and experimentally obtained hyperspectral holograms of several microobjects [6]. In current work we discuss new results of recent research.…”

Section: Introductionmentioning

confidence: 69%

Hyperspectral digital holography of microobjects

2015

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Novel method is suggested for a hyperspectral wave field holographic recording, based on asymmetrical Fourier spectrometer with a flat microobject placed in one of its arms. The output signal, which is the interference of the reference field with the field diffracted by the object, is registered by CCD. The process of recording is reduced to consecutive registration of two-dimensional interferograms by changing the optical length of the reference arm of the interferometer. One-dimensional Fourier transform of the interferogram in each pixel gives a spatial distribution of the complex amplitude for all spectral components of a hyperspectral object field. Inverse Fresnel transform of this field gives a hyperspectral object field in the object plane. Hyperspectral amplitude and average-phase profile images of standard microscope samples obtained experimentally are presented. Coloring, Fellgett's advantage and speckle noise reduction are discussed.

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Section: Introductionmentioning

confidence: 69%

Hyperspectral digital holography of microobjects

2015

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“…where R t.k stays for the harmonic reference signal varying from experiment-to-experiment [29], [30], [31].…”

Section: Introductionmentioning

confidence: 99%

ADMM and Spectral Proximity Operators in Hyperspectral Broadband Phase Retrieval for Quantitative Phase Imaging

Katkovnik¹,

Shevkunov²,

Егиазарян³

2021

Preprint

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A novel formulation of the hyperspectral broadband phase retrieval is developed for the scenario where both object and modulation phase masks are spectrally varying. The proposed algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and Spectral Proximity Operators (SPO) derived for Gaussian and Poissonian observations. Computations for these operators are reduced to the solution of sets of cubic (for Gaussian) and quadratic (for Poissonian) algebraic equations. These proximity operators resolve two problems. Firstly, the complex domain spectral components of signals are extracted from the total intensity observations calculated as sums of the signal spectral intensities. In this way, the spectral analysis of the total intensities is achieved. Secondly, the noisy observations are filtered, compromising noisy intensity observations and their predicted counterparts. The ability to resolve the hyperspectral broadband phase retrieval problem and to find the spectrum varying object are essentially defined by the spectral properties of object and image formation operators. The simulation tests demonstrate that the phase retrieval in this formulation can be successfully resolved.

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“…Over the last few years, HS coherent diffractive imaging (HSCDI) shows a strong progress, for instance, in high-resolution microscopy. Interference between reference and object wavefronts registered as intensity diffraction patterns is used conventionally in spectrally resolved interferometry, 1,2 Fourier transform holography, 3,4 and HS digital holography. 2,5,6 Along with the traditional intensity imaging, HSCDI provides phase imaging, which brings additional information about an object under investigation, e.g., label-free multispectral refractive index estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Interference between reference and object wavefronts registered as intensity diffraction patterns is used conventionally in spectrally resolved interferometry, 1,2 Fourier transform holography, 3,4 and HS digital holography. 2,5,6 Along with the traditional intensity imaging, HSCDI provides phase imaging, which brings additional information about an object under investigation, e.g., label-free multispectral refractive index estimation. 7 In this paper, we consider a class of HSCDI in a shearography optical setup, 8 where two coherent beams, the main (basic) and its time-delay copy, go through a transparent object simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral phase retrieval: spectral–spatial data processing with sparsity-based complex domain cube filter

2021

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Hyperspectral (HS) imaging retrieves information from data obtained across broadband spectral channels. Information to retrieve is a 3D cube, where two coordinates are spatial and the third one is spectral. This cube is complex-valued with varying amplitude and phase. We consider shearography optical setup, in which two phase-shifted broadband copies of the object projections are interfering at a sensor. Registered observations are intensities summarized over spectral channels. For phase reconstruction, the variational setting of the phase retrieval problem is used to derive the iterative algorithm, which includes the original proximity spectral analysis operator and the sparsity modeling of the complex-valued object 3D cube. We resolve the HS phase retrieval problem without random phase coding of wavefronts typical for the most conventional phase retrieval techniques. We show the performance of the algorithm for object phase and thickness imaging in simulation and experimental tests. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 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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Spectrally-spatial fourier-holography

Cited by 29 publications

References 3 publications

Hyperspectral digital holography of microobjects

Hyperspectral digital holography of microobjects

ADMM and Spectral Proximity Operators in Hyperspectral Broadband Phase Retrieval for Quantitative Phase Imaging

Hyperspectral phase retrieval: spectral–spatial data processing with sparsity-based complex domain cube filter

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