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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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Journal ArticleDOI

Review of digital holographic microscopy for three-dimensional profiling and tracking

TL;DR: A review of the state-of-the-art of DHM for three-dimensional profiling and tracking with emphasis on DHM techniques, reconstruction criteria, and their applications in various branches of science, including biomedical microscopy, particle imaging velocimetry, micrometrology, and holographic tomography are presented.
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Speckle-noise reduction on kinoform reconstruction using a phase-only spatial light modulator.

TL;DR: The dependence of the speckle contrast on the initial random phase and the influence of the liquid-crystal spatial light modulator's display performance on the image quality are discussed.
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Holographic opto-fluidic microscopy.

TL;DR: This imaging modality utilizes partially coherent in-line holography and pixel super-resolution to create high-resolution amplitude and phase images of the objects flowing within microfluidic channels, which is demonstrated by imaging C. elegans, Giardia lamblia, and Mulberry pollen.
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Quasi noise-free digital holography.

TL;DR: A novel framework is proposed that combines the concepts of encoding multiple uncorrelated digital holograms, block grouping and collaborative filtering to achieve quasi noise-free DH reconstructions and is comparable to the quality achievable with non-coherent techniques and far beyond the current state of art in DH.
Journal ArticleDOI

Reduction of speckle in digital holography by discrete Fourier filtering.

TL;DR: A digital signal processing technique that reduces the speckle content in reconstructed digital holograms based on sequential sampling of the discrete Fourier transform of the reconstructed image field is presented.
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