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Open AccessJournal ArticleDOI

Strategies for reducing speckle noise in digital holography

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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Book

Digital holography and digital image processing : principles, methods, algorithms

TL;DR: Methods and Algorithms of Digital Filtering of Signal/Image Processing and Computer Generated Holograms.
Journal ArticleDOI

Learned hardware-in-the-loop phase retrieval for holographic near-eye displays

TL;DR: The proposed hardware-in-the-loop approach is robust to spatial, temporal and hardware deviations, and improves the image quality of existing methods qualitatively and quantitatively in SNR and perceptual quality.
Journal ArticleDOI

Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning.

TL;DR: A motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism, providing a more sensitive technique for diagnosing diseases, without the need for chemical labeling.
Journal ArticleDOI

Observing distant objects with a multimode fibre-based holographic endoscope

TL;DR: This work introduces an alternative 'farfield' endoscope, capable of imaging macroscopic objects across a large depth of field, and paves the way towards the exploitation of minimally-invasive holographic micro-endoscopes in clinical and diagnostics applications.
References
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Book

A wavelet tour of signal processing

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI

Scale-space and edge detection using anisotropic diffusion

TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Journal ArticleDOI

De-noising by soft-thresholding

TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
Journal ArticleDOI

Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Proceedings ArticleDOI

A non-local algorithm for image denoising

TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.
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