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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.
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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 .
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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 .
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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Journal ArticleDOI

Speckle noise reduction in television holography fringes using wavelet thresholding

TL;DR: It is shown that the method achieves the highest speckle reduction whenSpeckle grains have the same average size of the image pixels, which is a very important problem in the quantitative analysis of transient phenomena.
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Digital holography with ultimate sensitivity

TL;DR: In this article, a variant of the heterodyne holography scheme was proposed, which combines the properties of off-axis and phase-shifting holographysy, making it possible to filter off numerically the zero-order image alias and the technical noise of the reference.
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Diagnostic Tools for Lab-on-Chip Applications Based on Coherent Imaging Microscopy

TL;DR: Recent progresses in developing imaging tools based on coherent imaging microscopy that can be very useful when applied into biomicrofluidics are presented.
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Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography

TL;DR: A fast non-Bayesian denoising method is proposed that avoids this trade-off by means of a numerical synthesis of a moving diffuser and shows a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement.
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