Strategies for reducing speckle noise in digital holography
Vittorio Bianco,Pasquale Memmolo,Marco Leo,Silvio Montresor,Cosimo Distante,Melania Paturzo,Pascal Picart,Bahram Javidi,Pietro Ferraro +8 more
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.read more
Citations
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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
Chao Zuo,Jiaming Qian,Shijie Feng,Wei Yin,Yixuan Li,Pengfei Fan,Jing Han,Kemao Qian,Qian Chen +8 more
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
Chao Zuo,Jiaming Qian,Shijie Feng,Wei Yin,Yixuan Li,Pengfei Fan,Jing Han,Kemao Qian,Qian Chen +8 more
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
Xin Yang,Ye Pu,Demetri Psaltis +2 more
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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