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
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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.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.
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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
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 .
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Deep learning in optical metrology: a review
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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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Imaging blood cells through scattering biological tissue using speckle scanning microscopy
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References
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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
Michel Gross,Michael Atlan +1 more
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
Francesco Merola,Pasquale Memmolo,Lisa Miccio,Vittorio Bianco,Melania Paturzo,Pietro Ferraro +5 more
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
Vittorio Bianco,Melania Paturzo,Pasquale Memmolo,Andrea Finizio,Pietro Ferraro,Bahram Javidi +5 more
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.