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Strategies for reducing speckle noise in digital holography

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TLDR
A broad discussion about the noise issue in DH is provided, with the aim of covering the best-performing noise reduction approaches that have been proposed so far and quantitative comparisons among these approaches will be presented.
Abstract
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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Three-Dimensional Imaging and ProcessingUsing Computational Holographic Imaging

TL;DR: Digital holography is a technique that permits digital capture of holograms and subsequent processing on a digital computer as mentioned in this paper, and various applications of this technique cover three-dimensional (3-D) imaging as well as several problems.
Journal ArticleDOI

Deep learning in holography and coherent imaging.

TL;DR: In a discussion of the topic, Yair Rivenson, Yichen Wu, and Aydogan Ozcan explain how once “trained” with appropriate datasets, neural networks can learn to reconstruct images with added benefits such as improved phase recovery and extended depth of field as well as enhanced spatial resolution and superior signal-to-noise ratio.
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Deep learning in optical metrology: a review

TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Journal ArticleDOI

Deep learning in optical metrology: a review

TL;DR: Deep learning-enabled optical metrology is a kind of data-driven approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances as discussed by the authors .
Posted Content

Imaging blood cells through scattering biological tissue using speckle scanning microscopy

TL;DR: Clear images of multiple cells were obtained with subcellular resolution and good image fidelity, provided that the object dimension was smaller than the maximum scanning range of the speckle pattern.
References
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BookDOI

Digital Holography and Three-Dimensional Display

TL;DR: Digital Holography: Computer-Generated Holograms for White Light Reconstruction as discussed by the authors, 3D display technologies, 3D Display and Information Processing based on Integral Imaging, Autostereoscopic, Partial Pixel, Spatially Multiplexed, and other 3D displays technologies.
Journal ArticleDOI

Recent advances in holographic 3D particle tracking

TL;DR: A complete review of state-of-the-art holographic 3D particle-tracking methods and their applications in bio-microfluidics is provided.
Journal ArticleDOI

NL-InSAR: Nonlocal Interferogram Estimation

TL;DR: A statistically grounded patch-similarity criterion suitable to SLC images is derived and a weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way.
Journal ArticleDOI

Comparison of Fourier transform, windowed Fourier transform, and wavelet transform methods for phase extraction from a single fringe pattern in fringe projection profilometry

TL;DR: Phase extraction methods from a single fringe pattern using different transform methods are compared using both simulations and experiments to determine the merits and limitations of each.
Journal ArticleDOI

A simple and effective method for filtering speckle-interferometric phase fringe patterns

TL;DR: It is shown that a simple process such as multiple repetition of an anisotropic sine/cosine average filter produces the effect of an excellent automatic adaptive filter for filtering speckle-interferometric phase fringe patterns.
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