Basic Fundamentals of Digital Holography

Picart, Pascal; Gross, Michel; Marquet, Pierre

doi:10.1002/9781119091745.ch1

Cited by 25 publications

(27 citation statements)

References 124 publications

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“…300 K), then the contributions of the dark current noise (σ DC ) and background noise (σ bg ) are irrelevant and can be neglected in Eq. (5). Whereas σ RoN is technical data from the camera provider, the photons noise is given by σ 2 ph = α i N sat and the quantization noise is given by σ 2 q = N 2 sat /(12…”

Section: Estimation Of the Phase Noisementioning

confidence: 99%

Modelling of the photometric balance for two-wavelength spatially multiplexed digital holography

Piniard

Sorrente

Hug

et al. 2021

Modeling Aspects in Optical Metrology VIII

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Multi-wavelength digital holography is a very powerful approach for surface shape measurements. It has the advantage of being contact-less, non-intrusive, and yields full-field surface shape data without any requirement for scanning. This paper proposes the analysis of the standard deviation of noise in surface-shape data from twowavelength spatially-multiplexed digital holograms. The influence of noise on the measurements of the surface shape is described by an analytical approach. Numerical simulations with realistic experimental parameters are provided and discussed.

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Section: Estimation Of the Phase Noisementioning

confidence: 99%

Modelling of the photometric balance for two-wavelength spatially multiplexed digital holography

Piniard

Sorrente

Hug

et al. 2021

Modeling Aspects in Optical Metrology VIII

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“…The most important feature in the reconstructed image plane is the average size of the speckle grains, which is related to the transverse correlation length of the speckle field. As a general rule, the size of the speckle grain is linked to the physical parameters of the optical set-up 7 – 10 , 26 according to Eq. ( 2 ): …”

Section: Noise Process In Dhmentioning

confidence: 99%

“…This decorrelation noise may have several origins 7 , 9 , 10 , 46 , 48 , 94 – 97 , 99 – 101 . These are briefly reviewed in ref.…”

Section: Noise Process In Dhmentioning

confidence: 99%

Strategies for reducing speckle noise in digital holography

Bianco¹,

Memmolo²,

Leo³

et al. 2018

Light Sci Appl

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225

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Digital holography (DH) has emerged as one of the most effective coherent imaging technologies. The technological developments of digital sensors and optical elements have made DH the primary approach in several research fields, from quantitative phase imaging to optical metrology and 3D display technologies, to name a few. Like many other digital imaging techniques, DH must cope with the issue of speckle artifacts, due to the coherent nature of the required light sources. Despite the complexity of the recently proposed de-speckling methods, many have not yet attained the required level of effectiveness. That is, a universal denoising strategy for completely suppressing holographic noise has not yet been established. Thus the removal of speckle noise from holographic images represents a bottleneck for the entire optics and photonics scientific community. This review article provides a broad discussion about the noise issue in DH, with the aim of covering the best-performing noise reduction approaches that have been proposed so far. Quantitative comparisons among these approaches will be presented.

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“…The phase generally refers to the “phase fringe pattern”, that is, an image exhibiting structured lines whose values are ranged in the

interval. This phase pattern is related to the inspected structure and permits us to address many industrial problems in connection with surface roughness, surface shape, surface deformation or surface vibration [ 2 ]. Digital holographic interferometry yields contactless, non-intrusive and full-field measurements.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Network for Speckle De-Noising in Severe Conditions

2022

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Digital holography is well adapted to measure any modifications related to any objects. The method refers to digital holographic interferometry where the phase change between two states of the object is of interest. However, the phase images are corrupted by the speckle decorrelation noise. In this paper, we address the question of de-noising in holographic interferometry when phase data are polluted with speckle noise. We present a new database of phase fringe images for the evaluation of de-noising algorithms in digital holography. In this database, the simulated phase maps present characteristics such as the size of the speckle grains and the noise level of the fringes, which can be controlled by the generation process. Deep neural network architectures are trained with sets of phase maps having differentiated parameters according to the features. The performances of the new models are evaluated with a set of test fringe patterns whose characteristics are representative of severe conditions in terms of input SNR and speckle grain size. For this, four metrics are considered, which are the PSNR, the phase error, the perceived quality index and the peak-to-valley ratio. Results demonstrate that the models trained with phase maps with a diversity of noise characteristics lead to improving their efficiency, their robustness and their generality on phase maps with severe noise.

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Basic Fundamentals of Digital Holography

Cited by 25 publications

References 124 publications

Modelling of the photometric balance for two-wavelength spatially multiplexed digital holography

Modelling of the photometric balance for two-wavelength spatially multiplexed digital holography

Strategies for reducing speckle noise in digital holography

Deep Learning Network for Speckle De-Noising in Severe Conditions

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